Protein thermostability engineering

H. Pezeshgi Modarres ac, M. R. Mofrad ab and A. Sanati-Nezhad *cd
aMolecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, 208A Stanley Hall, Berkeley, CA 94720-1762, USA
bPhysical Biosciences Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
cBioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4
dCenter for BioEngineering Research and Education, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4. E-mail:; Tel: +1 403 220 7708

Received 1st July 2016 , Accepted 7th November 2016

First published on 7th November 2016


The use of enzymes for industrial and biomedical applications is limited to their function at elevated temperatures. The principles of thermostability engineering need to be implemented for proteins with low thermal stability to broaden their applications. Therefore, understanding the thermal stability modulating factors of proteins is necessary for engineering their thermostability. In this review, first different thermostability enhancing strategies in both the sequence and structure levels, discovered by studying the natural proteins adapted to different conditions, are introduced. Next, the progress in the development of various computational methods to engineer thermostability of proteins by learning from nature and introducing several popular tools and algorithms for protein thermostability engineering is highlighted. Further discussion includes the challenges in the field of protein thermostability engineering such as the protein stability–activity trade-off. Finally, how thermostability engineering could be instrumental for the design of protein drugs for biomedical applications is demonstrated.

1. Introduction

Enzymes are the primary catalytic agents conducting chemical reactions within cells. Enzymes' functions have not been limited to cells but are used in a range of applications because of their favorable features such as high specificity and activity. However, natural enzymes cannot usually meet the needs of industrial conditions such as the harsh environment in chemical industries, because they are evolved to function in their native conditions. Protein engineering has revolutionized the application of naturally available enzymes for different applications and led to the development of commercially available enzymes. The aim of protein engineering is to improve different features of functional proteins, such as the stability and activity, to overcome their natural limitations. Stability engineering mainly develops longer stability at harsh conditions such as elevated temperatures, high pH values, and high concentrations of salts. In this review, thermostability engineering will be discussed, which is a special subset of protein engineering, with the focus on the engineering of proteins/enzymes to overcome the natural limitations of their stability against temperature.

Increasing the temperature by only a few degrees higher than the normal functioning temperature of proteins leads to unfolding and structural changes, and impacts their function.1,2 In addition to the structural and functional changes, the hydrophobic amino acids (AAs), normally buried inside the protein structure, are exposed to solvents and hydrophobic AAs from other proteins and form aggregates, leading to irreversible unfolding.3

However, stability of proteins at higher temperatures has always been attractive for different reasons. Heat treatment methods can be used to purify enzymes after their production within mesophilic hosts. In addition, higher thermostability of proteins is associated with their stability to other destabilizing agents such as guanidinium hydrochloride and solvents. Finally, conducting an enzymatic chemical reaction is more favorable at higher temperatures, where a lower viscosity and a higher diffusivity and solubility of the substrate can increase the reaction yield and minimize the risk of contamination by mesophilic species. In addition, understanding thermostabilizing and destabilizing factors is important not only for protein thermostability engineering (PTE) with different applications, but also to figure out the origin of genetic diseases because pathogenic missense mutations result in misfolding and destabilizing effects.4

The thermostability engineering of natural enzymes has been implemented by assessing their stability response to temperature changes. Such assessment for naturally occurring proteins has extended our knowledge about the thermostabilizing factors used by nature over thousands of years to evolve enzymes for adapting the ever-changing environment. There are enzymes in nature, called extro-enzymes, that can function at extreme conditions such as high salt concentrations (halozymes), highly alkaline conditions (alkanozymes), as well as at high temperature and pressure.5,6 Hyper/thermophilic proteins have been isolated from both natural (such as hydrothermal vents) and industrial (such as geothermal power plants) sources.7 It is worth noting that the stable temperature of proteins belonging to hyper and thermophilic organisms is not essentially the same as their optimum growth temperature (OGT). Although most of the enzymes characterized from these organisms possess the optimal activity at temperatures around the OGT of the organism, their cell bound and extracellular proteins such as saccharidases have optimal activity at temperatures higher than the OGT of the host microorganism. For example, the optimum activity of amylopullulanase occurs at 117 °C, whereas the OGT of its hosting microorganism (Thermococcus litoralis) is reported to be as high as 88 °C.8 In addition, to study the behavior of protein at elevated temperatures, we should consider that although the upper limit of temperature for their survival is not definitely known, biomolecules such as metabolites and AAs become unstable at above 110 °C and key stabilizing interactions within protein structures, mainly hydrophobic interactions, are significantly weakened.9 The development of accurate methods to engineer proteins viable at elevated temperatures broadens the application of enzymes considerably for different purposes.

2. Thermostabilization strategies

Different studies have been conducted to characterize the modulating factors of the thermostability of proteins. There are two primary resources of data needed to learn about thermostability: (1) naturally occurring sequences with diverse degrees of thermostability, and (2) the mutagenesis data.

Here, a set of thermostabilization strategies used by nature to enhance the thermostability of proteins is summarized. These strategies have been studied by comparing of proteins with different thermostabilities. Nevertheless, these strategies can be useful as starting points for engineering a target protein sequence to enhance its thermostability.7 It should be noted that the comparative studies cannot provide universal rules of protein thermostability modulation, given the fact that the results of the comparative studies vary among protein families. However, they can be considered as the family specific thermostability governing modes.10,11 The results of the systematic comparison have not been very successful for the thermostability assessment.12 Furthermore, hyper/thermophilic and mesophilic homologs are highly similar because: (1) typically, their sequence similarity is in the range of 40–85% or higher,13 (2) they have both superposable three-dimensional (3D) structures,14 and (3) their catalytic mechanism is the same.15 From now on in this review, the comparative results are summarized to two levels of the sequence and the structure. Our knowledge of these two analysis levels is used to design effective PTE strategies.

2.1. Sequence level comparison

Finding a correlation between the composition of AAs in protein sequences and their thermostability has been one of the primary approaches for engineering the thermal stability of proteins.16 However, the rapid growth in DNA sequencing technologies has clarified that the difference in the composition of AAs in mesophilic and thermophilic sequences is not as significant as initially expected.17 It was concluded that the thermostability modulating factors cannot be expressed only by identifying the differences in the amino acid composition.17 The first research addressing the difference between mesophilic and thermophilic sequences indicated a higher hydrophobicity and residue volume, fewer uncharged polar residues, and higher charged AAs in thermophilic proteins.18 Advances in genome sequencing technologies have been supplying more data at the sequence level for different types of micro-organisms, and have, therefore, provided more statistics for comparison purposes with higher resolutions. The hydrophobic content is higher in thermophiles compared with mesophiles, which in consequence increases both the hydrophobicity and rigidity of the protein.19 In particular, isoleucine (Ile) and valine (Val) show higher frequencies in the thermophilic proteins.19,20 Although some studies indicate that glycine (Gly) has a lower frequency in the thermophilic proteins because there are voids within the protein structure,21 other reports show no difference in Gly presence in mesophilic and thermophilic proteins.22 Proline (Pro) has been demonstrated to have a higher frequency in thermophilic proteins22,23 so that an increase in Pro content has been used as a strategy to enhance protein thermostability.24 On the other hand, methionine (Met) has a lower frequency in thermophilic proteins compared to that in mesophiles.20,25 Several reports indicate that uncharged polar AAs, mainly serine (Ser), glutamine (Gln), cysteine (Cys), and threonine, are less frequent in thermophiles compared to mesophiles.19,20,22 Cys, asparagine, and Gln are known as thermolabile AAs.26 This explains their lower frequency in thermophilic proteins which minimizes the backbone cleavage, deamination, and oxidation at temperatures corresponding to thermophiles.20,25 Therefore, the following replacement preference was reported from mesophile to thermophile: Met → Ala, Cys → Ala, Trp → Tyr, Met → Leu, Cys → Val, and Cys → Ile.27 Generally, a higher frequency of charged AAs is reported in thermophilic proteins.22,28 The most common explanation for such an increase in the charged amino acid content is making the salt bridges stabilizing weak points on the protein structure against high temperatures.29–34 In a combinatorial work, the E + K/Q + H ratio has been reported as an indicator for the thermostability of proteins. The protein is considered as hyper-thermophilic for the ratios above 4.5, mesophilic for the ratios below 2.5, and thermophilic for the ratios between 3.2 and 4.6.35

A relatively higher frequency is reported for aromatic AAs in thermophilic proteins.20,22 However, it is worth noting that the difference in preference of AAs between mesophilic and thermophilic proteins vary from one protein family to other.28,36,37 In another report exploring all possible amino acid combinations, the sum of frequencies of Ile, arginine (Arg), glutamic acid (Glu), Val, tyrosine (Tyr), tryptophan (Trp), and leucine (Leu) (IVYWREL) AAs presented a correlation coefficient with the OGT of the microorganisms as high as 0.93 in 86 proteomes.38

In addition to analysis of the amino acid composition, the coupling patterns of AAs were assessed in terms of thermostability analysis in the sequence level.39 Denoting the [XdZ] as the coupling between AAs X and Z with a distance d, the following amino acid patterns have been suggested to fit mesophilic proteins: [C(−4)L], [C(−3)L], [C(−2)L], [C2L], [C3L], [D(−5)T], [D(−4)T], [E(−8)T], [E(−4)T], [E1Q], [E3T], [E4T], [G(−3)Q], [K(−4)T], [K2T], and [K3T]. Similarly, the amino acid patterns of [C(−2)P], [C1P], [C3C], [C4C], [C6C], [C7C], [K(−7)E], [K(−4)E], [K3E], [K4E], and [H(−4)V] were suggested to fit thermophilic proteins.39

AA physicochemical properties. A systematic analysis of the differences in 48 physicochemical properties of AAs between mesophilic and thermophilic proteins indicated that the shape factor and Gibbs free energy of the hydration for native proteins are the most important properties of AAs for thermostability labeling of proteins.40
Occurrence of AAs on different structural regions. In a report that studied the occurrence of AAs following their solvent accessible surface area (SASA) in thermophilic proteins, a higher relative occurrence was reported for Gln and Ile at the fully exposed state; Arg, Glu, and AAs with lower solvation energy; ion pairs located on the 3/10 helix at the exposed state; flexible AAs at the partially exposed states; alanine (Ala) residues located on the 3/10 helix; Arg and Glu at the buried state; and cation–π interactions at the well-buried states. However, a lower relative occurrence was reported for Ala, Ser, and Val at the exposed state, Ser at the partially exposed state, Met at the buried state, and aspartic acid (Asp) and Gly at the well-buried state.12

2.2. Structure level comparison

Comparison of protein structures provides more useful and practical help because it considers the meaningful pair and group interactions between the different classes of AAs that are physically interpretable.
Disulfide bridges. Disulfide bridges make strong interactions and can stabilize the protein by decreasing the entropy of the unfolding state.41
Hydrophobic interactions. The effect of hydrophobic interactions to the protein stability via escaping from water media and forming and preserving hydrophobic–hydrophobic interactions was first shown by Kauzmann.42 The hydrophobic cores have not only been shown to be essential for the general protein stability43,44 but also as a principal stabilizing strategy for the PTE.42,45,46 The hydrophobicity content has also been proposed to be informative for discriminating between mesophilic and thermophilic proteins.47 As well as comparisons between mesophilic and thermophilic proteins, several mutagenesis studies proved that the protein stability could be altered significantly by mutations in the hydrophobic cores.48 Nevertheless, the mechanism of stabilization/destabilization of such mutations is not well understood.48 Some reports proposed that alteration in the stability was dependent on factors such as changes in the transfer free energy and neighboring residues in the structure.48 In particular, the loss of hydrophobicity and disturbance of the well packed core residue side chains have been suggested to contribute in destabilizing the mutations in the hydrophobic cores.46 Many experimental and theoretical works have also addressed the effect of mutation size from the small to large (and vice versa) substitution.44,49,50 It was indicated that both the large to small (i.e., by introducing cavities)48,51 and small to large (i.e., by introducing unfavorable contacts)48,52 mutations could result in protein instability. Mutations should make specific interactions and optimize a hydrophobic core, thus the “small to large mutations” strategy cannot be used as a universal rule. However, L to I mutation usually do not show alteration in the protein stability.53 This could reveal the importance of side chain hydrophobicity versus packing.53 To gain more insight into the thermodynamics, ΔΔG of the substitution of a hydrophobic core member was correlated with features of hydrophobic cores such as the local packing density,44 structural perturbations in neighboring atoms,54 and the number of neighboring methyl and methylene groups.55 However, whereas this approach is effective for several specific proteins, the generalization of these correlations was shown to be difficult.44 Somewhat simpler correlations between ΔΔG and features of hydrophobic AAs and the consequent cavities and interactions cannot be simply generalized to all other protein families.44 Besides experimental studies, computational techniques have also been utilized to address the effect of hydrophobic core mutations on the protein thermostability.56
Packing. Although better packing has been shown to contribute to the thermostability enhancement,57 the role of packing of the interior and exposed residues is still under debate. Whereas Pack and Yoo58 found no distinct difference between packing of exposed residues in mesophilic and thermophilic proteins, Glyakina et al.59 recently showed that the exposed residues are more closely packed in thermophilic proteins than in mesophilic ones.
Aromatic interactions. The contribution of aromatic interactions in the protein folding and stability is significant and has been discussed for a long time.60,61 Different forms of aromatic interactions have been detected including π–π, cation–π, aryl–sulfur, and carbohydrate–π interactions.62 The cluster formation of two or more aromatic AAs has also been shown to be prevalent with a significant role in protein stability.63,64 For Phe–Phe interactions on locations i and i + 4 of a helix, a free energy of −0.1 to −0.8 kcal mol−1 is reported, which is the greatest among π–π interactions,65,66 and is almost the same magnitude as hydrophobic interactions such as Leu–Tyr67 or Phe–Met.68,69 The stability effect of Phe–Phe interaction also depends on its location on the protein structure, showing more stabilizing impact on the C-terminus compared to the central regions of a helix.66 Aromatic clusters are also required for well folded hairpin structures.70,71 In fact, Phe–Phe interactions are among the most abundant cross-stand pair interactions on β-strands72 and are suggested as key interactions in the formation of amyloids.73 Trp–Trp cross-strand pair interactions on a beta-hairpin such as tryptophan zipper (Trpzip) strongly increase the stability to exhibit a melting point temperature (Tm) of up to 78 °C.74 Aromatic interactions have also been utilized for inducing specificity in folded protein structures, showing features such as a narrow thermal denaturation range.75,76 However, the cation–π-interaction tendency has been reported to have a wide range of −0.8 to −1.2 kcal mol−1 for Trp–His with almost no stabilizing effect on the protein structure for Phe–Lys/Arg interaction.62,77–79
Salt bridges and hydrogen bonds. Stronger networks of salt bridges and hydrogen bonds play a central role in the higher thermostability of hyper/thermophilic proteins compared to mesophilic proteins.80 It has been suggested that the salt bridges are not the driving force of the protein folding and stability because there exists a limited number of these bridges in the protein structure that are not highly conserved.81 However, other researchers have indicated a significant stabilizing role for salt bridges.82 For T4 lysozyme, the stabilizing effect was calculated to be around 3–5 kcal mol−1 for a single salt bridge.83 Although salt bridges have a positive effect on the thermostability they do not have a principal role. Furthermore, a larger number of salt bridges are not able to explain the increased number of the positively charged residues compared to the negatively charged ones.84 It is argued that the number of charged AAs in thermophilic proteins increases in order to make more salt bridges. However, this needs further discussion and more detailed investigation. It is suggested that the surface charged residue interactions with solvent molecules at higher temperatures increase the stability. In addition to the effect of solvent interaction, a “negative design” is suggested to explain why the charged AAs are present more in hyper/thermophilics, whereas they do not necessarily form salt bridges.85 A negative design means that during the evolution, proteins are selected in a way that partially unfolded states are less favorable because of the clustering of residues with identical charges with a consequent repelling effect.85,86
Prolines and decreasing the entropy of unfolding. The reduction in entropy of the protein unfolding is suggested to act as one stabilizing factor.87 Whereas Gly has the highest conformational entropy, Pro has limited conformational states, leading to a lower conformational entropy.88 Therefore, Glu → Pro is expected to enhance the stability of proteins. This technique has been used in other studies.89 The most significant stabilizing effect has been reported for mutations located on turns or the N caps of helices. However, this kind of mutation has less stabilizing effect if it introduced unfavorable interactions or removed favorable interactions such as H-bonds.90
Inter-subunit interactions and oligomerization. Ion pairs and hydrophobic interactions have been shown to enhance the thermostability via inter-subunit interactions.91–93
Helix dipole stabilization. Helix dipoles are shown to be effective on the protein stability.94 Locating the negatively charged residues close to the N-terminal, and the positively charged ones close to the C-terminal of helixes is shown to have a stabilizing effect.95
Docking of the N and C termini, and anchoring of loose ends. Protein N and C terminals and loops show the highest B-factors. Therefore, stabilizing these regions with the rest of the protein via H-bonds and ion pairs can result in thermostabilization of the protein. For loops, shortening the loop length is another strategy for thermostabilization.96
Metal binding. Binding of metal ions is also shown to alter the thermostability of proteins.97
Extrinsic parameters. Although hyper/thermostable proteins are inherently stable at elevated temperatures, some environmental factors such as salts98,99 and substrate100 can increase the thermostability of intracellular enzymes.
Secondary structure propensity. The secondary structure propensity of AAs is also suggested to affect the protein thermostability.101 However, such a relationship is not supported by all the reports.81

2.3. Quantification of protein thermostability

The thermostability of proteins is addressed by measuring the thermodynamic and kinetic stabilities. The thermodynamic stability is usually expressed by the unfolding free energy (ΔG) of proteins and their Tm, whereas the kinetic stability is expressed by the half-life time (t1/2) for enzymes at a specific temperature.7 The unfolding free energy is typically reported to be between 5 and 15 kcal mol−1 for globular mesophilic proteins at 25 °C. There is a small difference between the unfolding free energy of hyper/thermophilic and mesophilic homologs, which is typically between 5 and 20 kcal mol−1. However, mutations in enzymes with change in Gibbs free energy (ΔΔG) of 3 to 6.5 kcal mol−1 may increase the melting temperature up to 12 °C.102 Theoretically, there are three approaches for increasing the thermodynamic stability of proteins by manipulating the ΔGT curve by:103 (1) shifts towards higher ΔG, (2) shifts towards higher T, and (3) flattening.103 Different combinations of these three approaches are used by hyper/thermophilic proteins to achieve a higher thermodynamic stability although the most common approach is the shifting of the curve towards higher ΔG values.103

3. Protein thermostability engineering

Although the reaction rates, in general, double by increasing the reaction temperature by 10 °C (Q10 rule), hyper/thermophilic and mesophilic homologous enzymes usually have the same activity at their corresponding physiological conditions.7 Nevertheless, the observation of lower catalytic efficiency of hyper/thermophilic enzymes, compared to mesophilic, at mesophilic conditions has strongly suggested that there is a trade-off between the activity and thermostability.7 A number of protein engineering studies showed the possibility of increasing the thermostability without compromising the activity.104

Traditional experimental methods particularly the random mutations method have been employed to engineer proteins with low thermostability. Given the improvement in the knowledge about proteins, specifically the difference between categories of thermostability, computational techniques nowadays contribute to improve the thermostability. In this section, some commonplace experimental methods are introduced and because this review is mainly devoted to exploring in silico methods, the computational methods are investigated in depth.

3.1. Experimental methods for protein thermostability enhancement

The stabilization of proteins has usually been conducted via genetic protein engineering (by introducing appropriate mutations) or chemical modifications.105 Site specific mutations are usually used when the structure of the protein is known. However, for directed evolution techniques,106,107 the structure of the target protein is not needed.108,109 In addition, the extension of the protein via N- or C-terminals using a random peptide technique to improve the thermostability of proteins can also be used.110,111

Chemical modification techniques do not introduce any mutation. These techniques have been considered less during the last decade compared to genetic strategies.105 The proteins of interest can be chemically altered by the covalent attachment of proteins to water soluble polymers.112 Alternatively, the surface of the protein can be chemically modified using chemical groups. For example, the side chains of surface lysines were modified using citraconic anhydrides and as a consequence, the lysines with positive charges were replaced with carboxyl groups with negative charges.113 The details of these experimental techniques have been reviewed elsewhere.114,115

3.2. Computational protein thermostability engineering techniques

Because the experimental methods used for studying the protein stability are usually costly and time-consuming, computational techniques are appropriate alternatives to predict the function and activity of proteins.116 The most common computational technique used for the engineering of protein thermostability is called rational engineering. In this method, a hot spot should be first detected in a protein structure, followed by engineering an appropriate mutation to improve the thermostability. Hot spots could be mechanically weak regions on the structure, detected by B-factors or molecular dynamic (MD) simulations, or cavities within the protein structure composed of hydrophobic residues. Stabilizing substitutes can stabilize the hot spots by either making salt bridges or improving the hydrophobic cores. The comparison studies are considered to be the origin of the methods that can improve the stability such as introducing a salt bridge at the appropriate point on the structure. Alternatively, the concept of stability predictors introduces another category of computational methods for thermostability engineering.
Sequence-based engineering. All the analyses presented in previous sections were based on knowledge about the structure of the protein of interest. However, for a lot of (interesting) protein sequences, there is no structure available. However, there are plenty of characterized and annotated protein sequences available in public databases. Therefore, any analytic tool that can provide hints about the enhancement of the thermostability of proteins out of such a big collection of information would be of great interest.117 As an alternative to structure-based engineering, sequence-based engineering, known as data driven engineering, has recently been attracting attention. Specifically, advances in DNA sequencing technologies and an increasing number of characterized protein sequences have made this approach more attractive.118 This approach uses all the available homologous sequences to propose thermostabilizing mutations.118
Consensus concept. To extract thermostabilizing mutations out of homologous sequences, the consensus concept (CC) was introduced and has been used in many studies as the primary sequence-based PTE technique.119–127 This method has been applied successfully for different proteins including phytases,128 repeat proteins,129–133 β-lactamase,134 endoglucanase,123 fibronectin type III (FN3) domains,135 fluorescent proteins,136 penicillin G acylase,137 glucose dehydrogenase,138 and α-amino ester hydrolases.139 The logic behind this method is simple: using a multiple sequence alignment (MSA), non-consensus residues are substituted by consensus ones.118 This method was first introduced by Pantoliano et al.140 and a few years later Steipe provided a statistical explanation by making an analogy to the thermodynamic canonical ensemble.141,142 The number of sequences is not important in this method. A successful PTE has even been reported using four sequences.142,143 Because the functional residues essential for the protein folding and enzyme activity belong to the consensus pool,144 this method does not compromise the stability and catalytic activity.126,144,145 Furthermore, the thermostabilized proteins that are engineered by this approach have shown enhanced stability against water miscible organic solvents and the high concentration of kosmotropic and chaotropic salts.125

However, the CC method does not guarantee that all the proposed individual mutations can increase the thermostability. Reports indicate that the mutations suggested by the CC method are usually composed of stabilizing, neutral, and destabilizing mutations, which eventually counterbalance each other and produce an overall stabilizing effect.121,126,145 Nevertheless, removing the destabilizing mutations has been shown to increase the thermostability.118,121,145 Therefore, it is important to formulate strategies to refine the CC results and more precisely detect stabilizing and destabilizing candidates among the proposed set of mutations. In the next sections, three CC refinement methods that have been used by different groups will be introduced.

Analysis of residues' coupling. An important assumption behind the CC method is that the functions of AAs in all the positions are independent, whereas in reality, this is not true because residues interact to form the optimum structure and work cooperatively to gain the overall required functions.146 Studies on indole-3-glycerol phosphate synthase and anthranilate phosphoribosyltransferase enzymes confirm that positions on the protein that are highly correlated with other sites are important for protein stability and function147 and the mutation of such positions should be omitted from the library. For example, Sullivan et al. found that mutation of the coupled location to other residues were less likely to stabilize the protein in triosephosphate isomerase from Saccharomyces cerevisiae.148,149 They suggested the removal of statistically correlated sites as an effective strategy to eliminate destabilizing mutations from the library.148 However, it is worth noting that the analysis of the AA coupling needs big MSAs and this can limit the application of this method for those protein families with a limited number of sequences.127,148,149
Comparing sequences with higher thermostability. There are a few reports which describe taking advantage of comparing the thermostability of target proteins with their thermophilic homologues.104,150 The primary benefit of this method underlies the fact that the target protein is compared with the known thermally stable sequences, which results in mutations with a higher chance of thermostability enhancement. In addition, the sequences extracted from the thermophilic species are tolerant to a set of harsh conditions, where the high temperature is only part of it. Therefore, the mutations found through this method are expected to improve not only the thermostability but also the resistance to other harsh conditions such as intense pH. Nevertheless, this approach suffers from shortage of the number of species isolated from hyper/thermophilic regions compared to mesophiles.104,150,151
Structure-guided CC. For further refinement of CC results and to increase the chance of selecting thermostabilizing mutations, the rational analysis of the protein structure is one of the most popular approaches,119,120 specifically for a target sequence with a few available homologues or low sequence identities.137–139,152 This method has been applied successfully for enzymes such as penicillin G acylase,137 glucose dehydrogenase,138 α-amino ester hydrolases,139 and pullulanase152 with a higher rate of thermostabilizing mutations detection compared to the conventional CC method. In all these studies, after building a mutation library using the traditional CC method, the function or stability disturbing mutations were eliminated using the following procedure. First of all, to decrease the risk of mutation on the function and activity of the proteins, mutation sites should be far away from the important residues such as those located at the active sites (6–10 Å away). Then, to preserve the secondary structure of the protein, the secondary structure propensity should be taken into account. For example, helix destabilizing substitutions were eliminated from the list of mutations. Finally, mutations that could disturb the existing salt bridges or hydrogen bonds were eliminated from the list. As an example, Polizzi et al.137 decreased the number of mutations from 109 to 21 mutations using this method. Further complementary analysis can be added to this procedure for further refinement of the results using available experimental data such as B-factor analysis, analysis of the water-exposed surface, subunit interactions, and Ramachandran plots.138,139,152
Structure-based engineering. At high temperatures, mechanically weak regions of proteins are the most likely unfolding initiators. Therefore, locating these fragile regions and strengthening them by appropriate mutations has been widely used for the PTE.153–160 In this approach, first, the weak points are found on the protein structure by flexibility/rigidity analysis and then strengthened by genetic modifications such as adding salt bridges,161,162 introducing disulfide bridges,163–165 or incorporating Pro residues.154,166–169
B-factor analysis. From the data available for proteins with known 3D structures, characterized using X-ray crystallography, the relative flexibility of atoms on the protein structure can be studied by looking at their B-factors. The B-factor analysis of crystallographic structures has been used in several thermostability engineering studies.124,154,170–172 The B-values may vary significantly between proteins depending on the structural refinement and the crystal quality. To select the best 3D structure for the protein B-factor analysis, high-resolution structures without any molecules attached to the protein with the minimum number of non-resolved residues should be considered.173 In cases with several available Protein Data Bank (PDB) files for a protein or different subunits of an individual protein, it is possible that different structures present different B-factor scales. Therefore, for making the comparison possible between different PDB files, the B-factors should be taken from the BDB database174 or standardized using algorithms such as B-FITTER.175

The B-factor of the CA atom of a residue is usually considered as the representative of the residue and is used to find the most flexible residues or regions on the protein structure. However, the measured B-factors belong to the crystal state of the proteins and the crystal packing affects the dynamic information extracted from the B-factors. Therefore, the B-factor does not represent the dynamics of protein residues in solution.104 In addition, the crystallographic data are measured at temperatures around 110 K under which the protein may not essentially represent the physiological state.176

Molecular dynamics (MD) simulations. Whereas the experimental investigation is challenging for studying phenomena such as protein folding and unfolding, MD simulations have been very helpful. The protocols and applications of MD simulations are detailed elsewhere.177,178 MD simulations have been used for analysis of the unfolding pathway of proteins at temperatures higher than the working physiological temperature to study the thermostability affecting factors and detecting the weak points.151,154,179,180 The MD method is the most widely used computational technique for thermostability analysis and engineering of a variety of enzymes including lipases,181–183 esterases,184,185 α-amylases,186 amidases,187 xylanases,167,180,188 adenylate kinases,189 adenylosuccinate synthetase,190 carboxylesterases,191,192 xylose isomerase,193 β-fructosidases,194 phytases,195 and ligases.104 MD simulations for the thermostability analysis usually consist of a sequence of steps. First, a 3D structure of a target protein is prepared. The structure is characterized using nuclear magnetic resonance spectroscopy or X-ray crystallography, and if not available, generated using homology modeling. After addition of explicit water molecules and neutralizing ions to the system, the energy minimization is used to remove atomic clashes. Then, the system is equilibrated at room temperature. Finally, the production simulations are run at room temperature or higher temperatures and the trajectory is saved over time for use in the subsequent analyses for example, flexibility analysis. The length of simulations varies in the range of 2–100 ns depending upon the nature of the protein and the computational power.196 The time step is usually set to 2 ps in typical biological systems. However, to prevent the unforeseen protein collapse at higher temperatures it is more effective to set it to 1 fs.104,151 In some studies, simulations are run and analyzed only at room temperature to calculate the B-factor using root mean square fluctuations (RMSF) and to determine the overall flexibility of the protein using the root mean square deviation (RMSD).163,197 However, in the majority of MD simulations for the thermostability analysis, simulations are run at different temperatures ranging from 298 K to 600 K.151,154,179,180 An important advantage of MD simulation is that the flexibility analysis is not only possible for individual residues but also for a sub-domain of the protein. Such sub-domain analysis is not feasible using the B-factor analysis.104,151 For example, using MD simulations, Wang et al.151 detected an susceptible to unfold loop region that was stabilized by mutations, leading to the thermostability improvement. In addition to targeting the hot-spots for the PTE, MD simulations have also been used to understand the thermostabilizing factors and mechanisms. It is implemented by studying the flexibility, secondary structure, hydrogen bonds, salt-bridges, SASA, RMSF, RMSD, metal ion binding sites, and radius of gyration at different temperatures for the thermo-resistant proteins and mutants.155,182,195,196,198–201

Different MD simulation packages such as NAMD,202 GROMACS,203 CHARMM,204 or Amber205 have been used for thermostability analysis and engineering studies. However, thermo-sensitive analysis using MD simulations can only find hot spots, which are usually located on the surface of a protein. They cannot provide useful information about clusters of hydrophobic residues that are usually buried inside the protein structure. These hydrophobic residues are the key for protein folding and preserve the stability.

The thermostability simulations addressed previously have been categorized as atom simulations meaning that all the atoms in the system including the protein and solvent, which usually make up the biggest portion of the system, are included. The incorporation of such a large number of particles in a system causes limitations in the simulation time length (usually less than 100 ns for thermostability simulations) and does not allow phenomena that may happen at longer time scales to be observed. Coarse-grained (CG) force fields can be utilized to tackle this limitation. In CG models, a group of atoms are represented by a single particle and can significantly decrease the number of particles that are subjected to the simulation. CG models lead to longer simulations with more sampling of protein dynamics and the exploration of the thermal unfolding process. Although several studies have used atomic MD simulations, the use of the CG model is rarely reported in the field of thermostability analysis and engineering.206 Kalimeri et al.206 used a CG model called optimized potential for efficient protein structure prediction (OPEP) to study the thermostability of a protein for hundreds of nanoseconds. They showed that the number of sub-states visited by the hyperthermophilic one is larger compared to the mesophilic homolog. They also showed slower dynamics with more resilient behavior against the temperature increment. There is still a lack of evidence about the successful performance and application of CG models for thermostability studies. New CG force fields need to be tested for different proteins.

Constraint network analysis (CNA). Constraint network analysis (CNA) runs rigidity/flexibility analysis, models thermal unfolding, and computes the local and global stability indices. This method uses a simplified representation of protein residue interactions and could be used as an alternative to MD simulations that may take much time and computational resources.207–209 However, as far as is known to the authors, this method was rarely used in thermostability studies.210
Floppy inclusion and rigid substructure topography (FIRST). The FIRST algorithm represents the protein structure as a set of constraints over covalent bonds and angles, hydrogen bonds, and hydrophobic interactions, and further uses the constraint analysis to identify the rigid and flexible regions.211 Although the FIRST algorithm has shown successful results for the flexibility prediction for some proteins,212–214 more protein families should be tested by this technique to assess its performance.173,215

Although algorithms such as FIRST and CNA are much faster than MD simulations for the detection of flexible and rigid regions on the protein structure, they are simplified methods and do not take into account important details such as the effects of explicit solvent molecules and long range electrostatic interactions that are considered in the atomic MD simulations. Such differences can result in differences in the measured flexibilities calculated using simplified methods and MD simulations.216

Disulfide by design. This program uses energy and geometry constraints, extracted from known protein structures containing disulfide bonds, to locate potential sites for Cys substitutions leading to the formation of the disulfide bond.217 This algorithm has been used in several studies not only for the thermostability analysis of thermophilic proteins218 but also for protein engineering to improve the thermostability.219–223
Rosetta design. This program finds the sequences of AAs that can fit into a given protein structure with optimal packing, hydrogen bonding, and hiding of hydrophobic residues. The program uses the Monte Carlo optimization and simulated annealing to search the possible sequence space.224 It also contains a package to estimate changes in the protein stability via single and multiple mutations.225 The Rosetta design has been used to find stabilizing mutations for weak points detected using either MD simulations154 or B-factor analysis.168,169 Korkegian et al.226 showed that this program can be utilized without a weak point detection step for further thermostabilization of the proteins.
Databases. The databases play a crucial role in the design and evaluation of engineering algorithms and tools by providing the needed data.
ProTherm. ProTherm is the most frequently used database for design and assessment of the stability change predictors for proteins. ProTherm contains numerical values for thermodynamic parameters for protein unfolding including changes in Gibbs free energy (ΔΔG), heat capacity (ΔCP), enthalpy (ΔH), and melting temperature (ΔTm) and their changes upon mutations. Based on the latest update, it contains 12[thin space (1/6-em)]561 single, 12[thin space (1/6-em)]561 double, and 1132 multiple mutations extracted from 1902 references for 1040 proteins. ProTherm227 has been used to design several prediction algorithms and tools including FoldX,228 Prethermut,229 PoPMuSiC,230 iPTREE,231 FQ-STAB,232 WET-STAB,233 AUTO-MUTE,234 CUPSAT,235 MUpro,236 and I-Mutant.237 However, for developing stability predictors that are based on machine learning techniques, one should be aware that the majority of available experimental data are biased.238 Whereas there are abundant data for some frequently occurring substitutions (such as X → Ala, where X can be any of the other 19 AAs), the accumulated data is very limited for some substitutions.238 In addition, the majority of the reported mutations in a database such as ProTherm (around 70%)227 are destabilizing which causes another bias, answering the question: “why is the accuracy of prediction of the de-stabilizing mutations higher than that of stabilizing mutations”.4
DSDBASE. DSDBASE together with AnalyCys239 are databases that provide information not only for native disulfides on proteins but also for the residue pairs that can stereochemically form disulfide bridges. This database can be used for PTE by introducing disulfide bridges for the analysis of native disulfide bonds in the database, if available, or by mutating the suggested locations on the target protein structure.240
MODEL. MODEL is another database that provides MD simulation trajectories for different proteins. Currently, it contains simulations for 1595 structures covering around 40% of the PDB protein structures with the probability of observing results by chance (e-value) of less than 10−5.241 One can use this database for flexibility analysis without running expensive MD simulations.

3.3. Stability predictors

The main feature of predictors is that only the information needed about the protein is its sequence or structure. Although there are predictors that are based on the protein sequence, their accuracy is less than the structure-based predictors.242–247 However, even the structure-based predictors still suffer from limited accuracy. This, together with the fact that they do not provide sufficient rationalization for the thermostability analysis (specifically machine learning based predictors), makes them difficult to use for thermostability engineering with high confidence. For an efficient engineering of proteins, predictors are needed that can accurately predict the effect of any mutation on stability of the protein, preferably with a reasonable answer to the question of “why the mutation makes the protein thermo-stable or thermo-unstable”.242–247 Such predictors can also be helpful for better understanding the mechanisms underlying genetic diseases.116,248 Different computational tools have been developed to predict the effect of single or multiple mutations on the protein stability by predicting the difference in the free energy of unfolding or the ΔTm between the wild type and the mutated protein. Such computational methods can be classified into two groups: methods that use energy functions249–258 and methods that utilize the machine learning approach.249,259–262Table 1 presents a list of available predictors. In a systematic evaluation of several stability predictors upon the mutations, Potapov et al.263 compared CC/PBSA, EGAD, FoldX, I-Mutant2.0, Rosetta, Hunter, and combinations of them to predict the effect of 2156 mutations on the stability of proteins. Interestingly, the results showed that even the combination of methods did not significantly enhance the power of predictions. In another study, Khan and Vihinen4 showed that I-Mutant (utilizing protein structure information), D-Mutant, and FoldX gave the most reliable results with almost similar accuracies. However, it is worth noting that even for these three predictors, the accuracy level was moderate (60%), and designing tools with a higher accuracy are still challenging. Both these studies suggest that there is still opportunity for the development of more accurate stability predictors.
Table 1 Protein stability change predictors upon mutationsa
Name Input Mutation Method Website address Ref.
a The second column shows the inputs needed for the prediction. Str. and Seq. represent structure and sequence, respectively. The third column defines the mutations predicted using the predictor. The forth column shows the methods used for the design of the predictor. ML and PE represent machine learning and potential energy function, respectively.
FoldX Str. Single/multiple PE 228
Prethermut Str. Single/multiple ML 229
MLI Str./Seq. Single ML 330
PoPMuSiC-2.0 Str. Single PE 230
iPTREE-STAB Seq. Single ML 231
FQ-STAB Seq. Single Fuzzy query 232
WET-STAB Seq. Double ML 233
AUTO-MUTE Str. Single PE 234
CUPSAT Str. Single PE 235
MUpro Str./Seq. Single ML 236
I-Mutant Seq. Single ML 237
SDM Str. Single PE˜sdm/sdm.php 331
mCSM Str. Single Graphs 332
DUET Str. Single ML 333
iStable Str./Seq. Single ML 334
INPS Seq. Single ML 335
NeEMO Str. Single Networks 336
ENCoM Str. Single Normal mode analysis 337
EASE-MM Seq. Single ML 338
MAESTRO Str. Single/multiple ML 339
STRUM Str. Single PE 340

4. Challenges of thermostability engineering

4.1. Absence of a protein structure

Many of the rational PTE methods need the 3D structure of the target protein. In other words, without knowing the protein structure the existing valuable tools cannot be used for protein engineering purposes. However, for most of the protein sequences available, there is no structure available in the sequence databases. To overcome this problem, two approaches are applicable: using the structure prediction methods264 and using sequences for the prediction of structural features such as flexibility.265

Over the years, many algorithms have been developed for protein structure prediction, and these have been comprehensively reviewed elsewhere.264 Among the protein structure prediction methods, homology modeling is one of the most popular methods used in several thermostability analysis and engineering studies.166,266–272 In homology modeling, a 3D structure is generated for a target sequence using the structure of its homologous sequence(s) and packages such as SWISS-MODEL273 and Modeller.274 The constructed structure can then be used as the starting point for the PTE,166,271,275–278 for the analysis of mutants of the target protein to identify the mechanisms of thermostabilization,104,272,279–281 or for studying the origin of the thermostability for hyper/thermophilic proteins.282–286

In cases with no available structure among homologous sequences, it is possible to predict the contact maps using the sequence alignment of homologs.287–291 The knowledge about the contact maps is very important, specifically for the detection of hydrogen bonds, salt bridges, and hydrophobic cores. In addition to the contact map prediction, other methods and algorithms have been developed to predict the specific structural features of a given sequence. For example, it is possible to determine the flexibility of residues only using the sequence of residues for detecting the potential weak points,265,292–294 predicting the secondary structure, for considering the secondary structure's propensity for thermostability engineering,295–297 and predicting the disulfide forming residues on the protein sequence.298–301

4.2. The stability–activity trade-off and flexibility/rigidity of enzymes

Enzyme function modulating mutations are reported to be destabilizing in general. The effect of mutation on the protein can be dramatic, first for catalytic residues, then for substrate binding residues, and finally for surface residues with a non-adaptive evolutionary change.302 The arrangement of active site residues is naturally unfavourable because they are usually charged or polar when located within hydrophobic clefts,303 and they often have unfavourable backbone angles.304 Therefore, a mutation in catalytic residues often arises with a significant stability increment and at the same time, sacrificing the original function or activity.305–307 Wang et al. and others308–310 have shown that the mutations resulted in new functions are usually destabilizing. Nevertheless, it is necessary to remember that most mutations are destabilizing, whatever the functional/catalytic role of the residues.311,312

The extensive experimental data have suggested that mesophilic enzymes are more flexible than their thermophilic homologues at ambient temperatures. Thus, the direct correlation between the rigidity and thermostability has been the primary working hypothesis so far,313,314 confirmed by the MD and B-factor analysis.315 However, this hypothesis is not supported by some other studies which state that the stability and rigidity are not necessarily correlated.315,316 The enzymes with a higher activity than thermophilic homologues are expected to combine the local flexibility of their active sites (responsible for their higher activity) with a higher overall rigidity (which is the origin of their higher thermostability).317

The thermophilic enzymes have a lower activity around ambient temperature when compared to psychrophilic enzymes.318–320 The higher activity of enzymes has been linked to their higher flexibility of active site residues during their evolution and this may cause unfavorable geometry and interactions. Such flexibility in the active site results in the effective transition state binding and subsequently reduces the activation energy barrier. However, the stability of active site is optimized in thermophilic homologues at the cost of activity.321 Following the transition state theory, there are two possibilities for increasing the activity of an enzyme: decreasing the activation enthalpy change (ΔH) or increasing the activation entropy change (ΔS). Structurally, the decreased flexibility of active sites in thermophilic proteins can be related to the ΔH increment by considering the increased number of thermodynamically favorable interactions such as salt bridges and hydrogen bonds.320–322 On the other hand, the higher flexibility results in a bigger population of conformational states and therefore a higher ΔS.323

It is also possible to improve the thermostability and catalytic efficiency simultaneously278,324,325 or at least keep the catalytic activity close to its original state.226 Such evidence helped to elucidate the next generation of in silico protein engineering tools and algorithms: simultaneous improvement of thermostability and activity. During the last decade, the primary concern of scientists has been minimizing the unfavourable consequences of thermal-stabilizing mutations on enzymatic activity and now it is time to improve both with a minimum number of mutations. Interestingly, some of the available computational techniques that contributed to improving the enzymatic activity are very similar in function to thermostability engineering. For example, Liu et al.326 used statistical coupling analysis to improve the activity of isopentenyl phosphate kinase. However, they did not report the effect of activity improving mutations on the stability of the enzyme.

5. Thermostability of protein drugs

Protein drugs have been very attractive to the pharmaceutical industry. Indeed, compared to small molecule drugs, protein drugs have a higher activity and specificity.327 The number of engineered proteins and peptides used as therapeutics is growing including an antagonistic variant of human growth hormone (hGH); Genentech's Somavert 1 (pegvisomant), Trimeris' Fuzeon 1 (enfuvirtide), an inhibitor of human immunodeficiency virus (HIV) fusion derived from the viral protein gp41; Amgen's Aranesp 1 (darbepoetin alfa), a hyperglycosylated variant of erythropoietin (EPO); a fully human monoclonal anti-TNF-α antibody; and Abbott's Humira 1 (adalimumab).328,329 However, some of the proteins eligible to be considered as therapeutics do not show needed or optimal features essential for a therapeutic protein. Therefore, the improvement of their favourable features by protein engineering will provide a huge opportunity for the development of more efficient protein therapeutics. A variety of strategies including mutations, fusions, and chemical modifications have been developed to engineer different features of therapeutic proteins such as their stability, solubility, binding affinity, and oligomerization.329 Next, mutation-based genetic engineering will be focussed on, with the aim of enhancing the stability of protein therapeutics and to further show how PTE can be helpful in the detection of such mutations. Table 2 shows some of the genetically engineered protein therapeutics.
Table 2 Genetically engineered protein-based drugs available on the market (adapted from 327)
Name Protein Mutation Disease Company
Betaseron® Interferon β (IFN-β) Cys-Ser substitution Multiple sclerosis therapy Bayer
Humalog® Insulin lispro Pro28lysine (Lys), Lys29Pro Diabetes Eli Lilly
NovoLog® Insulin aspart Pro28Asp Diabetes Novo Nordisk
Proleukin® (aldesleukin) Interleukin 2 (IL-2) Cys-Ser substitution Metastatic renal cell carcinoma, metastatic melanoma Prometheus

The stability of protein drugs is not only important for their shelf life and efficiency during the treatment but also for the production process.327 A drug–protein with a higher stability lasts longer, therefore a less frequent usage with a lower dosage is needed.341,342 In addition, the expression level of stable recombinant proteins is often higher, and its function during the manufacturing process is retained, leading to lower costs.341 Whereas small molecules can be taken orally, lack of stability does not allow them to be taken orally, thereby they are traditionally used by injection.343–345 Instability of proteins against different conditions makes their application as therapeutic agents challenging. Although thermostability, as discussed in this review, plays a crucial role in the global stability of drug proteins, it is not the only factor.342 Other factors impact on the protein stability, e.g., physical and chemical factors such as pH, ionic strength, shearing, shaking, solvents, additives, pressure, protein concentration, oxidation, deamidation, hydrolysis, isomerization, succinimidation, non-disulfide crosslinking, and deglycosylation.327,346 In addition, protein pharmaceuticals should be stored at low temperatures or be freeze dried to preserve them for an acceptable lifetime.347 The weak thermostability of protein drugs is one of the main reasons which necessitates maintaining these drugs at low temperatures during the storage and transportation.346,348 Furthermore, aggregate formation can be considered as a common consequence of proteins' instability which may be caused by a change in temperature. The increase in the stability of the protein drugs can minimize the risk of aggregation.347 Therefore, exploring the stabilizing factors to improve their desired features is a major step in the development of protein drugs.327 Among the different strategies, genetic engineering is focussed on for the thermal stabilization of protein drugs, given that their sensitivity to temperature is the most critical origin of instability.347

The genetic modifications that improve the conformational thermostability of proteins can increase the global stability of protein drugs and even improve other favorable features.349 For example, the pharmaceutical application of wild-type hGH is limited because of its low stability as a solution.350 The thermal stability of this protein was increased by 16 °C using six to ten mutations, selected using computational techniques, which could modify core interactions. As another example, the mutant granulocyte colony stimulating factor (G-CSF), with 10 to 14 substitutions, detected by computational techniques, showed not only an increase up to 13 °C in thermal stability but also preserved its biological activity with a prolonged shelf life.351 Human fibroblast growth factor 1 (FGF1) is another successful example of therapeutic protein engineering. FGF1 is a promising therapeutic candidate because of its osteogenic, angiogenic, and wound healing properties. Using the semi-rational approach, FGF1's mutants showed an increase up to 27 °C in thermostability with a prolonged in vivo half-life and an enhanced protease resistance.327,352 Finally, mutations in the G-CSF not only increased the thermostability but also improved the shelf life by 5–10 fold, thus preserving its bioactivity.351

Genetic engineering methods need special care to avoid unfavorable side effects for practical aspects of protein therapeutics in vivo. As a general consideration, mutations increasing the stability of proteins may also affect their activity drastically in a negative way as discussed in the section on protein stability function trade-off. The stability and activity may decrease simultaneously, for example for D58A mutation in ribonuclease T1.353 However, it is still not feasible to deal with this challenge to increase and preserve the original activity.104,312 In other words, achieving such mutations through the process of random mutation is very rare, whereas rational engineering approaches can aid significantly in the selection of the mutations with the lowest negative effects on the activity.104 For proteins with a potential application as drugs, in particular, the effect of the mutation on the activity is not the only issue that should be taken into account during genetic engineering. Another important subject is the effect of mutations on immunogenicity. A higher similarity of the engineered proteins to the endogenous human proteins has been reported by avoiding T- and B-lymphocyte reactivity as a strategy to overcome the immunogenicity problem.343 To deal with this problem, a method called humanization was successfully utilized.354–358 Using the CC method that has been shown to be useful to improve the thermostability and other favorable features of the protein, one can build up a mutation library and add constraints that increase the probability of similarity to the human sequence from the selected mutations. This is recognized as a practical strategy for engineering the proteins towards a higher stability with a minimal risk of the immunogenicity. Finally, the solubility is another important feature of drug proteins that may be influenced by mutations, specifically for hydrophobic/hydrophilic exchange mutations.347 Because the improvement of hydrophilic interactions on the surface and optimization of hydrophobic interactions within the protein core are among the main stability engineering strategies, it is important to make absolutely sure that the thermostability enhancing mutations do not inversely affect the solubility of the target proteins.

The replacement of the free Cys residues, CC process, and introducing the disulfides are reported as three successful genetic engineering approaches to increase the stability of protein drugs.327,329,341,342,352,354,359–370 Among them, the replacement of free Cys is most likely to be the most popular approach.327,329,366–370 Such mutation prevents the aggregation and instability of proteins caused by the intermolecular disulfide bonds. This genetic modification has been successfully applied for commercially available IL-2 and (IFN-β) (Table 2).369 It is worth noting that although Cys replacements can increase the half-life of the protein, it may inversely decrease the thermostability.329,366 Another commonly used method is the CC process.341,342,352,354,359–363 This method was used in human fibroblast growth hormone-1 and resulted in a 27 °C increase in its thermostability.352 It also had a longer half-life with a stronger mitogenic activity.352 Introducing disulfide bridges354,364,365 and optimization of the interaction of AAs located on the protein core350 have also been reported to enhance the stability of the protein drugs.

Correlating the mutation-based modifications with the alterations induced to the different features of a protein is usually difficult. Approaches such as the CC not only improve the thermostability but also benefit other therapeutic functions of the proteins such as the solubility and the expression level.342,359,360 If the sequence set of proteins, used to form the consensus sequence, is selected carefully from the human homologs, or if special care is taken to the preserve the residues conserved within the human homologs, it can overcome the immunogenicity issue by converging it with the humanization method.354–358 In addition, methods that use comparison with more stable homologous sequences (such as comparing them with hyper/thermophilic sequences) are in fact comparing the sequence with sequences that are not only working at high temperatures but also under other harsh conditions, such as intense pH, high salt concentration, and in the presence of organic solvents. Therefore, mutations selected by comparing the target sequence with homologs with a higher thermostability can also increase the stability of the protein against other harsh conditions. Finally, the thermostability enhancing strategies that stabilize flexible regions of the protein structure can also improve other favorable features of the protein drugs such as reducing the proteolytic susceptibility.371 As the protease cleavage sites are usually placed on the flexible regions of the protein, the flexibility decreasing mutations may make the proteolysis prone site to be no longer a match for the putative protease.371 This concept is in complete synergy with the thermostabilizing strategies such as the thermo-sensitive region enhancement and in a good example shows how the thermostabilization by structural modifications can lead to the enhancement of other stability factors.

6. Conclusions

Given the progress in thermostability engineering of proteins, there are still significant challenges in the areas of activity trade-off, rigidity, and drug properties. There are pressing needs to develop complex models that can cover different desired features, satisfy them by a reasonable trade off, and enhance all the favorable properties of proteins at the same time. It is also necessary to develop new sophisticated experimental evidence to find correlations between different properties. So far, only single parameter evaluations such as the correlation between various structure/sequence properties and the individual biochemical features such as the thermostability or activity have been investigated. Finding the relationship between the structure/sequence properties and multiple biochemical features, such as the thermostability and activity together needs numerous experimental data. High-throughput techniques using robotic systems or microfluidics are expected to be helpful in this context.

Most individual prediction/engineering methods used for thermostability analysis suffer from some limitations. More comprehensive and global models should be developed to minimize the risks by mixing different models to cover the weaknesses of each other but meanwhile taking advantage of them. Rational engineering methods are mostly based on the structures although there are fewer structures available compared to the sequences. Incorporation of accurate 3D structure predictors can partially solve this challenge.

All the discussions about protein drugs together indicate that the suggested mutations developed using thermostability engineering methods could be a good starting point for designing protein drug engineering libraries. To protect or improve the function, activity, immunogenicity, and other important factors of the protein drugs, more constraints should be applied to the initially designed mutation library. This leads to the importance of a new generation of computational algorithms that can simultaneously take into account all of these issues.


The authors acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Alberta Innovates Bio Solutions for their support to this research.


  1. K. Ghosh and K. Dill, Biophys. J., 2010, 99, 3996–4002 CrossRef CAS PubMed.
  2. Y.-G. Park, M.-C. Jung, H. Song, K.-W. Jeong, E. Bang, G.-S. Hwang and Y. Kim, J. Biol. Chem., 2016, 291, 1692–1702 CrossRef CAS PubMed.
  3. D. Volkin and C. Middaugh, Stability of protein pharmaceuticals, part A. Chemical and physical pathways of protein degradation, Plenum Press, New York, NY, 1992, pp. 215–247 Search PubMed.
  4. S. Khan and M. Vihinen, Hum. Mutat., 2010, 31, 675–684 CrossRef CAS PubMed.
  5. A. Lewin, A. Wentzel and S. Valla, Curr. Opin. Biotechnol., 2013, 24, 516–525 CrossRef CAS PubMed.
  6. L. Tazi, D. P. Breakwell, A. R. Harker and K. A. Crandall, Extremophiles, 2014, 18, 525–535 CrossRef PubMed.
  7. C. Vieille and G. J. Zeikus, Microbiol. Mol. Biol. Rev., 2001, 65, 1–43 CrossRef CAS PubMed.
  8. S. H. Brown and R. M. Kelly, Appl. Environ. Microbiol., 1993, 59, 2614–2621 CAS.
  9. R. Jaenicke, Biochemistry, 1998, 63, 312–321 CAS.
  10. G. Vogt, S. Woell and P. Argos, J. Mol. Biol., 1997, 269, 631–643 CrossRef CAS PubMed.
  11. S. Chakravarty and R. Varadarajan, FEBS Lett., 2000, 470, 65–69 CrossRef CAS PubMed.
  12. S. P. Pack, T. J. Kang and Y. J. Yoo, Appl. Biochem. Biotechnol., 2013, 171, 1212–1226 CrossRef CAS PubMed.
  13. C. Vieille, J. M. Hess, R. M. Kelly and J. G. Zeikus, Appl. Environ. Microbiol., 1995, 61, 1867–1875 CAS.
  14. D. Maes, J. P. Zeelen, N. Thanki, N. Beaucamp, M. Alvarez, M. H. D. Thi, J. Backmann, J. A. Martial, L. Wyns and R. Jaenicke, Proteins: Struct., Funct., Bioinf., 1999, 37, 441–453 CrossRef CAS.
  15. M. W. Bauer and R. M. Kelly, Biochemistry, 1998, 37, 17170–17178 CrossRef CAS PubMed.
  16. P. Argos, M. G. Rossmann, U. M. Grau, H. Zuber, G. Frank and J. D. Tratschin, Biochemistry, 1979, 18, 5698–5703 CrossRef CAS PubMed.
  17. B. Gerald, Int. J. Pept. Protein Res., 1994, 43, 97–106 Search PubMed.
  18. X.-X. Zhou, Y.-B. Wang, Y.-J. Pan and W.-F. Li, Amino Acids, 2008, 34, 25–33 CrossRef CAS PubMed.
  19. S. Chakravarty and R. Varadarajan, FEBS Lett., 2000, 470, 65–69 CrossRef CAS PubMed.
  20. S. Kumar, C.-J. Tsai and R. Nussinov, Protein Eng., 2000, 13, 179–191 CrossRef CAS PubMed.
  21. N. Panasik, J. E. Brenchley and G. K. Farber, Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol., 2000, 1543, 189–201 CrossRef CAS.
  22. S. P. Pack and Y. J. Yoo, J. Biotechnol., 2004, 111, 269–277 CrossRef CAS PubMed.
  23. M. Sadeghi, H. Naderi-Manesh, M. Zarrabi and B. Ranjbar, Biophys. Chem., 2006, 119, 256–270 CrossRef CAS PubMed.
  24. B. Van den Burg, G. Vriend, O. R. Veltman, G. Venema and V. G. Eijsink, Proc. Natl. Acad. Sci. U. S. A., 1998, 95, 2056–2060 CrossRef CAS.
  25. Z. Xu, Y. Liu, Y. Yang, W. Jiang, E. Arnold and J. Ding, J. Bacteriol., 2003, 185, 4038–4049 CrossRef CAS PubMed.
  26. F. Catanzano, G. Barone, G. Graziano and S. Capasso, Protein Sci., 1997, 6, 1682–1693 CrossRef CAS PubMed.
  27. M. M. Gromiha, M. Oobatake and A. Sarai, Biophys. Chem., 1999, 82, 51–67 CrossRef CAS PubMed.
  28. S. Trivedi, H. Gehlot and S. Rao, Genet. Mol. Res., 2006, 5, 816–827 CAS.
  29. A. Szilágyi and P. Závodszky, Structure, 2000, 8, 493–504 CrossRef.
  30. S. Fukuchi, K. Yoshimune, M. Wakayama, M. Moriguchi and K. Nishikawa, J. Mol. Biol., 2003, 327, 347–357 CrossRef CAS PubMed.
  31. H. Nakashima, S. Fukuchi and K. Nishikawa, J. Biochem., 2003, 133, 507–513 CrossRef CAS PubMed.
  32. N. F. Saunders, T. Thomas, P. M. Curmi, J. S. Mattick, E. Kuczek, R. Slade, J. Davis, P. D. Franzmann, D. Boone and K. Rusterholtz, Genome Res., 2003, 13, 1580–1588 CrossRef CAS PubMed.
  33. K. Suhre and J.-M. Claverie, J. Biol. Chem., 2003, 278, 17198–17202 CrossRef CAS PubMed.
  34. Y. Tanaka, K. Tsumoto, Y. Yasutake, M. Umetsu, M. Yao, H. Fukada, I. Tanaka and I. Kumagai, J. Biol. Chem., 2004, 279, 32957–32967 CrossRef CAS PubMed.
  35. S. T. Farias and M. Bonato, Genet. Mol. Res., 2003, 2, 383–393 CAS.
  36. T. Kawashima, N. Amano, H. Koike, S.-i. Makino, S. Higuchi, Y. Kawashima-Ohya, K. Watanabe, M. Yamazaki, K. Kanehori and T. Kawamoto, Proc. Natl. Acad. Sci. U. S. A., 2000, 97, 14257–14262 CrossRef CAS PubMed.
  37. O. Fütterer, A. Angelov, H. Liesegang, G. Gottschalk, C. Schleper, B. Schepers, C. Dock, G. Antranikian and W. Liebl, Proc. Natl. Acad. Sci. U. S. A., 2004, 101, 9091–9096 CrossRef PubMed.
  38. K. B. Zeldovich, I. N. Berezovsky and E. I. Shakhnovich, PLoS Comput. Biol., 2007, 3, 62–72 CrossRef CAS PubMed.
  39. H. K. Liang, C. M. Huang, M. T. Ko and J. K. Hwang, Proteins: Struct., Funct., Bioinf., 2005, 59, 58–63 CrossRef CAS PubMed.
  40. M. M. Gromiha, M. Oobatake and A. Sarai, Biophys. Chem., 1999, 82, 51–67 CrossRef CAS PubMed.
  41. A. A. Dombkowski, K. Z. Sultana and D. B. Craig, FEBS Lett., 2014, 588, 206–212 CrossRef CAS PubMed.
  42. H. Chirakkal, G. C. Ford and A. Moir, Protein Eng., 2001, 14, 161–166 CrossRef CAS PubMed.
  43. J. L. Gilmore, X. Yi, L. Quan and A. V. Kabanov, Journal of Neuroimmune Pharmacology, 2008, 3, 83–94 CrossRef PubMed.
  44. M. Bueno, L. A. Campos, J. Estrada and J. Sancho, Protein Sci., 2006, 15, 1858–1872 CrossRef CAS PubMed.
  45. M. M. Gromiha, M. C. Pathak, K. Saraboji, E. A. Ortlund and E. A. Gaucher, Proteins, 2013, 81, 715–721 CrossRef CAS PubMed.
  46. J. G. B. Northey, A. A. Di Nardo and A. R. Davidson, Nat. Struct. Biol., 2002, 9, 126–130 CrossRef CAS PubMed.
  47. A. Banerji and I. Ghosh, Eur. Biophys. J. Biophys. Lett., 2009, 38, 577–587 CrossRef CAS PubMed.
  48. H. Kono, M. Nishiyama, M. Tanokura and J. Doi, Protein Eng., 1998, 11, 47–52 CrossRef CAS PubMed.
  49. J. M. Chen and W. E. Stites, Biochemistry, 2001, 40, 14004–14011 CrossRef CAS PubMed.
  50. F. Pucci, R. Bourgeas and M. Rooman, Sci. Rep., 2016, 6, 23257 CrossRef CAS PubMed.
  51. J. T. Kellis, K. Nyberg and A. R. Fersht, Biochemistry, 1989, 28, 4914–4922 CrossRef CAS PubMed.
  52. E. Baldwin, J. Xu, O. Hajiseyedjavadi, W. A. Baase and B. W. Matthews, J. Mol. Biol., 1996, 259, 542–559 CrossRef CAS PubMed.
  53. B. Anil, S. Sato, J. H. Cho and D. P. Raleigh, J. Mol. Biol., 2005, 354, 693–705 CrossRef CAS PubMed.
  54. A. M. Buckle, P. Cramer and A. R. Fersht, Biochemistry, 1996, 35, 4298–4305 CrossRef CAS PubMed.
  55. D. E. Otzen, M. Rheinnecker and A. R. Fersht, Biochemistry, 1995, 34, 13051–13058 CrossRef CAS PubMed.
  56. U. D. Priyakumar, J. Biomol. Struct. Dyn., 2012, 29, 961–971 CAS.
  57. W.-t. Li, R. A. Grayling, K. Sandman, S. Edmondson, J. W. Shriver and J. N. Reeve, Biochemistry, 1998, 37, 10563–10572 CrossRef CAS PubMed.
  58. S. P. Pack and Y. J. Yoo, Int. J. Biol. Macromol., 2005, 35, 169–174 CrossRef CAS PubMed.
  59. A. V. Glyakina, S. O. Garbuzynskiy, M. Y. Lobanov and O. V. Galzitskaya, Bioinformatics, 2007, 23, 2231–2238 CrossRef CAS PubMed.
  60. G. Dong, C. Vieille, A. Savchenko and J. G. Zeikus, Appl. Environ. Microbiol., 1997, 63, 3569–3576 CAS.
  61. K. Ishikawa, M. Okumura, K. Katayanagi, S. Kimura, S. Kanaya, H. Nakamura and K. Morikawa, J. Mol. Biol., 1993, 230, 529–542 CrossRef CAS PubMed.
  62. M. L. Waters, Pept. Sci., 2004, 76, 435–445 CrossRef CAS PubMed.
  63. R. Bhattacharyya, U. Samanta and P. Chakrabarti, Protein Eng., 2002, 15, 91–100 CrossRef CAS PubMed.
  64. A. Thomas, R. Meurisse and R. Brasseur, Proteins: Struct., Funct., Bioinf., 2002, 48, 635–644 CrossRef CAS PubMed.
  65. M. L. Waters, Curr. Opin. Chem. Biol., 2002, 6, 736–741 CrossRef CAS PubMed.
  66. S. M. Butterfield, P. R. Patel and M. L. Waters, J. Am. Chem. Soc., 2002, 124, 9751–9755 CrossRef CAS PubMed.
  67. S. Padmanabhan, M. Jimenez, D. Laurents and M. Rico, Biochemistry, 1998, 37, 17318–17330 CrossRef CAS PubMed.
  68. A. R. Viguera and L. Serrano, Biochemistry, 1995, 34, 8771–8779 CrossRef CAS PubMed.
  69. B. J. Stapley, C. A. Rohl and A. J. Doig, Protein Sci., 1995, 4, 2383–2391 CrossRef CAS PubMed.
  70. J. F. Espinosa and S. H. Gellman, Angew. Chem., 2000, 112, 2420–2423 CrossRef.
  71. S. Honda, N. Kobayashi and E. Munekata, J. Mol. Biol., 2000, 295, 269–278 CrossRef CAS PubMed.
  72. C. K. Smith and L. Regan, Acc. Chem. Res., 1997, 30, 153–161 CrossRef CAS.
  73. E. Gazit, FASEB J., 2002, 16, 77–83 CrossRef CAS PubMed.
  74. A. G. Cochran, N. J. Skelton and M. A. Starovasnik, Proc. Natl. Acad. Sci. U. S. A., 2001, 98, 5578–5583 CrossRef CAS PubMed.
  75. J. J. Skalicky, B. R. Gibney, F. Rabanal, R. J. Bieber Urbauer, P. L. Dutton and A. J. Wand, J. Am. Chem. Soc., 1999, 121, 4941–4951 CrossRef CAS.
  76. V. Souza, C. Ikegami, G. Arantes and S. Marana, FEBS J., 2016, 238, 1124–1138 CrossRef PubMed.
  77. C. D. Tatko and M. L. Waters, Protein Sci., 2003, 12, 2443–2452 CrossRef CAS PubMed.
  78. C. D. Andrew, S. Bhattacharjee, N. Kokkoni, J. D. Hirst, G. R. Jones and A. J. Doig, J. Am. Chem. Soc., 2002, 124, 12706–12714 CrossRef CAS PubMed.
  79. Z. Shi, C. A. Olson and N. R. Kallenbach, J. Am. Chem. Soc., 2002, 124, 3284–3291 CrossRef CAS PubMed.
  80. P. I. de Bakker, P. H. Hunenberger and J. A. McCammon, J. Mol. Biol., 1999, 285, 1811–1830 CrossRef CAS PubMed.
  81. K. A. Dill, Biochemistry, 1990, 29, 7133–7155 CrossRef CAS PubMed.
  82. A. S. Thomas and A. H. Elcock, J. Am. Chem. Soc., 2004, 126, 2208–2214 CrossRef CAS PubMed.
  83. D. E. Anderson, W. J. Becktel and F. W. Dahlquist, Biochemistry, 1990, 29, 2403–2408 CrossRef CAS PubMed.
  84. G. Saelensminde, O. Halskau Jr and I. Jonassen, Extremophiles, 2009, 13, 11–20 CrossRef CAS PubMed.
  85. I. N. Berezovsky, K. B. Zeldovich and E. I. Shakhnovich, PLoS Comput. Biol., 2007, 3, e52 Search PubMed.
  86. O. Halskau Jr, R. Perez-Jimenez, B. Ibarra-Molero, J. Underhaug, V. Munoz, A. Martinez and J. M. Sanchez-Ruiz, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 8625–8630 CrossRef PubMed.
  87. B. Matthews, H. Nicholson and W. Becktel, Proc. Natl. Acad. Sci. U. S. A., 1987, 84, 6663–6667 CrossRef CAS.
  88. D. Sriprapundh, C. Vieille and J. G. Zeikus, Protein Eng., 2000, 13, 259–265 CrossRef CAS PubMed.
  89. T. Nakai, K. Okada, S. Akutsu, I. Miyahara, S.-i. Kawaguchi, R. Kato, S. Kuramitsu and K. Hirotsu, Biochemistry, 1999, 38, 2413–2424 CrossRef CAS PubMed.
  90. K. Watanabe, T. Masuda, H. Ohashi, H. Mihara and Y. Suzuki, Eur. J. Biochem., 1994, 226, 277–283 CrossRef CAS PubMed.
  91. R. N. Z. A. Rahman, S. Fujiwara, H. Nakamura, M. Takagi and T. Imanaka, Biochem. Biophys. Res. Commun., 1998, 248, 920–926 CrossRef CAS PubMed.
  92. C. Vetriani, D. L. Maeder, N. Tolliday, K. S.-P. Yip, T. J. Stillman, K. L. Britton, D. W. Rice, H. H. Klump and F. T. Robb, Proc. Natl. Acad. Sci. U. S. A., 1998, 95, 12300–12305 CrossRef CAS.
  93. K. E. Neet and D. E. Timm, Protein Sci., 1994, 3, 2167–2174 CrossRef CAS PubMed.
  94. H. Nicholson, W. Becktel and B. Matthews, Nature, 1988, 336, 651–656 CrossRef CAS PubMed.
  95. A. Harada, H. Yagi, A. Saito, H. Azakami and A. Kato, Biosci., Biotechnol., Biochem., 2007, 71, 2952–2961 CrossRef CAS PubMed.
  96. W. Grabarse, M. Vaupel, J. A. Vorholt, S. Shima, R. K. Thauer, A. Wittershagen, G. Bourenkov, H. D. Bartunik and U. Ermler, Structure, 1999, 7, 1257–1268 CrossRef CAS PubMed.
  97. K. Kojoh, H. Matsuzawa and T. Wakagi, Eur. J. Biochem., 1999, 264, 85–91 CrossRef CAS PubMed.
  98. S. Shima, D. A. Hérault, A. Berkessel and R. K. Thauer, Arch. Microbiol., 1998, 170, 469–472 CrossRef CAS PubMed.
  99. S. Shima, C. Tziatzios, D. Schubert, H. Fukada, K. Takahashi, U. Ermler and R. K. Thauer, Eur. J. Biochem., 1998, 258, 85–92 CAS.
  100. V. Wilquet, J. A. Gaspar, M. van de Lande, M. Van de Casteele, C. Legrain, E. M. Meiering and N. Glansdorff, Eur. J. Biochem., 1998, 255, 628–637 CAS.
  101. A. M. Facchiano, G. Colonna and R. Ragone, Protein Eng., 1998, 11, 753–760 CrossRef CAS PubMed.
  102. S. Kawamura, Y. Kakuta, I. Tanaka, K. Hikichi, S. Kuhara, N. Yamasaki and M. Kimura, Biochemistry, 1996, 35, 1195–1200 CrossRef CAS PubMed.
  103. H. Nojima, A. Ikai, T. Oshima and H. Noda, J. Mol. Biol., 1977, 116, 429–442 CrossRef CAS PubMed.
  104. H. Pezeshgi Modarres, B. D. Dorokhov, V. O. Popov, N. V. Ravin, K. G. Skryabin and M. Dal Peraro, Biochemistry, 2015, 3076–3085 CrossRef CAS PubMed.
  105. C. Ó’Fágáin, Enzyme Microb. Technol., 2003, 33, 137–149 CrossRef.
  106. K. Chen and F. H. Arnold, Proc. Natl. Acad. Sci. U. S. A., 1993, 90, 5618–5622 CrossRef CAS.
  107. W. P. Stemmer, Proc. Natl. Acad. Sci. U. S. A., 1994, 91, 10747–10751 CrossRef CAS.
  108. B. Morawski, S. Quan and F. H. Arnold, Biotechnol. Bioeng., 2001, 76, 99–107 CrossRef CAS PubMed.
  109. V. G. Eijsink, S. Gåseidnes, T. V. Borchert and B. van den Burg, Biomol. Eng., 2005, 22, 21–30 CrossRef CAS PubMed.
  110. T. Matsuura, T. Yomo and I. Urabe, Methods Mol. Biol., 2002, 182, 221–230 CAS.
  111. J.-H. Liu, C.-F. Tsai, J.-W. Liu, K.-J. Cheng and C.-L. Cheng, Enzyme Microb. Technol., 2001, 28, 582–589 CrossRef CAS PubMed.
  112. L. Gomez and R. Villalonga, Biotechnol. Lett., 2000, 22, 1191–1195 CrossRef CAS.
  113. K. Khajeh, H. Naderi-Manesh, B. Ranjbar, A. akbar Moosavi-Movahedi and M. Nemat-Gorgani, Enzyme Microb. Technol., 2001, 28, 543–549 CrossRef CAS PubMed.
  114. R. D. Socha and N. Tokuriki, FEBS J., 2013, 280, 5582–5595 CrossRef CAS PubMed.
  115. C. Ó’Fágáin, Protein Chromatography, 2017, 101–129 Search PubMed.
  116. J. Thusberg and M. Vihinen, Hum. Mutat., 2009, 30, 703–714 CrossRef CAS PubMed.
  117. H. J. Wijma, R. J. Floor and D. B. Janssen, Curr. Opin. Struct. Biol., 2013, 23, 588–594 CrossRef CAS PubMed.
  118. J. F. Chaparro-Riggers, K. M. Polizzi and A. S. Bommarius, Biotechnol. J., 2007, 2, 180–191 CrossRef PubMed.
  119. K. M. Polizzi, J. F. Chaparro-Riggers, E. Vazquez-Figueroa and A. S. Bommarius, Biotechnol. J., 2006, 1, 531–536 CrossRef CAS PubMed.
  120. E. Vazquez-Figueroa, J. Chaparro-Riggers and A. S. Bommarius, ChemBioChem, 2007, 8, 2295–2301 CrossRef CAS PubMed.
  121. M. Lehmann, L. Pasamontes, S. F. Lassen and M. Wyss, Biochim. Biophys. Acta, 2000, 1543, 408–415 CrossRef CAS.
  122. M. Lehmann, C. Loch, A. Middendorf, D. Studer, S. F. Lassen, L. Pasamontes, A. P. van Loon and M. Wyss, Protein Eng., 2002, 15, 403–411 CrossRef CAS PubMed.
  123. M. Anbar, O. Gul, R. Lamed, U. O. Sezerman and E. A. Bayer, Appl. Environ. Microbiol., 2012, 78, 3458–3464 CrossRef CAS PubMed.
  124. J. K. Blum, M. D. Ricketts and A. S. Bommarius, J. Biotechnol., 2012, 160, 214–221 CrossRef CAS PubMed.
  125. E. Vazquez-Figueroa, V. Yeh, J. M. Broering, J. F. Chaparro-Riggers and A. S. Bommarius, Protein Eng., Des. Sel., 2008, 21, 673–680 CrossRef CAS PubMed.
  126. M. Lehmann and M. Wyss, Curr. Opin. Biotechnol., 2001, 12, 371–375 CrossRef CAS PubMed.
  127. B. T. Porebski and A. M. Buckle, Protein Eng., Des. Sel., 2016, 1–7 Search PubMed.
  128. M. Lehmann, D. Kostrewa, M. Wyss, R. Brugger, A. D'Arcy, L. Pasamontes and A. P. van Loon, Protein Eng., 2000, 13, 49–57 CrossRef CAS PubMed.
  129. A. Kohl, H. K. Binz, P. Forrer, M. T. Stumpp, A. Plückthun and M. G. Grütter, Proc. Natl. Acad. Sci. U. S. A., 2003, 100, 1700–1705 CrossRef CAS PubMed.
  130. L. K. Mosavi, D. L. Minor and Z.-y. Peng, Proc. Natl. Acad. Sci. U. S. A., 2002, 99, 16029–16034 CrossRef CAS PubMed.
  131. P. Forrer, H. K. Binz, M. T. Stumpp and A. Plückthun, ChemBioChem, 2004, 5, 183–189 CrossRef CAS PubMed.
  132. K. W. Tripp and D. Barrick, Structure, 2003, 11, 486–487 CrossRef CAS PubMed.
  133. T. Kajander, A. L. Cortajarena and L. Regan, Protein Design, 2006, 151–170 CAS.
  134. N. Amin, A. Liu, S. Ramer, W. Aehle, D. Meijer, M. Metin, S. Wong, P. Gualfetti and V. Schellenberger, Protein Eng., Des. Sel., 2004, 17, 787–793 CrossRef CAS PubMed.
  135. S. A. Jacobs, M. D. Diem, J. Luo, A. Teplyakov, G. Obmolova, T. Malia, G. L. Gilliland and K. T. O'Neil, Protein Eng., Des. Sel., 2012, 25, 107–117 CrossRef CAS PubMed.
  136. M. Dai, H. E. Fisher, J. Temirov, C. Kiss, M. E. Phipps, P. Pavlik, J. H. Werner and A. R. Bradbury, Protein Eng., Des. Sel., 2007, 20, 69–79 CrossRef CAS PubMed.
  137. K. M. Polizzi, J. F. Chaparro-Riggers, E. Vazquez-Figueroa and A. S. Bommarius, Biotechnol. J., 2006, 1, 531–536 CrossRef CAS PubMed.
  138. E. Vázquez-Figueroa, J. Chaparro-Riggers and A. S. Bommarius, ChemBioChem, 2007, 8, 2295–2301 CrossRef PubMed.
  139. J. K. Blum, M. D. Ricketts and A. S. Bommarius, J. Biotechnol., 2012, 160, 214–221 CrossRef CAS PubMed.
  140. M. W. Pantoliano, M. Whitlow, J. F. Wood, S. W. Dodd, K. D. Hardman, M. L. Rollence and P. N. Bryan, Biochemistry, 1989, 28, 7205–7213 CrossRef CAS PubMed.
  141. B. Steipe, B. Schiller, A. Pluckthun and S. Steinbacher, J. Mol. Biol., 1994, 240, 188–192 CrossRef CAS PubMed.
  142. B. Steipe, Protein Eng., 2004, 388, 176–186 CAS.
  143. M. W. Pantoliano, M. Whitlow, J. F. Wood, S. W. Dodd, K. D. Hardman, M. L. Rollence and P. N. Bryan, Biochemistry, 1989, 28, 7205–7213 CrossRef CAS PubMed.
  144. M. Lehmann, L. Pasamontes, S. F. Lassen and M. Wyss, Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol., 2000, 1543, 408–415 CrossRef CAS.
  145. M. Lehmann, C. Loch, A. Middendorf, D. Studer, S. F. Lassen, L. Pasamontes, A. P. G. M. van Loon and M. Wyss, Protein Eng., 2002, 15, 403–411 CrossRef CAS PubMed.
  146. V. Durani and T. J. Magliery, Methods Enzymol., 2013, 523, 237–256 CAS.
  147. S. Dietrich, N. Borst, S. Schlee, D. Schneider, J.-O. Janda, R. Sterner and R. Merkl, Biochemistry, 2012, 51, 5633–5641 CrossRef CAS PubMed.
  148. B. J. Sullivan, T. Nguyen, V. Durani, D. Mathur, S. Rojas, M. Thomas, T. Syu and T. J. Magliery, J. Mol. Biol., 2012, 420, 384–399 CrossRef CAS PubMed.
  149. B. J. Sullivan, V. Durani and T. J. Magliery, J. Mol. Biol., 2011, 413, 195–208 CrossRef CAS PubMed.
  150. Z. H. Xiao, H. Bergeron, S. Grosse, M. Beauchemin, M. L. Garron, D. Shaya, T. Sulea, M. Cygler and P. C. K. Lau, Appl. Environ. Microbiol., 2008, 74, 1183–1189 CrossRef CAS PubMed.
  151. K. Wang, H. Luo, J. Tian, O. Turunen, H. Huang, P. Shi, H. Hua, C. Wang, S. Wang and B. Yao, Appl. Environ. Microbiol., 2014, 80, 2158–2165 CrossRef PubMed.
  152. S.-f. Li, J.-y. Xu, Y.-j. Bao, H.-c. Zheng and H. Song, J. Biotechnol., 2015, 210, 8–14 CrossRef PubMed.
  153. X. Huang, D. Gao and C. G. Zhan, Org. Biomol. Chem., 2011, 9, 4138–4143 CAS.
  154. J. C. Joo, S. P. Pack, Y. H. Kim and Y. J. Yoo, J. Biotechnol., 2011, 151, 56–65 CrossRef CAS PubMed.
  155. Y. T. Meharenna and T. L. Poulos, Biochemistry, 2010, 49, 6680–6686 CrossRef CAS PubMed.
  156. S. Kundu and D. Roy, J. Mol. Graphics Modell., 2010, 28, 820–827 CrossRef CAS PubMed.
  157. S. Kundu and D. Roy, J. Mol. Graphics Modell., 2009, 27, 871–880 CrossRef CAS PubMed.
  158. M. B. A. Rahman, R. A. Karjiban, A. B. Salleh, D. Jacobs, M. Basri, A. L. T. Chor, H. A. Wahab and R. N. Z. R. A. Rahman, Protein Pept. Lett., 2009, 16, 1360–1370 CrossRef CAS PubMed.
  159. V. Spiwok, P. Lipovova, T. Skalova, J. Duskova, J. Dohnalek, J. Hasek, N. J. Russell and B. Kralova, J. Mol. Model., 2007, 13, 485–497 CrossRef CAS PubMed.
  160. M. Purmonen, J. Valjakka, K. Takkinen, T. Laitinen and J. Rouvinen, Protein Eng., Des. Sel., 2007, 20, 551–559 CrossRef CAS PubMed.
  161. J. Chen, H. Yu, C. Liu, J. Liu and Z. Shen, J. Biotechnol., 2013, 164, 354–362 CrossRef PubMed.
  162. B. Fei, H. Xu, Y. Cao, S. Ma, H. Guo, T. Song, D. Qiao and Y. Cao, J. Ind. Microbiol. Biotechnol., 2013, 40, 457–464 CrossRef CAS PubMed.
  163. M. G. Pikkemaat, A. B. Linssen, H. J. Berendsen and D. B. Janssen, Protein Eng., 2002, 15, 185–192 CrossRef CAS PubMed.
  164. S. Badieyan, D. R. Bevan and C. Zhang, Biotechnol. Bioeng., 2012, 109, 31–44 CrossRef CAS PubMed.
  165. Q. A. T. Le, J. C. Joo, Y. J. Yoo and Y. H. Kim, Biotechnol. Bioeng., 2012, 109, 867–876 CrossRef CAS PubMed.
  166. J. Tian, P. Wang, S. Gao, X. Chu, N. Wu and Y. Fan, FEBS J., 2010, 277, 4901–4908 CrossRef CAS PubMed.
  167. J. C. Joo, S. Pohkrel, S. P. Pack and Y. J. Yoo, J. Biotechnol., 2010, 146, 31–39 CrossRef CAS PubMed.
  168. H. S. Kim, Q. A. T. Le and Y. H. Kim, Enzyme Microb. Technol., 2010, 47, 1–5 CrossRef CAS.
  169. S. J. Kim, J. A. Lee, J. C. Joo, Y. J. Yoo, Y. H. Kim and B. K. Song, Biotechnol. Prog., 2010, 26, 1038–1046 CAS.
  170. H. S. Kim, A. T. L. Quang and Y. H. Kim, Enzyme Microb. Technol., 2010, 47, 1–5 CrossRef CAS.
  171. J. H. Zhang, Y. Lin, Y. F. Sun, Y. R. Ye, S. P. Zheng and S. Y. Han, Enzyme Microb. Technol., 2012, 50, 325–330 CrossRef CAS PubMed.
  172. A. Siglioccolo, R. Gerace and S. Pascarella, Biophys. Chem., 2010, 153, 104–114 CrossRef CAS PubMed.
  173. H. Yu and H. Huang, Biotechnol. Adv., 2014, 32, 308–315 CrossRef CAS PubMed.
  174. W. G. Touw and G. Vriend, Protein Eng., Des. Sel., 2014, 27, 457–462 CrossRef CAS PubMed.
  175. M. T. Reetz and J. D. Carballeira, Nat. Protoc., 2007, 2, 891–903 CrossRef CAS PubMed.
  176. K. Teilum, J. G. Olsen and B. B. Kragelund, Cell. Mol. Life Sci., 2009, 66, 2231–2247 CrossRef CAS PubMed.
  177. A. Heinecke, W. Eckhardt, M. Horsch and H.-J. Bungartz, in Supercomputing for Molecular Dynamics Simulations, Springer, 2015, pp. 11–29 Search PubMed.
  178. P. E. Lopes, O. Guvench and A. D. MacKerell, Molecular Modeling of Proteins, 2015, 47–71 CAS.
  179. F. Zhu, Y. Zhuang, B. Wu, J. Li and B. He, Appl. Biochem. Biotechnol., 2016, 178, 725–738 CrossRef CAS PubMed.
  180. M. Purmonen, J. Valjakka, K. Takkinen, T. Laitinen and J. Rouvinen, Protein Eng., Des. Sel., 2007, 20, 551–559 CrossRef CAS PubMed.
  181. M. B. Abdul Rahman, R. A. Karjiban, A. B. Salleh, D. Jacobs, M. Basri, T. Chor, A. Leow, H. A. Wahab and R. N. Z. Abd Rahman, Protein Pept. Lett., 2009, 16, 1360–1370 CrossRef PubMed.
  182. R. A. Karjiban, M. B. A. Rahman, M. Basri, A. B. Salleh, D. Jacobs and H. A. Wahab, Protein J., 2009, 28, 14–23 CrossRef PubMed.
  183. R. Abedi Karjiban, B. A. Rahman, A. Bakar Salleh, M. Basri, R. Noor Zaliha Raja Abd Rahman and A. L. T. Chor, Protein Pept. Lett., 2010, 17, 699–707 CrossRef.
  184. S. D'Auria, P. Herman, J. R. Lakowicz, E. Bertoli, F. Tanfani, M. Rossi and G. Manco, Proteins: Struct., Funct., Bioinf., 2000, 38, 351–360 CrossRef.
  185. P. L. Wintrode, D. Zhang, N. Vaidehi, F. H. Arnold and W. A. Goddard, J. Mol. Biol., 2003, 327, 745–757 CrossRef CAS PubMed.
  186. J. Fitter and J. Heberle, Biophys. J., 2000, 79, 1629–1636 CrossRef CAS PubMed.
  187. R. D. Sharma, A. M. Lynn and P. K. Sharma, et al., High temperature unfolding of Bacillus anthracis amidase-03 by molecular dynamics simulations, Bioinformation, 2009, 3(10), 430–434 CrossRef PubMed.
  188. D. S. Vieira and L. Degreve, Mol. Phys., 2009, 107, 59–69 CrossRef CAS.
  189. E. Bae and G. N. Phillips, J. Biol. Chem., 2005, 280, 30943–30948 CrossRef CAS PubMed.
  190. S. Vemparala, S. Mehrotra and H. Balaram, Biochim. Biophys. Acta, Proteins Proteomics, 2011, 1814, 630–637 CrossRef CAS PubMed.
  191. S. Kundu and D. Roy, J. Mol. Graphics Modell., 2010, 28, 820–827 CrossRef CAS PubMed.
  192. X. Yu, S. C. Sigler, D. Hossain, M. Wierdl, S. R. Gwaltney, P. M. Potter and R. M. Wadkins, J. Mol. Model., 2012, 18, 2869–2883 CrossRef CAS PubMed.
  193. W. Xu, P. Cai, M. Yan, L. Xu and P.-k. Ouyang, Chin. J. Chem. Phys., 2009, 22, 467–472 CrossRef CAS.
  194. Y. Mazola, O. Guirola, S. Palomares, G. Chinea, C. Menéndez, L. Hernández and A. Musacchio, J. Mol. Model., 2015, 21, 1–11 CrossRef CAS PubMed.
  195. K. Kumar, K. Patel, D. Agrawal and J. Khire, J. Mol. Model., 2015, 21, 1–13 CrossRef CAS PubMed.
  196. B. Singh, G. Bulusu and A. Mitra, J. Phys. Chem. B, 2015, 119, 392–409 CrossRef CAS PubMed.
  197. T. Tu, H. Luo, K. Meng, Y. Cheng, R. Ma, P. Shi, H. Huang, Y. Bai, Y. Wang and L. Zhang, Appl. Environ. Microbiol., 2015, 81, 6938–6944 CrossRef CAS PubMed.
  198. X. Yin, Y. Yao, M. Wu, T. Zhu, Y. Zeng and Q. Pang, Biochemistry, 2014, 79, 531–537 CAS.
  199. I. A. Noorbatcha, A. Sultan, H. Salleh and A. Amid, Protein J., 2013, 32, 309–316 CrossRef CAS PubMed.
  200. N. J. Christensen and K. P. Kepp, PLoS One, 2013, 8, e61985 CAS.
  201. L. Chen, X. Li, R. Wang, F. Fang, W. Yang and W. Kan, J. Biomol. Struct. Dyn., 2016, 1–14 Search PubMed.
  202. J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. D. Skeel, L. Kale and K. Schulten, J. Comput. Chem., 2005, 26, 1781–1802 CrossRef CAS PubMed.
  203. E. Lindahl, B. Hess and D. Van Der Spoel, Molecular Modeling Annual, 2001, 7, 306–317 CrossRef CAS.
  204. B. R. Brooks, C. L. Brooks, A. D. MacKerell, L. Nilsson, R. J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels and S. Boresch, J. Comput. Chem., 2009, 30, 1545–1614 CrossRef CAS PubMed.
  205. D. A. Case, T. E. Cheatham, T. Darden, H. Gohlke, R. Luo, K. M. Merz, A. Onufriev, C. Simmerling, B. Wang and R. J. Woods, J. Comput. Chem., 2005, 26, 1668–1688 CrossRef CAS PubMed.
  206. M. Kalimeri, P. Derreumaux and F. Sterpone, J. Non-Cryst. Solids, 2015, 407, 494–501 CrossRef CAS PubMed.
  207. C. Pfleger, P. C. Rathi, D. L. Klein, S. Radestock and H. Gohlke, J. Chem. Inf. Model., 2013, 53, 1007–1015 CrossRef CAS PubMed.
  208. P. C. Rathi, D. Mulnaes and H. Gohlke, Bioinformatics, 2015, 31, 2394–2396 CrossRef CAS PubMed.
  209. D. M. Krüger, P. C. Rathi, C. Pfleger and H. Gohlke, Nucleic Acids Res., 2013, gkt292 Search PubMed.
  210. P. C. Rathi, K.-E. Jaeger and H. Gohlke, PLoS One, 2015, 10, e0130289 Search PubMed.
  211. D. J. Jacobs, L. A. Kuhn and M. F. Thorpe, in Rigidity Theory and Applications, Springer, 2002, pp. 357–384 Search PubMed.
  212. A. Ahmed and H. Gohlke, Proteins: Struct., Funct., Bioinf., 2006, 63, 1038–1051 CrossRef CAS PubMed.
  213. A. Rader, Phys. Biol., 2009, 7, 016002 CrossRef CAS PubMed.
  214. P. C. Rathi, S. Radestock and H. Gohlke, J. Biotechnol., 2012, 159, 135–144 CrossRef CAS PubMed.
  215. I. B. Kuznetsov, Curr. Protein Pept. Sci., 2009, 10, 607–613 CrossRef CAS PubMed.
  216. H. Gohlke, L. A. Kuhn and D. A. Case, Proteins: Struct., Funct., Bioinf., 2004, 56, 322–337 CrossRef CAS PubMed.
  217. D. B. Craig and A. A. Dombkowski, BMC Bioinf., 2013, 14, 1 CrossRef PubMed.
  218. X. Yin, D. Hu, J.-F. Li, Y. He, T.-D. Zhu and M.-C. Wu, PLoS One, 2015, 10, e0126864 Search PubMed.
  219. S. Zhang, Y. Wang, X. Song, J. Hong, Y. Zhang and L. Yao, J. Chem. Inf. Model., 2014, 54, 2826–2833 CrossRef CAS PubMed.
  220. Z. Tan, J. Li, M. Wu and J. Wang, Appl. Biochem. Biotechnol., 2014, 173, 1752–1764 CrossRef CAS PubMed.
  221. M. Surzhik, A. Schmidt, E. Glazunov, D. Firsov and M. Petukhov, Appl. Biochem. Microbiol., 2014, 50, 118–124 CrossRef CAS.
  222. L. Liu, Z. Deng, H. Yang, J. Li, H.-d. Shin, R. R. Chen, G. Du and J. Chen, Appl. Environ. Microbiol., 2014, 80, 798–807 CrossRef PubMed.
  223. H. Ding, F. Gao, D. Liu, Z. Li, X. Xu, M. Wu and Y. Zhao, Enzyme Microb. Technol., 2013, 53, 365–372 CrossRef CAS PubMed.
  224. Y. Liu and B. Kuhlman, Nucleic Acids Res., 2006, 34, W235–W238 CrossRef CAS PubMed.
  225. K. W. Kaufmann, G. H. Lemmon, S. L. DeLuca, J. H. Sheehan and J. Meiler, Biochemistry, 2010, 49, 2987–2998 CrossRef CAS PubMed.
  226. A. Korkegian, M. E. Black, D. Baker and B. L. Stoddard, Science, 2005, 308, 857–860 CrossRef CAS PubMed.
  227. K. A. Bava, M. M. Gromiha, H. Uedaira, K. Kitajima and A. Sarai, Nucleic Acids Res., 2004, 32, D120–D121 CrossRef CAS PubMed.
  228. R. Guerois, J. E. Nielsen and L. Serrano, J. Mol. Biol., 2002, 320, 369–387 CrossRef CAS PubMed.
  229. J. Tian, N. Wu, X. Chu and Y. Fan, BMC Bioinf., 2010, 11, 1 CrossRef PubMed.
  230. Y. Dehouck, A. Grosfils, B. Folch, D. Gilis, P. Bogaerts and M. Rooman, Bioinformatics, 2009, 25, 2537–2543 CrossRef CAS PubMed.
  231. L.-T. Huang, M. M. Gromiha and S.-Y. Ho, Bioinformatics, 2007, 23, 1292–1293 CrossRef CAS PubMed.
  232. L.-T. Huang, L.-F. Lai and C.-C. Wu, Open Struct. Biol. J., 2009, 3, 143–148 CrossRef CAS.
  233. L.-T. Huang and M. M. Gromiha, Bioinformatics, 2009, 25, 2181–2187 CrossRef CAS PubMed.
  234. M. Masso and I. I. Vaisman, Bioinformatics, 2008, 24, 2002–2009 CrossRef CAS PubMed.
  235. V. Parthiban, M. M. Gromiha and D. Schomburg, Nucleic Acids Res., 2006, 34, W239–W242 CrossRef CAS PubMed.
  236. J. Cheng, A. Randall and P. Baldi, Proteins: Struct., Funct., Bioinf., 2006, 62, 1125–1132 CrossRef CAS PubMed.
  237. E. Capriotti, P. Fariselli, R. Calabrese and R. Casadio, Bioinformatics, 2005, 21, ii54–ii58 CrossRef CAS PubMed.
  238. S. Kang, G. Chen and G. Xiao, Protein Eng., Des. Sel., 2009, 22, 75–83 CrossRef CAS PubMed.
  239. R. R. Thangudu, P. Sharma, N. Srinivasan and B. Offmann, Proteins, 2007, 67, 255–261 CrossRef CAS PubMed.
  240. A. Vinayagam, G. Pugalenthi, R. Rajesh and R. Sowdhamini, Nucleic Acids Res., 2004, 32, D200–D202 CrossRef CAS PubMed.
  241. T. Meyer, M. D'Abramo, A. Hospital, M. Rueda, C. Ferrer-Costa, A. Pérez, O. Carrillo, J. Camps, C. Fenollosa and D. Repchevsky, Structure, 2010, 18, 1399–1409 CrossRef CAS PubMed.
  242. L. Baltzer and J. Nilsson, Curr. Opin. Biotechnol., 2001, 12, 355–360 CrossRef CAS PubMed.
  243. J. D. Bloom, M. M. Meyer, P. Meinhold, C. R. Otey, D. MacMillan and F. H. Arnold, Curr. Opin. Struct. Biol., 2005, 15, 447–452 CrossRef CAS PubMed.
  244. D. N. Bolon, C. A. Voigt and S. L. Mayo, Curr. Opin. Chem. Biol., 2002, 6, 125–129 CrossRef CAS PubMed.
  245. G. L. Butterfoss and B. Kuhlman, Annu. Rev. Biophys. Biomol. Struct., 2006, 35, 49–65 CrossRef CAS PubMed.
  246. M. Lehmann and M. Wyss, Curr. Opin. Biotechnol., 2001, 12, 371–375 CrossRef CAS PubMed.
  247. B. van den Burg and V. G. Eijsink, Curr. Opin. Biotechnol., 2002, 13, 333–337 CrossRef CAS PubMed.
  248. S. Sunyaev, W. Lathe IIIrd and P. Bork, Curr. Opin. Struct. Biol., 2001, 11, 125–130 CrossRef CAS PubMed.
  249. E. Capriotti, P. Fariselli and R. Casadio, Bioinformatics, 2004, 20(1), i63–68 CrossRef CAS PubMed.
  250. J. W. Pitera and P. A. Kollman, Proteins, 2000, 41, 385–397 CrossRef CAS.
  251. C. Deutsch and B. Krishnamoorthy, Bioinformatics, 2007, 23, 3009–3015 CrossRef CAS PubMed.
  252. Y. Li and J. Fang, PLoS One, 2012, 7, e47247 CAS.
  253. D. Gilis and M. Rooman, Protein Eng., 2000, 13, 849–856 CrossRef CAS PubMed.
  254. C. Magyar, M. M. Gromiha, G. Pujadas, G. E. Tusnady and I. Simon, Nucleic Acids Res., 2005, 33, W303–W305 CrossRef CAS PubMed.
  255. H. Zhou and Y. Zhou, Protein Sci., 2002, 11, 2714–2726 CrossRef CAS PubMed.
  256. H. Zhou and Y. Zhou, Proteins, 2004, 54, 315–322 CrossRef CAS PubMed.
  257. R. Guerois, J. E. Nielsen and L. Serrano, J. Mol. Biol., 2002, 320, 369–387 CrossRef CAS PubMed.
  258. V. Parthiban, M. M. Gromiha and D. Schomburg, Nucleic Acids Res., 2006, 34, W239–W242 CrossRef CAS PubMed.
  259. E. Capriotti, P. Fariselli, I. Rossi and R. Casadio, BMC Bioinf., 2008, 9(2), S6 CrossRef PubMed.
  260. J. Cheng, A. Randall and P. Baldi, Proteins, 2006, 62, 1125–1132 CrossRef CAS PubMed.
  261. Z. Dosztanyi, C. Magyar, G. Tusnady and I. Simon, Bioinformatics, 2003, 19, 899–900 CrossRef CAS PubMed.
  262. B. Shen, J. Bai and M. Vihinen, Protein Eng., Des. Sel., 2008, 21, 37–44 CrossRef CAS PubMed.
  263. V. Potapov, M. Cohen and G. Schreiber, Protein Eng., Des. Sel., 2009, 22, 553–560 CrossRef CAS PubMed.
  264. M. Dorn, M. B. E. Silva, L. S. Buriol and L. C. Lamb, Comput. Biol. Chem., 2014, 53, 251–276 CrossRef CAS PubMed.
  265. A. G. de Brevern, A. Bornot, P. Craveur, C. Etchebest and J.-C. Gelly, Nucleic Acids Res., 2012, 40, W317–W322 CrossRef CAS PubMed.
  266. G. OmPraba, D. Velmurugan, P. Arumugam, V. Govindasamy and P. Kalaichelvan, J. Biomol. Struct. Dyn., 2007, 25, 311–319 CAS.
  267. G. Manco, L. Camardella, F. Febbraio, G. Adamo, V. Carratore and M. Rossi, Protein Eng., 2000, 13, 197–200 CrossRef CAS PubMed.
  268. R. P. Singh, A. R. Rai, K. Roychoudhury and R. Dubey, J. Appl. Sci. Environ. Sanit., 2011, 6, 485–494 CAS.
  269. J. Wong, Molecular Modeling of Thermostable Endoglucanases, ProQuest, 2007 Search PubMed.
  270. I. Gontia-Mishra, V. Kumar Singh, N. Tripathi, S. Sasidharan and S. Tiwari, Biologia, 2014, 69, 1283–1294 CrossRef CAS.
  271. P. Farrokh, B. Yakhchali and A. A. Karkhane, J. Mol. Microbiol. Biotechnol., 2014, 24, 262–269 CrossRef CAS PubMed.
  272. G. Wang, R.-Y. Cao, R. Chen, L. Mo, J.-F. Han, X. Wang, X. Xu, T. Jiang, Y.-Q. Deng and K. Lyu, Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 7619–7624 CrossRef CAS PubMed.
  273. M. Biasini, S. Bienert, A. Waterhouse, K. Arnold, G. Studer, T. Schmidt, F. Kiefer, T. G. Cassarino, M. Bertoni and L. Bordoli, Nucleic Acids Res., 2014, gku340 Search PubMed.
  274. B. Webb and A. Sali, Current Protocols in Bioinformatics, 2014, 5.6.1–5.6.32 Search PubMed.
  275. R. Feng, B. Liang, C. Hou, D. Han, L. Han, Q. Lang, A. Liu and L. Han, Enzyme Microb. Technol., 2016, 84, 78–85 CrossRef CAS PubMed.
  276. H. Kaneko, H. Minagawa and J. Shimada, Biotechnol. Lett., 2005, 27, 1777–1784 CrossRef CAS PubMed.
  277. S. B. Mabrouk, N. Aghajari, M. B. Ali, E. B. Messaoud, M. Juy, R. Haser and S. Bejar, Bioresour. Technol., 2011, 102, 1740–1746 CrossRef PubMed.
  278. G. C. Mu, Y. Nie, X. Q. Mu, Y. Xu and R. Xiao, Appl. Biochem. Biotechnol., 2015, 176, 1736–1745 CrossRef CAS PubMed.
  279. B. S. Alipour, S. Hosseinkhani, S. K. Ardestani and A. Moradi, Photochem. Photobiol. Sci., 2009, 8, 847–855 Search PubMed.
  280. K. Rakesh, C. Nisha, S. Ranvir, K. Pushpender and K. Jagdeep, Adv. Genet. Eng., 2015, 2015 DOI:2169-0111.1000126.
  281. N. Akbulut, M. T. Öztürk, T. Pijning, S. İ. Öztürk and F. Gümüşel, J. Biotechnol., 2013, 164, 123–129 CrossRef CAS PubMed.
  282. B. Kumwenda, D. Litthauer, Ö. T. Bishop and O. Reva, Evol. Bioinf., 2013, 9, 327 CrossRef PubMed.
  283. S. Sinchaikul, B. Sookkheo, S. Phutrakul, Y.-T. Wu, F.-M. Pan and S.-T. Chen, Biochem. Biophys. Res. Commun., 2001, 283, 868–875 CrossRef CAS PubMed.
  284. S.-G. Lee, S.-P. Hong, J. J. Song, S.-J. Kim, M.-S. Kwak and M.-H. Sung, Appl. Environ. Microbiol., 2006, 72, 1588–1594 CrossRef CAS PubMed.
  285. Y. Xiaoyan, M. Zhen, C. Dongwei, G. Xu, J. Zeyer, L. Shuangjiang and C. Jiang, Chin. J. Chem. Eng., 2012, 20, 52–61 CrossRef.
  286. H. Wang, Y. Gong, W. Xie, W. Xiao, J. Wang, Y. Zheng, J. Hu and Z. Liu, Appl. Biochem. Biotechnol., 2011, 164, 1323–1338 CrossRef CAS PubMed.
  287. H. P. Sun, Y. Huang, X. F. Wang, Y. Zhang and H. B. Shen, Proteins: Struct., Funct., Bioinf., 2015, 83, 485–496 CrossRef CAS PubMed.
  288. J. Andreani and J. Söding, Bioinformatics, 2015, btv041 Search PubMed.
  289. P. Di Lena, K. Nagata and P. Baldi, Bioinformatics, 2012, 28, 2449–2457 CrossRef CAS PubMed.
  290. J. A. Brown, V. Pensabene, D. A. Markov, V. Allwardt, M. D. Neely, M. Shi, C. M. Britt, O. S. Hoilett, Q. Yang and B. M. Brewer, Biomicrofluidics, 2015, 9, 054124 CrossRef PubMed.
  291. T. A. Hopf, C. P. Schärfe, J. P. Rodrigues, A. G. Green, O. Kohlbacher, C. Sander, A. M. Bonvin and D. S. Marks, Elife, 2014, 3, e03430 Search PubMed.
  292. O. V. Galzitskaya, S. O. Garbuzynskiy and M. Y. Lobanov, Bioinformatics, 2006, 22, 2948–2949 CrossRef CAS PubMed.
  293. A. Schlessinger, G. Yachdav and B. Rost, Bioinformatics, 2006, 22, 891–893 CrossRef CAS PubMed.
  294. E. Cilia, R. Pancsa, P. Tompa, T. Lenaerts and W. F. Vranken, Nucleic Acids Res., 2014, 42, W264–W270 CrossRef CAS PubMed.
  295. P. Kountouris and J. D. Hirst, BMC Bioinf., 2009, 10, 1 CrossRef PubMed.
  296. B. Borguesan, M. Inostroza-Ponta and M. Dorn, J. Comput. Biol., 2016 DOI:10.1089/cmb.2016.0074.
  297. A. Drozdetskiy, C. Cole, J. Procter and G. J. Barton, Nucleic Acids Res., 2015, gkv332 Search PubMed.
  298. F. Ferrè and P. Clote, Nucleic Acids Res., 2006, 34, W182–W185 CrossRef PubMed.
  299. J. Yang, B.-J. He, R. Jang, Y. Zhang and H.-B. Shen, Bioinformatics, 2015, 31, 3773–3781 CAS.
  300. A. Yaseen and Y. Li, Accelerating knowledge-based energy evaluation in protein structure modeling with Graphics Processing Units, J. Parallel Distr. Comput., 2012, 72(2), 297–307 CrossRef.
  301. A. Ceroni, A. Passerini, A. Vullo and P. Frasconi, Nucleic Acids Res., 2006, 34, W177–W181 CrossRef CAS PubMed.
  302. N. Tokuriki, et al. , How protein stability and new functions trade off, PLoS Comput. Biol., 2008, 4(2), e1000002 Search PubMed.
  303. A. Fersht, Structure and mechanism in protein science: a guide to enzyme catalysis and protein folding, W. H. Freeman, New York, 1999 Search PubMed.
  304. D. L. Ollis, E. Cheah, M. Cygler, B. Dijkstra, F. Frolow, S. M. Franken, M. Harel, S. J. Remington, I. Silman and J. Schrag, Protein Eng., 1992, 5, 197–211 CrossRef CAS PubMed.
  305. B. M. Beadle and B. K. Shoichet, J. Mol. Biol., 2002, 321, 285–296 CrossRef CAS PubMed.
  306. C. Garcia, C. Nishimura, S. Cavagnero, H. J. Dyson and P. E. Wright, Biochemistry, 2000, 39, 11227–11237 CrossRef CAS PubMed.
  307. R. A. Nagatani, A. Gonzalez, B. K. Shoichet, L. S. Brinen and P. C. Babbitt, Biochemistry, 2007, 46, 6688–6695 CrossRef CAS PubMed.
  308. X. Wang, G. Minasov and B. K. Shoichet, J. Mol. Biol., 2002, 320, 85–95 CrossRef CAS PubMed.
  309. Y. Chen, B. Shoichet and R. Bonnet, J. Am. Chem. Soc., 2005, 127, 5423–5434 CrossRef CAS PubMed.
  310. J. D. Bloom, S. T. Labthavikul, C. R. Otey and F. H. Arnold, Proc. Natl. Acad. Sci. U. S. A., 2006, 103, 5869–5874 CrossRef CAS PubMed.
  311. R. Godoy-Ruiz, R. Perez-Jimenez, B. Ibarra-Molero and J. M. Sanchez-Ruiz, J. Mol. Biol., 2004, 336, 313–318 CrossRef CAS PubMed.
  312. H. Zhao and F. H. Arnold, Protein Eng., 1999, 12, 47–53 CrossRef CAS PubMed.
  313. G. Manco, E. Giosuè, S. D'Auria, P. Herman, G. Carrea and M. Rossi, Arch. Biochem. Biophys., 2000, 373, 182–192 CrossRef CAS PubMed.
  314. A. Gershenson, J. A. Schauerte, L. Giver and F. H. Arnold, Biochemistry, 2000, 39, 4658–4665 CrossRef CAS PubMed.
  315. T. Lazaridis, I. Lee and M. Karplus, Protein Sci., 1997, 6, 2589 CrossRef CAS PubMed.
  316. G. Hernández, F. E. Jenney, M. W. Adams and D. M. LeMaster, Proc. Natl. Acad. Sci. U. S. A., 2000, 97, 3166–3170 CrossRef.
  317. A. Merz, T. Knöchel, J. N. Jansonius and K. Kirschner, J. Mol. Biol., 1999, 288, 753–763 CrossRef CAS PubMed.
  318. R. M. Daniel, M. J. Danson, D. W. Hough, C. K. Lee, M. E. Peterson and D. A. Cowan, Protein Adapt. Extremophiles, 2008, 1–34 CAS.
  319. G. Feller, J. Phys.: Condens. Matter, 2010, 22, 323101 CrossRef PubMed.
  320. K. S. Siddiqui and R. Cavicchioli, Annu. Rev. Biochem., 2006, 75, 403–433 CrossRef CAS PubMed.
  321. K. S. Siddiqui, Crit. Rev. Biotechnol., 2016, 1–14 CrossRef PubMed.
  322. P. S. Low, J. L. Bada and G. N. Somero, Proc. Natl. Acad. Sci. U. S. A., 1973, 70, 430–432 CrossRef CAS.
  323. T. Lonhienne, C. Gerday and G. Feller, Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol., 2000, 1543, 1–10 CrossRef CAS.
  324. S. You, T. Tu, L. Zhang, Y. Wang, H. Huang, R. Ma, P. Shi, Y. Bai, X. Su and Z. Lin, Biotechnol. Biofuels, 2016, 9, 124 CrossRef PubMed.
  325. X. Duan, J. Chen and J. Wu, Appl. Environ. Microbiol., 2013, 79, 4072–4077 CrossRef CAS PubMed.
  326. Y. Liu, Z. Yan, X. Lu, D. Xiao and H. Jiang, Sci. Rep., 2016, 6, 24117 CrossRef CAS PubMed.
  327. A. Szlachcic, M. Zakrzewska and J. Otlewski, Biotechnol. Adv., 2011, 29, 436–441 CrossRef CAS PubMed.
  328. G. A. Lazar, S. A. Marshall, J. J. Plecs, S. L. Mayo and J. R. Desjarlais, Curr. Opin. Struct. Biol., 2003, 13, 513–518 CrossRef CAS PubMed.
  329. S. A. Marshall, G. A. Lazar, A. J. Chirino and J. R. Desjarlais, Drug Discovery Today, 2003, 8, 212–221 CrossRef CAS PubMed.
  330. A. Özen, M. Gönen, E. Alpaydın and T. Haliloğlu, BMC Struct. Biol., 2009, 9, 1 CrossRef PubMed.
  331. C. L. Worth, R. Preissner and T. L. Blundell, Nucleic Acids Res., 2011, 39, W215–W222 CrossRef CAS PubMed.
  332. D. E. Pires, D. B. Ascher and T. L. Blundell, Bioinformatics, 2014, 30, 335–342 CrossRef CAS PubMed.
  333. D. E. Pires, D. B. Ascher and T. L. Blundell, Nucleic Acids Res., 2014, gku411 Search PubMed.
  334. C.-W. Chen, J. Lin and Y.-W. Chu, BMC Bioinf., 2013, 14, 1 CrossRef PubMed.
  335. P. Fariselli, P. L. Martelli, C. Savojardo and R. Casadio, Bioinformatics, 2015, 31, 2816–2821 CrossRef CAS PubMed.
  336. M. Giollo, A. J. Martin, I. Walsh, C. Ferrari and S. C. Tosatto, BMC Genomics, 2014, 15, 1 CrossRef PubMed.
  337. V. Frappier, M. Chartier and R. J. Najmanovich, Nucleic Acids Res., 2015, gkv343 Search PubMed.
  338. L. Folkman, B. Stantic, A. Sattar and Y. Zhou, J. Mol. Biol., 2016, 428, 1394–1405 CrossRef CAS PubMed.
  339. J. Laimer, J. Hiebl-Flach, D. Lengauer and P. Lackner, Bioinformatics, 2016, btv769 Search PubMed.
  340. L. Quan, Q. Lv and Y. Zhang, Bioinformatics, 2016, btw361 Search PubMed.
  341. I. Hwang and S. Park, Drug Discovery Today, 2008, 5, e43–e48 CrossRef PubMed.
  342. R. E. Kontermann, Curr. Opin. Biotechnol., 2011, 22, 868–876 CrossRef CAS PubMed.
  343. S. Frokjaer and D. E. Otzen, Nat. Rev. Drug Discovery, 2005, 4, 298–306 CrossRef CAS PubMed.
  344. H. O. Alpar, S. Somavarapu, K. Atuah and V. Bramwell, Adv. Drug Delivery Rev., 2005, 57, 411–430 CrossRef CAS PubMed.
  345. A. Hussain, J. J. Arnold, M. A. Khan and F. Ahsan, J. Controlled Release, 2004, 94, 15–24 CrossRef CAS PubMed.
  346. N. Rathore and R. S. Rajan, Biotechnol. Prog., 2008, 24, 504–514 CrossRef CAS PubMed.
  347. W. Wang, Int. J. Pharm., 1999, 185, 129–188 CrossRef CAS PubMed.
  348. W. Wang, S. Singh, D. L. Zeng, K. King and S. Nema, J. Pharm. Sci., 2007, 96, 1–26 CrossRef CAS PubMed.
  349. S. M. Malakauskas and S. L. Mayo, Nat. Struct. Mol. Biol., 1998, 5, 470–475 CAS.
  350. A. V. Filikov, R. J. Hayes, P. Luo, D. M. Stark, C. Chan, A. Kundu and B. I. Dahiyat, Protein Sci., 2002, 11, 1452–1461 CrossRef CAS PubMed.
  351. P. Luo, R. J. Hayes, C. Chan, D. M. Stark, M. Y. Hwang, J. M. Jacinto, P. Juvvadi, H. S. Chung, A. Kundu and M. L. Ary, Protein Sci., 2002, 11, 1218–1226 CrossRef CAS PubMed.
  352. M. Zakrzewska, D. Krowarsch, A. Wiedlocha, S. Olsnes and J. Otlewski, J. Mol. Biol., 2005, 352, 860–875 CrossRef CAS PubMed.
  353. B. A. Shirley, P. Stanssensp, J. Steyaertfll and C. N. Pace, Chemistry, 1989, 264, 11621–11625 CAS.
  354. J. Caravella and A. Lugovskoy, Curr. Opin. Chem. Biol., 2010, 14, 520–528 CrossRef CAS PubMed.
  355. J. C. Almagro, S. Kodangattil and J. Li, Making and Using Antibodies: A Practical Handbook, 2013, p. 395 Search PubMed.
  356. M. J. Bernett, S. Karki, G. L. Moore, I. W. Leung, H. Chen, E. Pong, D.-H. T. Nguyen, J. Jacinto, J. Zalevsky and U. S. Muchhal, J. Mol. Biol., 2010, 396, 1474–1490 CrossRef CAS PubMed.
  357. M. Kügler, C. Stein, M. Schwenkert, D. Saul, L. Vockentanz, T. Huber, S. K. Wetzel, O. Scholz, A. Plückthun and A. Honegger, Protein Eng., Des. Sel., 2009, 22, 135–147 CrossRef PubMed.
  358. A. Honegger, A. D. Malebranche, D. Röthlisberger and A. Plückthun, Protein Eng., Des. Sel., 2009, 22, 121–134 CrossRef CAS PubMed.
  359. C. S. Fishburn, J. Pharm. Sci., 2008, 97, 4167–4183 CrossRef CAS PubMed.
  360. S. Jevševar, M. Kunstelj and V. G. Porekar, Biotechnol. J., 2010, 5, 113–128 CrossRef PubMed.
  361. N. Wang, W. F. Smith, B. R. Miller, D. Aivazian, A. A. Lugovskoy, M. E. Reff, S. M. Glaser, L. J. Croner and S. J. Demarest, Proteins: Struct., Funct., Bioinf., 2009, 76, 99–114 CrossRef CAS PubMed.
  362. L. Borras, T. Gunde, J. Tietz, U. Bauer, V. Hulmann-Cottier, J. P. Grimshaw and D. M. Urech, J. Biol. Chem., 2010, 285, 9054–9066 CrossRef CAS PubMed.
  363. M. Gebauer and A. Skerra, Curr. Opin. Chem. Biol., 2009, 13, 245–255 CrossRef CAS PubMed.
  364. D. Saerens, K. Conrath, J. Govaert and S. Muyldermans, J. Mol. Biol., 2008, 377, 478–488 CrossRef CAS PubMed.
  365. R. Gong, B. K. Vu, Y. Feng, D. A. Prieto, M. A. Dyba, J. D. Walsh, P. Prabakaran, T. D. Veenstra, S. G. Tarasov and R. Ishima, J. Biol. Chem., 2009, 284, 14203–14210 CrossRef CAS PubMed.
  366. J. F. Culajay, S. I. Blaber, A. Khurana and M. Blaber, Biochemistry, 2000, 39, 7153–7158 CrossRef CAS PubMed.
  367. T. Arakawa, S. J. Prestrelski, L. O. Narhi, T. C. Boone and W. C. Kenney, J. Protein Chem., 1993, 12, 525–531 CrossRef CAS PubMed.
  368. J. Ishikawa, M. Yoshimura, T. Matsunashi, N. Tominaga, H. Teshima, A. Hiraoka, H. Nakamura, H. Shibata, T. Masaoka and F. Takaku, Jpn. J. Clin. Oncol., 1991, 21, 169–175 CAS.
  369. D. Mark, S. Lu, A. Creasey, R. Yamamoto and L. Lin, Proc. Natl. Acad. Sci. U. S. A., 1984, 81, 5662–5666 CrossRef CAS.
  370. L. Wang, A. Gamez, H. Archer, E. E. Abola, C. N. Sarkissian, P. Fitzpatrick, D. Wendt, Y. Zhang, M. Vellard and J. Bliesath, J. Mol. Biol., 2008, 380, 623–635 CrossRef CAS PubMed.
  371. S. W. Pipe and R. J. Kaufman, Proc. Natl. Acad. Sci. U. S. A., 1997, 94, 11851–11856 CrossRef CAS.

This journal is © The Royal Society of Chemistry 2016