Themed collection Computational protein design and structure prediction: Celebrating the 2024 Nobel Prize in Chemistry
Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning
Proposed de novo peptide design strategy against amyloidogenic targets. After initial computational preparation of the binder and target, the computational and experimental validation are incorporated in iterative machine learning powered cycles to generate better and improved peptide-based targets.
Chem. Commun., 2024,60, 632-645
https://doi.org/10.1039/D3CC04630C
Navigating the landscape of enzyme design: from molecular simulations to machine learning
Efficiently harnessing big data by combining molecular modelling and machine learning accelerates rational enzyme design for its applications in fine chemical synthesis and waste valorization, to address global environmental issues and sustainable development.
Chem. Soc. Rev., 2024,53, 8202-8239
https://doi.org/10.1039/D4CS00196F
Strategies for designing biocatalysts with new functions
Enzymes can be optimized to accelerate chemical transformations via a range of methods. In this review, we showcase how protein engineering and computational design techniques can be interfaced to develop highly efficient and selective biocatalysts.
Chem. Soc. Rev., 2024,53, 2851-2862
https://doi.org/10.1039/D3CS00972F
Computational design of orthogonal nucleoside kinases
Rosetta design software was employed to remodel the substrate specificity of Drosophila melanogaster 2′-deoxyribonucleoside kinase for efficient phosphorylation of the nucleoside analog prodrug 3′-deoxythymidine.
Chem. Commun., 2010,46, 8803-8805
https://doi.org/10.1039/C0CC02961K
De novo design of peptides that bind specific conformers of α-synuclein
De novo designed peptides bind specific conformers of α-synuclein fibrils.
Chem. Sci., 2024,15, 8414-8421
https://doi.org/10.1039/D3SC06245G
Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins
Hydrogen atom transfer (HAT) reactions, as they occur in many biological systems, are here predicted by machine learning.
Chem. Sci., 2024,15, 2518-2527
https://doi.org/10.1039/D3SC03922F
Tidying up the conformational ensemble of a disordered peptide by computational prediction of spectroscopic fingerprints
Pairing experiments with simulations, we predict spectroscopic fingerprints, enhancing understanding of disordered peptides' conformational ensembles. This helps rationalize elusive structure-spectra relationships for these peptides and proteins.
Chem. Sci., 2023,14, 8483-8496
https://doi.org/10.1039/D3SC02202A
Combining structural and coevolution information to unveil allosteric sites
Structure-based three-parameter model that integrates local binding site information, coevolutionary information, and information on dynamic allostery to identify potentially hidden allosteric sites in ensembles of protein structures.
Chem. Sci., 2023,14, 7057-7067
https://doi.org/10.1039/D2SC06272K
Thermodynamic origins of two-component multiphase condensates of proteins
We develop a computational method integrating a genetic algorithm with a residue-level coarse-grained model of intrinsically disordered proteins in order to uncover the molecular origins of multiphase condensates and enable their controlled design.
Chem. Sci., 2023,14, 1820-1836
https://doi.org/10.1039/D2SC05873A
Remodeling a β-peptide bundle
We apply the Rosetta algorithm to repack the hydrophobic core of a β-peptide bundle while retaining both structure and stability.
Chem. Sci., 2013,4, 319-324
https://doi.org/10.1039/C2SC21117C
Reaction mechanism and regioselectivity of uridine diphosphate glucosyltransferase RrUGT3: a combined experimental and computational study
A substrate binding induced conformational change was found to be essential for the occurrence of RrUGT3 catalyzed transglycosylation reactions.
Catal. Sci. Technol., 2024,14, 4882-4895
https://doi.org/10.1039/D4CY00721B
ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis tackle tasks in a dynamic setting.
Digital Discovery, 2024,3, 1389-1409
https://doi.org/10.1039/D4DD00013G
PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening
PIGNet2, a versatile protein–ligand interaction prediction model that performs well in both molecule identification and optimization, demonstrates its potential in early-stage drug discovery.
Digital Discovery, 2024,3, 287-299
https://doi.org/10.1039/D3DD00149K
Computational thermostability engineering of a nitrile hydratase using synergetic energy and correlated configuration for redesigning enzymes (SECURE) strategy
A computational strategy using synergetic energy and correlated configuration for redesigning enzymes (SECURE) is proposed for the thermostability engineering of multimeric proteins.
Catal. Sci. Technol., 2023,13, 5880-5891
https://doi.org/10.1039/D3CY01102J
A deep learning model for type II polyketide natural product prediction without sequence alignment
Utilizing a large protein language model, we have formulated a deep learning framework designed for predicting type II polyketide natural products.
Digital Discovery, 2023,2, 1484-1493
https://doi.org/10.1039/D3DD00107E
Benchmarking protein structure predictors to assist machine learning-guided peptide discovery
Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles.
Digital Discovery, 2023,2, 981-993
https://doi.org/10.1039/D3DD00045A
Parallelized identification of on- and off-target protein interactions
Yeast surface display using multi target selections enables monitoring of specificity profiles for thousands of proteins in parallel.
Mol. Syst. Des. Eng., 2020,5, 349-357
https://doi.org/10.1039/C9ME00118B
Accelerated electron transport from photosystem I to redox partners by covalently linked ferredoxin
Tethering ferredoxin (PetF) to photosystem I increased light-induced PetF-mediated electron transfer to soluble acceptors. Tethering was equivalent to using a ten-to-one molar ratio of soluble PetF to PSI.
Phys. Chem. Chem. Phys., 2013,15, 19608-19614
https://doi.org/10.1039/C3CP53264J
About this collection
This cross-journal collection celebrates the 2024 Nobel Prize in Chemistry by bringing together research published on computational protein design and protein structure prediction. Nobel Laureates Demis Hassabis and John M. Jumper have successfully used artificial intelligence to predict the structure of almost all known proteins, and Nobel Laureate David Baker has used this technology to design and create entirely new proteins. This collection highlights work on protein design and analysis using computational methods, providing applications in biocatalysis, drug design and more.