Establishment of the platform for reverse chemical genetics targeting novel protein–protein interactions

Abstract

In the “drug discovery” era, protein–protein interaction modules are becoming the most exciting group of targets for study. Although combinatorial libraries and active natural products are rapidly and systemically being equipped by both for-profit and not-for-profit organizations, complete drug-screening systems have not been achieved. There is a growing need for the establishment of drug discovery assays for highly effective utilization of the collected small molecules on a large scale. To generate drug-screening systems, we plan to identify novel protein–protein interactions that may participate in human diseases. The interactions have been identified by MS/MS analysis following immunoprecipitation using antibodies prepared from our cDNA projects. The intracellular pathway involving the identified interaction is computationally constructed, which then clarifies its relationship to the candidate disease. The development of reverse chemical genetics based on such information should help us to realize a significant increment in the number of drug discovery assays available for use. In this article, I describe our strategy for drug discovery and then introduce the applicability of fluorescence intensity distribution analysis (FIDA) and the expression-ready constructs called “ORF trap clones” to reverse chemical genetics.

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Hisashi Koga

Dr Hisashi Koga is the team leader of the CREATE (Collaboration of Regional Entities for the Advancement of Technological Excellence) program from JST (Japan Science and Technology Agency) and senior researcher (head of the mouse cDNA bank section) of the Kazusa DNA Research Institute. He is also a lecturer at the Brain Research Institute at Niigata University. He obtained his MD degree from the University of the Ryukyu Faculty of Medicine (1988) and his PhD degree from the Graduate School of Medical Sciences, Kumamoto University (1998). After one and a half years of postdoctoral work in the Department of Tumor Virology at Heinrich-Pette-Institute (Hamburg, Germany), he returned to Japan and joined the Helix Research Institute in 2000. At the end of 2001 he moved to the Kazusa DNA Research Institute. The main focus of his present research is the use of genomic and proteomic resources to design a suitable platform for chemical genetics.

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1. Introduction

Chemical genetics is an emerging research field utilizing biologically active compounds and natural products derived from several kinds of species such as plants and fungi.¹ Similar to conventional genetics, chemical genetics can be classified into two categories, “forward” and “reverse”. The goal of forward chemical genetics is to understand the relationship between the structure of small molecules and their phenotypic effects without prior knowledge of the target molecules. On the other hand, reverse chemical genetics, corresponding to “reverse genetics,” provide a phenotype-based screening based on the affinity of small molecules to the specific target protein identified beforehand.^2–4 Although these approaches of chemical genetics could both have a tremendous impact on drug discovery, the latter approach is suitable for expanding the knowledge of genomic and proteomic fields (e.g., the identification of receptor–ligand interaction) to the next level in diagnostic and therapeutic drugs discoveries.

Taking into consideration the necessity for information and knowledge in this field, we planned to establish a platform for the accumulation of related data, and first began a human cDNA sequencing project in 1994 in order to accumulate information about the long cDNAs that encode large proteins.^5–8 We focused our limited sequencing capacity on long cDNAs, because the function of many large proteins is possibly related to higher-order cell function and/or to illness (in fact, some of the larger proteins had already been identified as being expressed by disease-associated genes^9,10). Hoping also to contribute to neuroscience, one of the most exciting scientific fields, we constructed cDNA libraries derived from different areas of the human brain and identified more than 2000 genes (KIAA genes) unknown at the time they were sequenced.^6,10,11

Since December 2001, we have been working on the next step, collecting and characterizing cDNAs that encode mouse counterparts of human KIAA proteins (mKIAA). Mouse counterparts were used in order to overcome the legal and ethical restrictions on the use of human materials.^11–13 Specific molecules that capture proteins, such as antibodies, have become strong tools in further proteomic research. Therefore, we have also begun to generate ‘libraries’ of antibodies against mKIAA proteins.^14,15 Using our ‘libraries’ of antibodies, we are now identifying endogenous mKIAA protein–protein interactions.¹⁶ In our ongoing project, novel interactions were identified by MS/MS analysis following immunoprecipitation with anti-mKIAA antibodies. Studying the interactions with biologically known molecules should enable us to delineate the intracellular pathway related with the mKIAA/KIAA protein and further to link the protein with certain physiological and/or pathological states. This kind of knowledge promises to allow us to establish novel drug-screening systems for small molecules which could modulate the interaction.

This article highlights the platform established in our CREATE project in which reverse chemical genetics is expected to develop based on the progress, particularly, in the discovery of diagnostic and therapeutic drugs for neurological disorders. We also introduce our ongoing process for establishing a drug screening system based on protein–protein interactions.

2. How to utilize cDNA resources for identification of novel protein–protein interactions

For reverse chemical genetics, it is necessary to accumulate information about a target protein that plays an extremely important role in certain physiological or pathological states. To find such a drug-target protein, we started to identify the protein-coding sequences (CDSs) of unidentified human genes that encode large proteins (>100 kDa), beginning in 1994.⁸ Most positionally cloned human disease genes have been reported to encode large proteins like dystrophin.⁹ Moreover, the cellular functions of large proteins can be frequently classified to correspond with characteristics of multi-cellular organisms.¹⁰

These previous observations strongly motivated us to focus on genes encoding large proteins for identification of drug-target proteins. More than 2000 novel human genes were identified by our cDNA project (each cDNA was systematically designated as “KIAA” plus a four-digit number), and the average length of the cDNAs and gene products deduced from the cDNAs is 5.0 kb and 916 amino acid residues, respectively.⁵ Relatively great progress has been made in understanding the nature of KIAA genes; approximately 3% of the genes have already been identified as disease genes (Table 1). Furthermore, more than 4% of the genes encode proteins highly similar to disease gene products (amino acid identity >30%). The rest are still under investigation. Therefore we believe our focus on the large genes as a final step in reverse chemical genetics is a proper choice for the initial part of our project. It should also be noted that our cDNA project supplies evidence of the presence of the predicted genes that appeared by in silico analysis of the human genome sequences, at least at the transcriptional level. Detailed information regarding KIAA genes is available through the HUGE database (Human Unidentified Gene-Encoded large proteins: http://www.kazusa.or.jp/huge).¹⁷

Table 1 List of disease genes registered in the KIAA gene collection.

KIAA No.	Gene name	Length^a	Location	OMIM^b No.	OMIM named disorder
a The amino acid length deduced from the KIAA cDNA. b OMIM: Online Mendelian Inheritance in Man is a database of human genes and genetic disorders developed for the World Wide Web by NCBI (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM).
KIAA0006	ARHGEF6	773	Xq26	300267	Mental retardation, X-linked nonspecific type 46
KIAA0018	DHCR24	528	1p33–p31.1	606418	Desmosterolosis
KIAA0023	NUP214	2111	9q34.1	114350	Leukemia, acute myeloid
KIAA0027	MLC1	418	22qter	605908	Megalencephalic leukoencephalopathy with subcortical cysts
KIAA0160	JJAZ1	803	17	606245	Endometrial stromal tumors
KIAA0203	RB1CC1	1593	8q11	606837	Breast cancers
KIAA0207	GRB10	605	7p12–p11.2	601523	Russell–Silver syndrome
KIAA0243	TSC1	699	9q34	605284	Tuberous sclerosis-1
KIAA0328	ALMS1	2055	2p13	606844	Alstrom syndrome
KIAA0344	PRKWNK1	2066	12p13	605232	Pseudohypoaldosteronism type II
KIAA0347	PER2	1281	6	603426	Advanced sleep phase syndrome, familial
KIAA0382	ARHGEF12	750	11q23.3	604763	Leukemia, acute myeloid
KIAA0389	MYO6	1296	6q13	600970	Progressive, postlingual sensorineural deafness
KIAA0442	AUTS2	1266	7q11.2	607270	Autism
KIAA0457	DISC1	845	1q42.1	605210	Schizophrenia
KIAA0567	OPA1	978	3q28–q29	605290	Optic atrophy 1
KIAA0569	ZFHX1B	1318	2q22	605802	Hirschsprung disease–mental retardation syndrome
KIAA0591	KIF1B	1849	1p36.2	605995	Charcot–Marie–Tooth disease type 2A
KIAA0610	SPG20	686	13q12.3	607111	Troyer syndrome
KIAA0616	MECT1	634	19p13	607536	Malignant salivary gland tumor
KIAA0621	GRAF	753	5q31	605370	Leukemia, juvenile myelomonocytic
KIAA0673	NPHP4	1215	1p36	607215	Juvenile nephronophthisis
KIAA0730	SACS	1004	13q12	604490	Spastic ataxia, Charlevoix–Saguenay type
KIAA0778	ATP1A2	1049	1q21–q23	182340	Familial hemiplegic migraine-2
KIAA0837	FACL6	745	5q31	604443	Leukemia, acute myeloid
KIAA0845	NEFH	933	22q12.2	162230	Amyotrophic lateral sclerosis
KIAA0849	CYLD	960	16q12–q13	605018	Cylindromatosis, familial
KIAA0898	MUL	979	17q22–q23	253250	Mulibrey nanism
KIAA0986	CHAC	1458	9q21	605978	Choreoacanthocytosis
KIAA0991	MSF	568	17q25	604061	Leukemia, acute myeloid therapy-related
KIAA1017	HPS5	1095	11p15–p13	607521	Hermansky–Pudlak syndrome type 5
KIAA1073	MTMR2	675	11q22	603557	Charcot–Marie–Tooth disease type 4b
KIAA1083	SPG4	584	2p24–p21	604277	Spastic paraplegia-4
KIAA1113	TIF1G	1131	1p13	605769	Papillary thyroid carcinomas
KIAA1260	NLGN4	817	Xp22.33	300427	X-Linked autism
KIAA1347	ATP2C1	918	3q21–q24	604384	Hailey–Hailey disease
KIAA1351	WDR11	1243	10q26	606417	Glioblastoma
KIAA1385	GPHN	768	14	603930	Molybdenum cofactor deficiency type c
KIAA1438	MKL1	1075	22q13	606078	Acute megakaryocytic leukemia
KIAA1480	NLGN3	682	Xq13	300336	X-Linked autism
KIAA1563	ALS2	1658	2q33	606352	Amyotrophic lateral sclerosis
KIAA1581	ANKH	545	5p15.2–p14.1	605145	Craniometaphyseal dysplasia
KIAA1620	PRX	1398	19q13.1–q13.2	605725	Dejerine–Sottas neuropathy
KIAA1650	SHANK3	797	22q13.3	606230	22q13.3 deletion syndrome
KIAA1667	HPS4	505	22q11.2–q12.2	606682	Hermansky–Pudlak syndrome
KIAA1766	SBF2	1123	11p15	607697	Charcot–Marie–Tooth disease type 4B2
KIAA1774	CDH23	1041	10q21–q22	601067	Usher syndrome type 1d
KIAA1788	ALX4	413	11p11.2	605420	Parietal foramina 2
KIAA1812	CDH23	803	10q21–q22	605516	Usher syndrome type ID
KIAA1819	MAML2	1173	11q21	607537	Malignant salivary gland tumor
KIAA1820	RAI1	1644	17p11.2	607642	Smith–Magenis syndrome
KIAA1823	PHF6	377	Xq26.3	300414	Borjeson–Forssman–Lehmann syndrome
KIAA1845	CAPN10	705	2q37.3	605286	Diabetes mellitus, non-insulin dependent 1
KIAA1943	MASS1	1054	5q14	602851	Febrile convulsions, familial 4
KIAA1988	CIRH1A	636	16q22	607456	North American Indian childhood cirrhosis

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For the second step of our project, we started to comprehensively collect antibodies against these large proteins.^14,18,19 Specific molecules capturing proteins make it possible to identify their binding partners and consequently clarify their biological importance in certain physiological or pathological states. It is well known that the CDSs of mouse genes are highly homologous to those of human genes,^11,20 and antibodies against mouse proteins are expected to crossreact with human counterparts. We have thus been generating antibodies against mouse KIAA (mKIAA) proteins, to avoid ethical restrictions involved with the use of human materials. At present, we have already generated more than 1500 antibodies and are partly distributing information regarding mKIAA proteins through the InGaP database (Integrative Gene and Protein expression database; http://www.kazusa.or.jp/create/index.jsp).¹⁹

Fig. 1 is a schematic representation of our strategy to generate and to evaluate anti-mKIAA antibodies. To produce antigens in a high-throughput manner, we used shotgun clones generated during the entire sequencing of mKIAA cDNAs and the in vitro recombination-assisted method for rapid and accurate transfer of the mKIAA gene fragment to the expression construct.^14,21,22 After purification of the antigens, polyclonal antibodies against the mKIAA proteins were generated in rabbits. Although monoclonal antibodies are thought to be more specific than polyclonal antibodies, we focused on the evaluation and following protein–protein interaction experiments. The resulting antibodies were subjected to the following four experiments to evaluate their titers and specificities: (1) ELISA; (2) Western blotting using the samples extracted from adult mouse organs; (3) Western blotting using the samples extracted from cell lines derived from several mouse tissues; and (4) immunohistochemical analysis of the mouse brain. Using the validated antibodies derived from our cDNA project, we are now trying to identify novel protein–protein interactions involving mKIAA proteins.

Fig. 1 Our strategy for generating and evaluating anti-mKIAA antibodies. The cDNA libraries were constructed by the in vitro recombination-assisted method. More than 180 000 clones from size-fractionated libraries were subjected to single-pass sequencing from the 5′ and/or 3′ ends. Subsequently the single-pass end sequences were subjected to a BLAST search against human KIAA cDNA sequences, and cDNA clones with the end sequences possessing a BLAST score of 120 or greater were selected as candidates for mouse KIAA-homologous cDNA. In the difficult cases for orthologue selection from the single-pass sequencing information, we further applied MUCH (multiplex cloning of homologous genes³¹) and/or a PCR-based screening method. The selected cDNA clones were subjected to entire sequencing by a shotgun approach. Then, 0.8- to 1.0-kb shotgun fragments appropriate for antigens were finally transferred into the destination vector pGEX-4TDES or -6PDES by BP and LR recombination reactions. The robotically isolated plasmid DNAs were used for transformation into E. coli Rosetta (pLysS) (Novagen, Madison, WI). The solubility of the recombinant antigens was preliminarily assessed in a small-scale culture, and then large-scale production and purification was performed using glutathione-affinity beads or retrieval from polyacrylamide gels, depending on their solubility.¹⁴ After purification of the antigens, polyclonal antibodies were generated in rabbits by subcutaneous injection of each antigen mixed with Freund's complete adjuvant. The resulting rabbit antisera were subjected to several experiments to evaluate their titers and specificities.¹⁸

3. Importance of the information about protein–protein interaction for reverse chemical genetics

The amino acid region concerning protein–protein interaction is usually comprised of 28–40 amino acid residues, and the osculating plane is relatively wide (∼1600 Å). The drug design targeting the interaction surface is comparatively difficult, because it should be a target in the relative flat plane without an apparent pocket structure. A recent advancement overcame this difficulty, and many successful cases have already been reported. For example, Garcia-Echeverria et al. developed a small molecule interfering with the p53/hdm2 protein–protein interaction. A small peptidic inhibitor was first identified using phage display technology.^23,24 A structurally similar compound was then substituted for each amino acid, and extremely high activity was finally achieved (VIth European Symposium of The Protein Society, April 2005, Barcelona). A protein–protein interaction inhibitor based on the tertiary structure was also recently developed. Oltersdorf et al. reported a low-molecular weight compound that suppresses the function of the Bcl-2 protein.²⁵ These recent successes strongly encourage us to find novel protein–protein interactions as potential targets for diagnostic and therapeutic drugs.

Fig. 2 shows our strategy for the identification of novel protein–protein interactions. To identify novel mKIAA protein–protein interactions, we performed MS/MS analysis following immunoprecipitation with anti-mKIAA antibodies. Expecting efficient identification, we selected highly expressed tissues or cell lines based on the information obtained from the evaluation step of the antibody. The immunoprecipitates were separated by a 1-DE, in gel digested with trypsin, and subsequently analyzed by LC-MS/MS (LCQ, Thermo Finnigan, CA, USA). Based on this information about protein–protein interactions, an intracellular pathway related to the mKIAA protein could be constructed using commercially available pathway software (Ingenuity Pathway Analysis software, PathwayAssist, and KeyMolnet). Consequently the pathway may clarify the participation of the interaction in certain physiological or pathological states. Such interaction is a potential target for a specific disease and would be used for the establishment of a drug screening system for reverse chemical genetics.

Fig. 2 Our strategy for identification of novel protein–protein interactions and their involvement in the intracellular pathway. To identify novel protein–protein interaction targeting diagnostic and therapeutic drugs, we subjected tissue (or cell line) lysate to immunoprecipitation with antibodies against mKIAA protein. The immunoprecipitates were separated by a 1-DE, in gel digested with trypsin, and subsequently analyzed by LC-MS/MS (LCQ, Thermo Finnigan, CA, USA). The intracellular pathway involving the identified interaction was computationally constructed, which clarified its relationship to the candidate disease. This schema represents mKIAA0769 protein, a mammalian FCHSD (FCH and double SH3 domains) family protein, only predicted by in silico analysis.³² The interaction data and the constructed intracellular pathway are distributed through the InCeP (IntraCellular Pathway based on mKIAA protein–protein interactions database (http://www.kazusa.or.jp/create/index.jsp).

For instance, Fig. 2 represents the case of the mKIAA0769 protein. This protein is a hypothetical protein and no biological evidence of its existence has been reported. The presence of well-known domains such as Cdc15/Fes/CIP4 and SH3 domains may suggest the important function of this protein in connection with protein–protein interaction. To obtain endogenous mKIAA0769 and interacting proteins, we subjected protein lysate from an adult organ to immunoprecipitation with anti-mKIAA0769 antibody. Several known proteins were consequently co-immunoprecipitated. GEMIN4, GEMIN5, and DHX9 are some of these interacting proteins and function in RNA processing; therefore, the mKIAA0769 protein may also have an important role in this phenomenon. After construction of the pathway based on these interacting proteins, we speculated that mKIAA0769 participates in the pathogenesis of Alzheimer's disease.^26,27 Hence, a drug design targeting mKIAA0769 protein interaction will be a promising new candidate for the diagnosis, prevention, and treatment of Alzheimer's disease.

4. Our strategy for reverse chemical genetics

To generate drug screening systems based on protein–protein interactions using reverse chemical genetics, it is necessary to determine the precise interacting modules on each protein. We apply the strategy fostered during the entire sequencing of cDNAs and the generation of antigens (that is, the utilization of shotgun fragments and subsequent recombination reactions) and express recombinant protein fragments in a 384-well format. Using expressed proteins, the precise interacting modules are determined by fluorescence intensity distribution analysis (FIDA).²⁸ FIDA is based on the detection of instantaneous photon emission rates from an open confocal volume and was recently applied to the detection of protein–protein interactions.

After determination of the interacting modules, the modules can be directly used for the subsequent establishment of the screening system (Fig. 3). For example, Schilb et al. reported a FIDA-based high-throughput screening assay to search for active site modulators of the human heat shock protein 90β.²⁹ Heilker et al. also review the applicability of FIDA to GPCR-focused high-throughput screening and compare FIDA to two other GPCR-adaptable drug discovery techniques for ligand binding studies; the scintillation proximity assay (SPA) and macroscopic fluorescence polarization (FP) measurements.³⁰ FIDA is amenable to working with extremely low amounts of target molecules without immobilization of the molecules. Moreover, the FIDA technique can be adapted to relatively crude protein samples and is easily applicable to a high-throughput format due to its femtoliter-sized measurement. This technique thus can bring substantial benefits as a screening platform to reverse chemical genetics.

Fig. 3 Our strategy for drug discovery targeting a protein–protein interaction using reverse chemical genetics. Schematic illustration shows our strategy for drug discovery targeting a protein–protein interaction using reverse chemical genetics. For the first screening, fluorescence intensity distribution analysis (FIDA) is applied to select candidate inhibitors in a 384-well format. For further confirmation of the effect in vivo, the screening platform based on the cultured cells that express fluorescent-tagged KIAA/mKIAA protein was established. For this second screening, we used a combination of expression-ready constructs called “ORF trap clones” and a fluorescence cell analyzer, iCys (OLYMPUS).

Although the FIDA technique provides us several candidates for inhibitors of the protein–protein interaction in vitro, we must confirm the effect in vivo, since drug efficacy is dramatically altered by the penetrate rate and the intracellular metabolism of the small molecules. Considering this, we are also establishing a screening platform based on cultured cells that express fluorescent-tagged KIAA/mKIAA protein in a high-throughput manner. We transfect KIAA/mKIAA expression constructs in HEK293 cells and observe phenotypical changes and altered subcellular localization of the expressed protein following exposure to candidate inhibitors (Fig. 3). For this second screening, we have already generated expression-ready constructs called “ORF trap clones” in which full-length ORFs from KIAA/mKIAA cDNAs were introduced by a homologous recombination.³³ Moreover, the expression of fluorescent-tagged KIAA/mKIAA protein in cultured cells may be combined with fluorescence cell analyzers such as iCys (OLYMPUS) or IN Cell Analyzer (GE Healthcare) for further high-throughput exploitation. Depending on the cell type, the amount of each component in a given signal pathway is substantially different, so we must carefully consider changing the expressed host cells if we cannot obtain the expected results. We believe the discovery of drugs to the disorders related to KIAA genes will be accelerated by the two-step screening we have described.

Abbreviations

KIAA: “KI” stands for “Kazusa DNA Research Institute” and “AA” are reference characters; cDNA: complementary DNA; MS: mass spectrometry; CREATE: Collaboration of Regional Entities for the Advancement of Technological Excellence; FIDA: fluorescence intensity distribution analysis; ORF: open reading frame; HEK293: human embryonic kidney cell line 293; LC: liquid chromatography; 1-DE: one-dimensional gel electrophoresis; CDSs: protein-coding sequences; ELISA: enzyme-linked immunosorbent assay; p53: tumor protein p53; hdm2: transformed 3T3 cell double minute 2; Bcl-2: B-cell CLL/lymphoma 2; Cdc15/Fes/CIP4: cell division cycle 15/feline sarcoma oncogene/Cdc42-interacting protein 4; SH3: src homology-3; GEMIN: gem (nuclear organelle) associated protein; DHX9: DEAH (Asp-Glu-Ala-His) box polypeptide 9; GPCR: G protein-coupled receptor

Acknowledgements

I am grateful to all of the staff at the Kazusa DNA Research Institute. The work introduced here was supported by the CREATE Program (Collaboration of Regional Entities for the Advancement of Technological Excellence) from JST (Japan Science and Technology Agency) and a grant from Kazusa DNA Research Institute.

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