- Open Access
GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions
© Kundrotas et al.;licensee BioMed Central Ltd. 2012
- Received: 27 June 2012
- Accepted: 11 July 2012
- Published: 11 July 2012
Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database (http://gwidd.bioinformatics.ku.edu) offers an extensive source of data for structural studies of protein-protein complexes on genome scale. The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics.
- Protein-protein interactions
- Structural modeling
- Protein docking
- Structural genomics
Proteins function by interacting with other biologically relevant molecules. Understanding the mechanisms of protein-protein interactions (PPI) is essential for studying life processes at the molecular level. Genome sequencing provided a vast amount of information on proteins at the sequence level. Currently, efforts focus on the function assignment of these proteins based on their three-dimensional (3D) structures and interactions. Interaction maps for specific organisms and biochemical pathways need to be complemented by the structural information. Experimental techniques are limited in their ability to produce the structures on the genome scale. Thus, computational methods are essential for this task .
Structural modeling of PPI has its origins in ab initio techniques based on shape and physicochemical complementarity. More recent approaches take advantage of statistical potentials and machine learning [2, 3]. Despite progress in development of such template-free algorithms, their accuracy in the high-throughput structure determination is limited.
Rapidly increasing amount of data on PPI makes possible application of the template-based methods. Such approaches are based on the observation that monomers with similar sequences and/or structures, generally, have similar binding modes. Several groups assessed the quality of PPI modeling based on sequence alignment to complexes with known structure [4–9]. Studies showed that the majority of such homology-docking models are of acceptable and medium quality, according to the established criteria . An alternative template-based approach takes advantage of the structural similarity between the target and the template complexes [10–13].
The progress in 3D modeling of PPI is reflected in the Genome-Wide Docking Database (GWIDD) , which provides annotated collection of experimental and modeled PPI structures from the entire universe of life spanning from viruses to humans. The resource has user-friendly search interface, providing preview and download options for experimental and modeled PPI structures.
The user can enable the second half of the search interface if information related to the interaction partner is available (‘protein B,’ Figure 2). The search results can be filtered by the structure availability (experimental, modeled, or no structures). Online help is provided in pop-up windows. The search result screen displays all interactions in the database satisfying the input search criteria in the form of an expandable list of GWIDD interaction IDs. For the homology-docking models, the alignments used to build the model are provided, and the model quality is assessed by the sequence identity criteria . Links are provided to download the PDB-format files, along with the text file containing relevant information. Visualization screen is available to display the structures by different interactive representations. A link is provided to download the entire set of sequence-homology models in one gzipped archive.
GWIDD development will incorporate other structural modeling techniques, such as multi-template/threading modeling of interacting proteins, partial structural alignment , and template-free docking by GRAMM [23–25]. A major expansion of GWIDD will be the incorporation of new PPI sources from other publicly available PPI databases. Large-scale systematic benchmarking of the high-through put modeling will be used to assign a confidence score to the modeled structures.
Andrey Tovchigrechko and Tatiana Baronova made important contributions to the GWIDD project at the earlier stages of development. This work was supported by the National Institutes of Health grant R01 GM074255.
- Russell RB, Alber F, Aloy P, Davis FP, Korkin D, Pichaud M, Topf M, Sali A: A structural perspective on protein–protein interactions. Curr Opin Struct Biol. 2004, 14: 313-324. 10.1016/j.sbi.2004.04.006.View ArticlePubMedGoogle Scholar
- Vakser IA, Kundrotas P: Predicting 3D structures of protein-protein complexes. Curr Pharm Biotech. 2008, 9: 57-66. 10.2174/138920108783955209.View ArticleGoogle Scholar
- Lensink MF, Wodak SJ: Docking and scoring protein interactions: CAPRI 2009. Proteins. 2010, 78: 3073-3084. 10.1002/prot.22818.View ArticlePubMedGoogle Scholar
- Aloy P, Pichaud M, Russell RB: Protein complexes: structure prediction challenges for the 21st century. Curr Opin Struct Biol. 2005, 15: 15-22. 10.1016/j.sbi.2005.01.012.View ArticlePubMedGoogle Scholar
- Aloy P, Russell RB: Interrogating protein interaction networks through structural biology. Proc Natl Acad Sci USA. 2002, 99: 5896-5901. 10.1073/pnas.092147999.PubMed CentralView ArticlePubMedGoogle Scholar
- Kundrotas PJ, Alexov E: Predicting 3D structures of transient protein-protein complexes by homology. Bioch Biophys Acta. 2006, 1764: 1498-1511. 10.1016/j.bbapap.2006.08.002.Google Scholar
- Kundrotas PJ, Lensink MF, Alexov E: Homology-based modeling of 3D structures of protein-protein complexes using alignments of modified sequence profiles. Int J Biol Macromol. 2008, 43: 198-208. 10.1016/j.ijbiomac.2008.05.004.View ArticlePubMedGoogle Scholar
- Lu L, Lu H, Skolnick J: MULTIPROSPECTOR: an algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins. 2002, 49: 350-364. 10.1002/prot.10222.View ArticlePubMedGoogle Scholar
- Mukherjee S, Zhang Y: Protein-protein complex structure predictions by multimeric threading and template recombination. Structure. 2011, 13: 955-966.View ArticleGoogle Scholar
- Gunther S, May P, Hoppe A, Frommel C, Preissner R: Docking without docking: ISEARCH - prediction of interactions using known interfaces. Proteins. 2007, 69: 839-844. 10.1002/prot.21746.View ArticlePubMedGoogle Scholar
- Keskin O, Nussinov R, Gursoy A: PRISM: protein-protein interaction prediction by structural matching. Methods Mol Biol. 2008, 484: 505-521. 10.1007/978-1-59745-398-1_30.PubMed CentralView ArticlePubMedGoogle Scholar
- Sinha R, Kundrotas PJ, Vakser IA: Docking by structural similarity at protein-protein interfaces. Proteins. 2010, 78: 3235-3241. 10.1002/prot.22812.PubMed CentralView ArticlePubMedGoogle Scholar
- Korkin D, Davis FP, Alber F, Luong T, Shen M, Lucic V, Kennedy MB, Sali A: Structural modeling of protein interactions by analogy: application to PSD-95. PLoS Comp Biol. 2006, 2: 1365-1376.View ArticleGoogle Scholar
- Kundrotas PJ, Zhu Z, Vakser IA: GWIDD: genome-wide protein docking database. Nucl Acid Res. 2010, 38: D513-D517. 10.1093/nar/gkp944.View ArticleGoogle Scholar
- Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, Bantoft K, Betel D, Bobechko B, Boutilier K, Burgess E, Buzadzija K, Cavero R, D’Abreo C, Donaldson I, Dorairajoo D, Dumontier MJ, Dumontier MR, Earles V, Farrall R, Feldman H, Garderman E, Gong Y, Gonzaga R, Grytsan V, Gryz E, Gu V, Haldorsen E, Halupa A, Haw R, Hrvojic A, et al: The Biomolecular Interaction Network Database and related tools 2005 update. Nucl Acid Res. 2005, 33: D418-D424.View ArticleGoogle Scholar
- Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D: The Database of Interacting Proteins: 2004 update. Nucl Acid Res. 2004, 32: D449-D451. 10.1093/nar/gkh086.View ArticleGoogle Scholar
- Xenarios I, Rice DW, Salwinski L, Baron NK, Marcotte EM, Eisenberg D: DIP: the Database of Interacting Proteins. Nucleic Acids Res. 2000, 28: 289-291. 10.1093/nar/28.1.289.PubMed CentralView ArticlePubMedGoogle Scholar
- Ceol A, Aryamontri AC, Licata L, Peluso D, Briganti L, Perfetto L, Castagnoli L, Cesareni G: MINT, the molecular interaction database: 2009 update. Nucl Acid Res. 2010, 38: D532-D539. 10.1093/nar/gkp983.View ArticleGoogle Scholar
- Stark C, Breitkreutz BJ, Chatr-Aryamontri A, Boucher L, Oughtred R, Livstone MS, Nixon J, Van Auken K, Wang X, Shi X, Reguly T, Rust JM, Winter A, Dolinski K, Tyers M: The BioGRID Interaction Database: 2011 update. Nucl Acid Res. 2011, 39: D698-D704. 10.1093/nar/gkq1116.View ArticleGoogle Scholar
- Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J, Kerssemakers J, Leroy C, Menden M, Michaut M, Montecchi-Palazzi L, Neuhauser SN, Orchard S, Perreau V, Roechert B, van Eijk K, Hermjakob H: The IntAct molecular interaction database in 2010. Nucl Acid Res. 2010, 38: D525-D531. 10.1093/nar/gkp878.View ArticleGoogle Scholar
- Petrey D, Xiang Z, Tang CL, Xie L, Gimpelev M, Mitros T, Soto CS, Goldsmith-Fischman S, Kernytsky A, Schlessinger A, Koh IY, Alexov E, Honig B: Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling. Proteins. 2003, 53: 430-435. 10.1002/prot.10550.View ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of database programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralView ArticlePubMedGoogle Scholar
- Katchalski-Katzir E, Shariv I, Eisenstein M, Friesem AA, Aflalo C, Vakser IA: Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci USA. 1992, 89: 2195-2199. 10.1073/pnas.89.6.2195.PubMed CentralView ArticlePubMedGoogle Scholar
- Vakser IA, Matar OG, Lam CF: A systematic study of low-resolution recognition in protein-protein complexes. Proc Natl Acad Sci USA. 1999, 96: 8477-8482. 10.1073/pnas.96.15.8477.PubMed CentralView ArticlePubMedGoogle Scholar
- Tovchigrechko A, Wells CA, Vakser IA: Docking of protein models. Protein Sci. 2002, 11: 1888-1896. 10.1110/ps.4730102.PubMed CentralView ArticlePubMedGoogle Scholar
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