Literature DB >> 28891124

Dockground: A comprehensive data resource for modeling of protein complexes.

Petras J Kundrotas1, Ivan Anishchenko1, Taras Dauzhenka1, Ian Kotthoff1, Daniil Mnevets1, Matthew M Copeland1, Ilya A Vakser1,2.   

Abstract

Characterization of life processes at the molecular level requires structural details of protein interactions. The number of experimentally determined structures of protein-protein complexes accounts only for a fraction of known protein interactions. This gap in structural description of the interactome has to be bridged by modeling. An essential part of the development of structural modeling/docking techniques for protein interactions is databases of protein-protein complexes. They are necessary for studying protein interfaces, providing a knowledge base for docking algorithms, and developing intermolecular potentials, search procedures, and scoring functions. Development of protein-protein docking techniques requires thorough benchmarking of different parts of the docking protocols on carefully curated sets of protein-protein complexes. We present a comprehensive description of the Dockground resource (http://dockground.compbio.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X-ray docking decoy set 2. The resource offers a variety of interconnected datasets of protein-protein complexes and other data for the development and testing of different aspects of protein docking methodologies. Based on protein-protein complexes extracted from the PDB biounit files, Dockground offers sets of X-ray unbound, simulated unbound, model, and docking decoy structures. All datasets are freely available for download, as a whole or selecting specific structures, through a user-friendly interface on one integrated website.
© 2017 The Protein Society.

Keywords:  benchmark sets; protein recognition; protein-protein interactions; structure prediction

Mesh:

Substances:

Year:  2017        PMID: 28891124      PMCID: PMC5734278          DOI: 10.1002/pro.3295

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  57 in total

1.  Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations.

Authors:  Jeffrey J Gray; Stewart Moughon; Chu Wang; Ora Schueler-Furman; Brian Kuhlman; Carol A Rohl; David Baker
Journal:  J Mol Biol       Date:  2003-08-01       Impact factor: 5.469

2.  A new, structurally nonredundant, diverse data set of protein-protein interfaces and its implications.

Authors:  Ozlem Keskin; Chung-Jung Tsai; Haim Wolfson; Ruth Nussinov
Journal:  Protein Sci       Date:  2004-04       Impact factor: 6.725

3.  Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques.

Authors:  E Katchalski-Katzir; I Shariv; M Eisenstein; A A Friesem; C Aflalo; I A Vakser
Journal:  Proc Natl Acad Sci U S A       Date:  1992-03-15       Impact factor: 11.205

4.  Templates are available to model nearly all complexes of structurally characterized proteins.

Authors:  Petras J Kundrotas; Zhengwei Zhu; Joël Janin; Ilya A Vakser
Journal:  Proc Natl Acad Sci U S A       Date:  2012-05-29       Impact factor: 11.205

5.  Favorable scaffolds: proteins with different sequence, structure and function may associate in similar ways.

Authors:  Ozlem Keskin; Ruth Nussinov
Journal:  Protein Eng Des Sel       Date:  2005-01       Impact factor: 1.650

6.  Global and local structural similarity in protein-protein complexes: implications for template-based docking.

Authors:  Petras J Kundrotas; Ilya A Vakser
Journal:  Proteins       Date:  2013-10-17

Review 7.  Toward a "structural BLAST": using structural relationships to infer function.

Authors:  Fabian Dey; Qiangfeng Cliff Zhang; Donald Petrey; Barry Honig
Journal:  Protein Sci       Date:  2013-02-21       Impact factor: 6.725

8.  Score_set: a CAPRI benchmark for scoring protein complexes.

Authors:  Marc F Lensink; Shoshana J Wodak
Journal:  Proteins       Date:  2014-09-11

9.  I-TASSER server for protein 3D structure prediction.

Authors:  Yang Zhang
Journal:  BMC Bioinformatics       Date:  2008-01-23       Impact factor: 3.169

10.  Simulated unbound structures for benchmarking of protein docking in the DOCKGROUND resource.

Authors:  Tatsiana Kirys; Anatoly M Ruvinsky; Deepak Singla; Alexander V Tuzikov; Petras J Kundrotas; Ilya A Vakser
Journal:  BMC Bioinformatics       Date:  2015-07-31       Impact factor: 3.169

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  19 in total

1.  Gene ontology improves template selection in comparative protein docking.

Authors:  Anna Hadarovich; Ivan Anishchenko; Alexander V Tuzikov; Petras J Kundrotas; Ilya A Vakser
Journal:  Proteins       Date:  2018-12-27

2.  Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model.

Authors:  Ivan Anishchenko; Petras J Kundrotas; Ilya A Vakser
Journal:  Biophys J       Date:  2018-08-08       Impact factor: 4.033

3.  A knowledge-based scoring function to assess quaternary associations of proteins.

Authors:  Abhilesh S Dhawanjewar; Ankit A Roy; Mallur S Madhusudhan
Journal:  Bioinformatics       Date:  2020-06-01       Impact factor: 6.937

4.  Application of docking methodologies to modeled proteins.

Authors:  Amar Singh; Taras Dauzhenka; Petras J Kundrotas; Michael J E Sternberg; Ilya A Vakser
Journal:  Proteins       Date:  2020-03-20

5.  Text mining for modeling of protein complexes enhanced by machine learning.

Authors:  Varsha D Badal; Petras J Kundrotas; Ilya A Vakser
Journal:  Bioinformatics       Date:  2021-05-01       Impact factor: 6.937

6.  How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking.

Authors:  Devlina Chakravarty; G W McElfresh; Petras J Kundrotas; Ilya A Vakser
Journal:  Proteins       Date:  2020-02-07

Review 7.  Computational approaches to macromolecular interactions in the cell.

Authors:  Ilya A Vakser; Eric J Deeds
Journal:  Curr Opin Struct Biol       Date:  2019-04-15       Impact factor: 6.809

8.  ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction.

Authors:  Jérôme Tubiana; Dina Schneidman-Duhovny; Haim J Wolfson
Journal:  Nat Methods       Date:  2022-05-30       Impact factor: 28.547

9.  Dockground scoring benchmarks for protein docking.

Authors:  Ian Kotthoff; Petras J Kundrotas; Ilya A Vakser
Journal:  Proteins       Date:  2022-02-05

10.  The HDOCK server for integrated protein-protein docking.

Authors:  Yumeng Yan; Huanyu Tao; Jiahua He; Sheng-You Huang
Journal:  Nat Protoc       Date:  2020-04-08       Impact factor: 13.491

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