Literature DB >> 25179222

Score_set: a CAPRI benchmark for scoring protein complexes.

Marc F Lensink1, Shoshana J Wodak.   

Abstract

Critical Assessment of PRedicted Interactions (CAPRI) has proven to be a catalyst for the development of docking algorithms. An essential step in docking is the scoring of predicted binding modes in order to identify stable complexes. In 2005, CAPRI introduced the scoring experiment, where upon completion of a prediction round, a larger set of models predicted by different groups and comprising both correct and incorrect binding modes, is made available to all participants for testing new scoring functions independently from docking calculations. Here we present an expanded benchmark data set for testing scoring functions, which comprises the consolidated ensemble of predicted complexes made available in the CAPRI scoring experiment since its inception. This consolidated scoring benchmark contains predicted complexes for 15 published CAPRI targets. These targets were subjected to 23 CAPRI assessments, due to existence of multiple binding modes for some targets. The benchmark contains more than 19,000 protein complexes. About 10% of the complexes represent docking predictions of acceptable quality or better, the remainder represent incorrect solutions (decoys). The benchmark set contains models predicted by 47 different predictor groups including web servers, which use different docking and scoring procedures, and is arguably as diverse as one may expect, representing the state of the art in protein docking. The data set is publicly available at the following URL: http://cb.iri.univ-lille1.fr/Users/lensink/Score_set.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  Capri; benchmark set; computational docking; protein-protein docking; protein-protein interaction; scoring function

Mesh:

Year:  2014        PMID: 25179222     DOI: 10.1002/prot.24678

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  24 in total

1.  Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins.

Authors:  J C Gaines; S Acebes; A Virrueta; M Butler; L Regan; C S O'Hern
Journal:  Proteins       Date:  2018-02-26

2.  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

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

Authors:  Petras J Kundrotas; Ivan Anishchenko; Taras Dauzhenka; Ian Kotthoff; Daniil Mnevets; Matthew M Copeland; Ilya A Vakser
Journal:  Protein Sci       Date:  2017-10-10       Impact factor: 6.725

4.  Dockground scoring benchmarks for protein docking.

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

5.  Energy-based graph convolutional networks for scoring protein docking models.

Authors:  Yue Cao; Yang Shen
Journal:  Proteins       Date:  2020-03-16

6.  Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View.

Authors:  Nícia Rosário-Ferreira; Catarina Marques-Pereira; Raquel P Gouveia; Joana Mourão; Irina S Moreira
Journal:  Methods Mol Biol       Date:  2021

7.  CLUB-MARTINI: Selecting Favourable Interactions amongst Available Candidates, a Coarse-Grained Simulation Approach to Scoring Docking Decoys.

Authors:  Qingzhen Hou; Marc F Lensink; Jaap Heringa; K Anton Feenstra
Journal:  PLoS One       Date:  2016-05-11       Impact factor: 3.240

8.  Finding correct protein-protein docking models using ProQDock.

Authors:  Sankar Basu; Björn Wallner
Journal:  Bioinformatics       Date:  2016-06-15       Impact factor: 6.937

9.  DockQ: A Quality Measure for Protein-Protein Docking Models.

Authors:  Sankar Basu; Björn Wallner
Journal:  PLoS One       Date:  2016-08-25       Impact factor: 3.240

10.  Protein Docking Model Evaluation by Graph Neural Networks.

Authors:  Xiao Wang; Sean T Flannery; Daisuke Kihara
Journal:  Front Mol Biosci       Date:  2021-05-25
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