Sense and simplicity in HADDOCK scoring: Lessons from CASP-CAPRI round 1.
Our information-driven docking approach HADDOCK is a consistent top predictor and scorer since the start of its participation in the CAPRI community-wide experiment. This sustained performance is due, in part, to its ability to integrate experimental data and/or bioinformatics information into the modelling process, and also to the overall robustness of the scoring function used to assess and rank the predictions. In the CASP-CAPRI Round 1 scoring experiment we successfully selected acceptable/medium quality models for 18/14 of the 25 targets - a top-ranking performance among all scorers. Considering that for only 20 targets acceptable models were generated by the community, our effective success rate reaches as high as 90% (18/20). This was achieved using the standard HADDOCK scoring function, which, thirteen years after its original publication, still consists of a simple linear combination of intermolecular van der Waals and Coulomb electrostatics energies and an empirically derived desolvation energy term. Despite its simplicity, this scoring function makes sense from a physico-chemical perspective, encoding key aspects of biomolecular recognition. In addition to its success in the scoring experiment, the HADDOCK server takes the first place in the server prediction category, with 16 successful predictions. Much like our scoring protocol, because of the limited time per target, the predictions relied mainly on either an ab initio center-of-mass and symmetry restrained protocol, or on a template-based approach whenever applicable. These results underline the success of our simple but sensible prediction and scoring scheme. Proteins 2017; 85:417-423. © 2016 Wiley Periodicals, Inc.
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Vangone
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Rodrigues
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Xue
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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van Zundert
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Geng
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Kurkcuoglu
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Nellen
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Narasimhan
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Karaca
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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van Dijk
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Melquiond
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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0000-0002-0167-3771
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Visscher
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Trellet
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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Kastritis
Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands.
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0000-0001-7369-1322
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Bonvin
D000465:Algorithms; D019985:Benchmarking; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D062105:Molecular Docking Simulation / Q000379:methods / Q000706:statistics & numerical data*; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry*; D012107:Research Design; D012984:Software; D055672:Static Electricity; D040681:Structural Homology, Protein; D013816:Thermodynamics
D011506:Proteins
biomolecular complexes; data-driven docking; desolvation energy; docking; electrostatics; ranking; scoring functions; van der Waals energy