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An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

View ORCID ProfileP.I. Koukos, View ORCID ProfileJ. Roel-Touris, F. Ambrosetti, View ORCID ProfileC. Geng, View ORCID ProfileJ. Schaarschmidt, View ORCID ProfileM.E. Trellet, View ORCID ProfileA.S.J. Melquiond, View ORCID ProfileL.C. Xue, View ORCID ProfileR.V. Honorato, View ORCID ProfileI. Moreira, View ORCID ProfileZ. Kurkcuoglu, View ORCID ProfileA. Vangone, View ORCID ProfileA.M.J.J. Bonvin
doi: https://doi.org/10.1101/718122
P.I. Koukos
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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J. Roel-Touris
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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F. Ambrosetti
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
5Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00184, Rome, Italy
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C. Geng
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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J. Schaarschmidt
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
2Multiscale Materials Modelling and Virtual Design, Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), 76 021, Karlsruhe, Germany
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M.E. Trellet
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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A.S.J. Melquiond
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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L.C. Xue
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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R.V. Honorato
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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I. Moreira
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
3CNC - Center for Neuroscience and Cell Biology; Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517; Coimbra, Portugal
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Z. Kurkcuoglu
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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A. Vangone
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
4PharmaResearch and Early Development, Large Molecule Research, Roche Innovation Center Munich, No nnenwald 2. 82377 Penzberg, Germany
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A.M.J.J. Bonvin
1Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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  • For correspondence: a.m.j.j.bonvin@uu.nl
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ABSTRACT

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modelling process, and to the robustness of its scoring function. We participated in CAPRI both as server and as manual predictors.

In CAPRI rounds 38-45, we have used various strategies depending on the information at hand. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement / CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall in the current rounds, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable models) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces.

An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 29, 2019.
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An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45
P.I. Koukos, J. Roel-Touris, F. Ambrosetti, C. Geng, J. Schaarschmidt, M.E. Trellet, A.S.J. Melquiond, L.C. Xue, R.V. Honorato, I. Moreira, Z. Kurkcuoglu, A. Vangone, A.M.J.J. Bonvin
bioRxiv 718122; doi: https://doi.org/10.1101/718122
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An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45
P.I. Koukos, J. Roel-Touris, F. Ambrosetti, C. Geng, J. Schaarschmidt, M.E. Trellet, A.S.J. Melquiond, L.C. Xue, R.V. Honorato, I. Moreira, Z. Kurkcuoglu, A. Vangone, A.M.J.J. Bonvin
bioRxiv 718122; doi: https://doi.org/10.1101/718122

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