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MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations

Yong Jung, Cunliang Geng, Alexandre M. J. J. Bonvin, Li C. Xue, Vasant G. Honavar
doi: https://doi.org/10.1101/2021.10.06.463442
Yong Jung
1Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
2Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
5Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
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Cunliang Geng
8Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
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Alexandre M. J. J. Bonvin
8Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
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Li C. Xue
8Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
9Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525 GA Nijmegen, the Netherlands
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  • For correspondence: vhonavar@psu.edu Li.Xue@radboudumc.nl
Vasant G. Honavar
1Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
2Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
3Center for Big Data Analytics and Discovery Informatics, Pennsylvania State University, University Park, PA 16823, USA
4Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
5Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
6Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, PA 16802, USA
7College of Information Sciences & Technology, Pennsylvania State University, University Park, PA 16802, USA
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  • For correspondence: vhonavar@psu.edu Li.Xue@radboudumc.nl
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Abstract

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and timeconsuming experimental approaches for determining 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking - the so-called scoring problem - still has considerable room for improvement.

We present here MetaScore, a new machine-learning based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using a rich set of features extracted from the respective protein-protein interfaces. These include physico-chemical properties, energy terms, interaction propensity-based features, geometric properties, interface topology features, evolutionary conservation and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging of the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of nine traditional SFs included in this work in terms of success rate and hit rate evaluated over the top 10 predicted conformations; (ii) An ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by judiciously leveraging machine-learning.

Competing Interest Statement

The authors have declared no competing interest.

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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 October 09, 2021.
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MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations
Yong Jung, Cunliang Geng, Alexandre M. J. J. Bonvin, Li C. Xue, Vasant G. Honavar
bioRxiv 2021.10.06.463442; doi: https://doi.org/10.1101/2021.10.06.463442
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MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations
Yong Jung, Cunliang Geng, Alexandre M. J. J. Bonvin, Li C. Xue, Vasant G. Honavar
bioRxiv 2021.10.06.463442; doi: https://doi.org/10.1101/2021.10.06.463442

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