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Refinement of Pairwise Potentials via Logistic Regression to Score Protein-Protein Interactions
  • Kiyoto Tanemura,
  • Jun Pei,
  • Kenneth Merz
Kiyoto Tanemura
Michigan State University
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Jun Pei
Michigan State University
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Kenneth Merz
Michigan State University
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Abstract

Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the ac-curate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared to conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation (RMSD) decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. All data and scripts used are available at: https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR .

Peer review status:ACCEPTED

24 Feb 2020Submitted to PROTEINS: Structure, Function, and Bioinformatics
26 Feb 2020Submission Checks Completed
26 Feb 2020Assigned to Editor
01 Mar 2020Reviewer(s) Assigned
06 May 2020Review(s) Completed, Editorial Evaluation Pending
07 May 2020Editorial Decision: Revise Minor
13 May 20201st Revision Received
18 May 2020Submission Checks Completed
18 May 2020Assigned to Editor
06 Jun 2020Reviewer(s) Assigned
09 Jun 2020Review(s) Completed, Editorial Evaluation Pending
14 Jun 2020Editorial Decision: Accept