Structural discrimination analysis for constraint selection in protein modeling

Author(s)

Bottino, Guilherme F. ^{[1, 2]} ; Ferrari, Allan J. R. ^{[1, 2]} ; Gozzo, Fabio C. ^[1] ; Martinez, Leandro ^{[1, 2]}

Affiliation

^[1] Univ Estadual Campinas, Inst Chem, Campinas, SP - Brazil

^[2] Univ Estadual Campinas, Ctr Computat Engn & Sci, Campinas, SP - Brazil

• Document type: Journal article
• Source: Bioinformatics; v. 37, n. 21, p. 3766-3773, NOV 1 2021.
• Web of Science Citations: 0

Abstract

Motivation: Protein structure modeling can be improved by the use of distance constraints between amino acid residues, provided such data reflects-at least partially-the native tertiary structure of the target system. In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search toward consensus structures. Results: Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning-based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint datasets is a general concept with possible applications to other constraint types and modeling problems. Supplementary information: Supplementary data are available at Bioinformatics online. (AU)

• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 16/13195-2 - Modeling of protein structure and protein complexes using mass spectrometry data
• Grantee: Allan Jhonathan Ramos Ferrari
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 19/17007-4 - Protein modeling of enzymes and complexes associated with the degradation of cellulose
• Grantee: Allan Jhonathan Ramos Ferrari
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 10/16947-9 - Correlations between dynamics, structure and function in protein: computer simulations and algorithms
• Grantee: Leandro Martinez
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/14274-9 - Protein Structure Determination from Distance Constraints Derived from Chemical Cross-linking: Computational Methods and Applications
• Grantee: Leandro Martinez
• Support Opportunities: Regular Research Grants