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SPServer: split-statistical potentials for the analysis of protein structures and protein–protein interactions Aguirre-Plans, Joaquim; Meseguer, Alberto; Molina-Fernandez, Ruben; Marín-López, Manuel A; Jumde, Gaurav; Casanova, Kevin; Bonet, Jaume; Fornes, Oriol; Fernandez-Fuentes, Narcis; Oliva, Baldo

Abstract

Background: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein–protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. Results: Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. Conclusions: While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. Server address: https://sbi.upf.edu/spserver/ .

Item Metadata

Title
SPServer: split-statistical potentials for the analysis of protein structures and protein–protein interactions
Creator
Aguirre-Plans, Joaquim; Meseguer, Alberto; Molina-Fernandez, Ruben; Marín-López, Manuel A; Jumde, Gaurav; Casanova, Kevin; Bonet, Jaume; Fornes, Oriol; Fernandez-Fuentes, Narcis; Oliva, Baldo
Contributor
University of British Columbia. Centre for Molecular Medicine and Therapeutics; St. Paul's Hospital (Vancouver, B.C.)
Publisher
BioMed Central
Date Issued
2021-01-06
Description
Background: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein–protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. Results: Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. Conclusions: While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. Server address: https://sbi.upf.edu/spserver/ .
Subject
Protein structure evaluation; Protein structure quality assessment; Protein structure prediction; Protein–protein interaction; Protein–protein evaluation; Knowledge-based potential
Genre
Article
Type
Text
Language
eng
Date Available
2021-01-07
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0395521
URI
http://hdl.handle.net/2429/77012
Affiliation
Medicine, Faculty of; Other UBC; Non UBC; Medical Genetics, Department of
Citation
BMC Bioinformatics. 2021 Jan 06;22(1):4
Publisher DOI
10.1186/s12859-020-03770-5
Peer Review Status
Reviewed
Scholarly Level
Faculty
Copyright Holder
The Author(s)
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Attribution 4.0 International (CC BY 4.0)