Joaquim Aguirre-Plans1, Alberto Meseguer1, Ruben Molina-Fernandez1, Manuel Alejandro Marín-López1, Gaurav Jumde1, Kevin Casanova1, Jaume Bonet2, Oriol Fornes3, Narcis Fernandez-Fuentes4,5, Baldo Oliva6. 1. Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain. 2. Laboratory of Protein Design and Immuno-Enginneering, School of Engineering, Ecole Polytechnique Federale de Lausanne, 1015, Lausanne, Vaud, Switzerland. 3. Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada. 4. Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Vic 08500, Barcelona, Catalonia, Spain. 5. Institute of Biological, Environ-Mental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. 6. Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain. baldo.oliva@upf.edu.
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/ .
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/ .
Entities:
Keywords:
Knowledge-based potential; Protein structure evaluation; Protein structure prediction; Protein structure quality assessment; Protein–protein evaluation; Protein–protein interaction
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