SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

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/ .

Keywords: Knowledge-based potential; Protein structure evaluation; Protein structure prediction; Protein structure quality assessment; Protein–protein evaluation; Protein–protein interaction.

Fig. 1

General scheme of the functioning of the SPServer. The web server is divided into three sections: input, to upload either single protein structures (for fold analyses) or binary complexes (for protein–protein interaction analyses); scoring, to score the quality of the single and complex structures; and output, to display the local profiles of single structures and heatmap of residue-residue scores in the interface of the input binary complexes

Fig. 2

Comparison of the residue pair scores for the native and wrong decoy structures of cysteine synthase calculated with PROSA and SPServer. a Residue-residue contact maps are shown at the top, with green/blue, pink/red and brown/yellow colors identifying native contacts that have been lost when comparing the native structure and the wrong decoy, where native contacts are lost. b Local profile of the difference between the scores per residue of the native structure and the wrong decoy (in red are shown the scores of PAIR and in blue the scores of Pair potential of PROSA). The regions highlighted in the contact maps are also shown on the X-axis above the residue number, showing a coincidence between high scores and the regions where the wrong decoy differs from the native structure

Fig. 3

Distribution of scores for proteins in CASP12 dataset. Scores of native (green), near native (blue) and wrong decoy structures (red) are shown with respect to the protein number of residues. The figure shows in four panels the distributions of scores obtained with PROSA (Z-score of Pair potential), DOPE and the Z-scores of PAIR (ZPAIR) and ES3DC (ZES3DC). Distribution of scores independent of protein length are shown in the left of each panel