SPEER-SERVER: a web server for prediction of protein specificity determining sites

Abstract

Sites that show specific conservation patterns within subsets of proteins in a protein family are likely to be involved in the development of functional specificity. These sites, generally termed specificity determining sites (SDS), might play a crucial role in binding to a specific substrate or proteins. Identification of SDS through experimental techniques is a slow, difficult and tedious job. Hence, it is very important to develop efficient computational methods that can more expediently identify SDS. Herein, we present Specificity prediction using amino acids' Properties, Entropy and Evolution Rate (SPEER)-SERVER, a web server that predicts SDS by analyzing quantitative measures of the conservation patterns of protein sites based on their physico-chemical properties and the heterogeneity of evolutionary changes between and within the protein subfamilies. This web server provides an improved representation of results, adds useful input and output options and integrates a wide range of analysis and data visualization tools when compared with the original standalone version of the SPEER algorithm. Extensive benchmarking finds that SPEER-SERVER exhibits sensitivity and precision performance that, on average, meets or exceeds that of other currently available methods. SPEER-SERVER is available at http://www.hpppi.iicb.res.in/ss/.

Figure 1.

Snapshots of the SPEER-SERVER web interface, displaying its various input options (Panels A and B) and output analysis tools, such as alignment display (Panel C), coevolutionary network display (Panel E), structure display (Panel F) and structure distance matrix display (Panel D).

Figure 2.

Comparison of prediction performance. ROC curves (A) for SDS prediction as performed by various SDS prediction programs. ROC curves (B) for the prediction of SDS based on manually curated input alignments and input alignments derived by the MAFFT (35) and PROBCONS (36) programs. Error rate and sensitivity values were calculated by averaging the equivalent error rate and sensitivity values using the ‘average-per-family’ approach.

Figure 3.

Comparison of performance on specificity site prediction. ROC curves for SPEER-SERVER, GroupSim (26) and MultiRELIEF (31) using manual, predetermined subgrouping and subgroupings computed using the automated methods, SECATOR (36) and SCI-PHY (37). Error rate and sensitivity values were calculated by averaging the equivalent error rate and sensitivity values using the ‘average-per-family’ approach.