High precision prediction of functional sites in protein structures

Abstract

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta.

Figure 1. Performance comparison of FEATURE-SVM, original FEATURE (FEATURE-NB) and Pfam.

y-axis is recall value at approximately 99% precision. Vertical lines within bars indicate 95% confidence intervals. Pfam result for ZINC_PROTEASE was not available because the InterPro database, which was used to map site names, does not have a mapping record for this functional site. The functional sites are sorted by increasing recall value of FEATURE-SVM.