Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database

Min Han,Yifan Song,Dengming Ming,Jiaqiang Qian

doi:10.1186/s12859-018-2206-2

Abstract

BackgroundIdentifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.ResultsIn this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. The method is based on a functional site and physicochemical interaction-annotated domain profile database, called fiDPD, which was built using protein domains found in the Protein Data Bank. This method was applied to 13 target proteins from the very recent Critical Assessment of Structure Prediction (CASP10/11), and our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for PFS prediction and an 80% recall in the prediction of the associated physicochemical interactions.ConclusionsOur results show that, in addition to the PFSs, the physical interactions at these sites are also conserved in the evolution of proteins. This work provides a valuable sequence-based tool for rational drug design and side-effect assessment. The method is freely available and can be accessed at http://202.119.249.49.

Highlights

Identifying protein functional sites (PFSs) and, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved
The 4 entries include an automated-match-domain profile built from 10 sequences from Arabidopsis thaliana, a second automated-matchdomain profile built from 4 sequences from Rattus norvegicus, an augmenter of liver regeneration domain profile built from 13 sequences from Rattus norvegicus, and a thiol-oxidase Erv2p domain profile built from 6 sequences from Saccharomyces cerevisiae
FiDPD gives new prediction for physicochemical interactions associated with the predicted PFSs

Summary

Introduction

Identifying protein functional sites (PFSs) and, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking. A few databases record detailed atomic interactions between proteins and ligands, facilitating PLI studies [44,45,46] These data provide new resources for the large-scale characterization of physicochemical interactions between proteins and their partners and have helped improve conventional docking simulation and pharmacology research. Several knowledge-based or ab initio methods have been developed for the prediction of PFSs; an accurate method for predicting the physicochemical interactions associated with PFSs is still lacking [47]

Methods

Results

Conclusion