The MULTICOM toolbox for protein structure prediction

Abstract

Background: As genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.

Results: To meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.

Conclusions: These tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Figure 1

The organization of the MULTICOM toolbox.

Figure 2

The plot of sensitivity and specificity (y axis) against different probability thresholds of classifying residues as disordered residues on CASP8 targets.

Figure 3

The plot of sensitivity and specificity (y axis) against different probability thresholds of classifying residues as disordered residues on CASP9 targets.

Figure 4

Superimpositions of predicted models (blue) and native structures (orange) of four CASP9 targets. (A) T0520, TM-Score = 85, (B) T0527, TM-Score = 74, (C) T0634, TM-Score = 88, (D) T0641, TM-Score = 91.

Figure 5

The MULTICOM toolbox web site.