Automatic protein structure prediction system enabling rapid and accurate model building for enzyme screening

Abstract

Protein structure prediction has great potential of understanding the function of proteins at the molecular level and designing novel protein functions. Here, we report rapid and accurate structure prediction system running in an automated manner. Since fold recognition of the target protein to be modeled is the starting point of the template-guided model building process, various approaches – such as profile analysis, threading, and SCOP fold classification – have been applied to generate the template library and to select the best template structure. After the best template was determined, fold consistency within the template candidates was considered using TM-score and SCOP database to select additional template structures among the template library. To generate a total of 100 decoy sets, MODELLER was used with the selected template structure. The predicted decoys were clustered with the RMSD deviation criterion of 3 Å to obtain centroids from each cluster. Finally, the selected centroids were subject to side-chain rearrangement using SCWRL module. Our fully automated structure prediction system was examined with sample test sets consisting of recently released 80 PDB chains. Judged by the TM-score (≥0.4), we concluded that 60 cases (75%) showed similar structures of statistical significance. This prediction system provides the users with simple and reliable models within hours of query submission, so that it is quite simply used for high throughput enzyme screening.