I-TASSER server: new development for protein structure and function predictions.

Jianyi Yang,Yang Zhang

doi:10.1093/nar/gkv342

Abstract

The I-TASSER server (http://zhanglab.ccmb.med.umich.edu/I-TASSER) is an online resource for automated protein structure prediction and structure-based function annotation. In I-TASSER, structural templates are first recognized from the PDB using multiple threading alignment approaches. Full-length structure models are then constructed by iterative fragment assembly simulations. The functional insights are finally derived by matching the predicted structure models with known proteins in the function databases. Although the server has been widely used for various biological and biomedical investigations, numerous comments and suggestions have been reported from the user community. In this article, we summarize recent developments on the I-TASSER server, which were designed to address the requirements from the user community and to increase the accuracy of modeling predictions. Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations. We expect that these new developments will improve the quality of the I-TASSER server and further facilitate its use by the community for high-resolution structure and function prediction.

Highlights

With the progress in protein structure prediction, it has become routine for molecular and cytological researchers to seek automated server predictions for their proteins before conducting experimental investigations
We report on the recent developments made to the I-TASSER server, which have dramatically improved the quality of the I-TASSER modeling and the functionality of the server system
The I-TASSER server is an online facility designed for automated protein structure prediction and structure-based function annotation

Summary

INTRODUCTION

With the progress in protein structure prediction, it has become routine for molecular and cytological researchers to seek automated server predictions for their proteins before conducting experimental investigations. A live-bench based platform has been recently embarked to assess the ligand-binding site prediction [15], which demonstrated the considerable accuracy and usefulness of automated protein function annotations [16]. The major new developments include: (i) a new approach in estimating residue-level local quality of the structural models, which are critical to guide functional studies by the biologist users; (ii) an algorithm for B-factor prediction; (iii) methods for atomic-level structure refinement to improve the hydrogen-bonding networks and physical realism of the I-TASSER models; (iv) a consensus-based ligand-binding site prediction that combines structure and sequence profile comparisons by COACH [10]; (v) an integration of the new function library BioLiP [18] to increase the coverage of. Protein functional annotations; and (vi) development of the new message board system for facilitating discussion and communication with the user community

MATERIALS AND METHODS

Findings

CONCLUSIONS