CGRAP: A Web Server for Coarse-Grained Rigidity Analysis of Proteins

Alistair Turcan,Lorraine Wong,Lauren Johnson,Filip Jagodzinski,Anna Zivkovic,Dylan Thompson

doi:10.3390/sym13122401

Abstract

Elucidating protein rigidity offers insights about protein conformational changes. An understanding of protein motion can help speed drug development, and provide general insights into the dynamic behaviors of biomolecules. Existing rigidity analysis techniques employ fine-grained, all-atom modeling, which has a costly run-time, particularly for proteins made up of more than 500 residues. In this work, we introduce coarse-grained rigidity analysis, and showcase that it provides flexibility information about a protein that is similar in accuracy to an all-atom modeling approach. We assess the accuracy of the coarse-grained method relative to an all-atom approach via a comparison metric that reasons about the largest rigid clusters of the two methods. The apparent symmetry between the all-atom and coarse-grained methods yields very similar results, but the coarse-grained method routinely exhibits 40% reduced run-times. The CGRAP web server outputs rigid cluster information, and provides data visualization capabilities, including a interactive protein visualizer.

Highlights

Observing how proteins flex and bend is not possible because the timescales involved are microseconds for conformational transitions and down to nanoseconds for sidechain fluctuations [1]
Because the molecular framework of the KINARI approach is fine-grained with a residue made up of many bodies, but in CGRAP, all atoms of a residue are represented by a single body, the resulting mechanical models of both approaches differ greatly (Figure 3)
To showcase the utility of CGRAP relative to KINARI’s all-atom approach, we analyzed the rigidity of 9046 proteins using both methods

Summary

Introduction

Observing how proteins flex and bend is not possible because the timescales involved are microseconds for conformational transitions and down to nanoseconds for sidechain fluctuations [1]. Combinatorial pebble-game approaches for identifying the rigid and flexible regions of proteins have been developed [9]. Those approaches, albeit fast and efficient, assumed an all-atom modeling approach. There are currently upwards of 40,000 proteins in the PDB with 1000 or more residues, and it is not possible to analyze their rigidity in near real-time using existing all-atom based approaches. Computational-based approaches for identifying the rigid regions of proteins dates back to the late 1990s. Efficient pebble game algorithms permit analyzing the body-bar-hinge-framework [15,16], and permit inferring the flexible and rigid regions of biomolecues

Methods

Results

Discussion

Conclusion