Generalized flexibility-rigidity index

TL;DR

This paper introduces generalized formulations of the flexibility-rigidity index (FRI) that improve protein fluctuation analysis and B-factor prediction, outperforming traditional models like GNM in accuracy and efficiency.

Contribution

The authors develop new rigidity and flexibility indices based on Gaussian surface structures, creating a more accurate and robust FRI method for protein analysis.

Findings

gFRI significantly outperforms GNM in B-factor prediction accuracy.

gFRI provides robust and efficient analysis of protein flexibility.

Demonstrated applications include molecular surface generation and flexibility visualization.

Abstract

Flexibility-rigidity index (FRI) has been developed as a robust, accurate and efficient method for macromolecular thermal fluctuation analysis and B-factor prediction. The performance of FRI depends on its formulations of rigidity index and flexibility index. In this work, we introduce alternative rigidity and flexibility formulations. The structure of the classic Gaussian surface is utilized to construct a new type of rigidity index, which leads to a new class of rigidity densities with the classic Gaussian surface as a special case. Additionally, we introduce a new type of flexibility index based on the domain indicator property of normalized rigidity density. These generalized FRI (gFRI) methods have been extensively validated by the B-factor predictions of 364 proteins. Significantly outperforming the classic Gaussian network model (GNM), gFRI is a new generation of methodologies for accurate, robust and efficient analysis of protein flexibility and fluctuation. Finally, gFRI based molecular surface generation and flexibility visualization are demonstrated.