Capturing protein multiscale thermal fluctuations

TL;DR

The paper introduces a multiscale flexibility-rigidity index (mFRI) method that improves protein thermal fluctuation predictions by capturing multiple length scales, outperforming traditional models like GNM in accuracy and efficiency.

Contribution

The paper presents a novel multiscale approach (mFRI) that enhances prediction accuracy and computational efficiency for protein thermal fluctuations across multiple length scales.

Findings

mFRI is about 20% more accurate than GNM in B-factor prediction.

mFRI accurately predicts fluctuations in multiscale macromolecules where GNM fails.

mFRI has linear computational complexity, O(N), unlike GNM's O(N^3).

Abstract

Existing elastic network models are typically parametrized at a given cutoff distance and often fail to properly predict the thermal fluctuation of many macromolecules that involve multiple characteristic length scales. We introduce a multiscale flexibility-rigidity index (mFRI) method to resolve this problem. The proposed mFRI utilizes two or three correlation kernels parametrized at different length scales to capture protein interactions at corresponding scales. It is about 20% more accurate than the Gaussian network model (GNM) in the B-factor prediction of a set of 364 proteins. Additionally, the present method is able to delivery accurate predictions for multiscale macromolecules that fail GNM. Finally, or a protein of $N$ residues, mFRI is of linear scaling (O(N)) in computational complexity, in contrast to the order of O(N^3) for GNM.