Mode Localization in the Cooperative Dynamics of Protein Recognition

TL;DR

This paper investigates how local fluctuations in protein structures facilitate biological functions by analyzing the dynamics of specific proteins using a novel coarse-grained model, LE4PD, which accurately predicts dynamic mechanisms with minimal computational effort.

Contribution

The study introduces and applies the LE4PD model to analyze protein dynamics, demonstrating its accuracy and efficiency in capturing biologically relevant fluctuations.

Findings

LE4PD accurately predicts protein dynamic mechanisms.

Experimental NMR conformers can be used with minimal computational resources.

Local fluctuations are key to protein function and recognition.

Abstract

The biological function of proteins is encoded in their structure and expressed through the mediation of their dynamics. Local fluctuations are known to initiate biologically relevant pathways as they cooperatively enhance the dynamics in specific regions in the protein. Those biologically active regions provide energetically-comparable conformational states that can be trapped by a reacting partner. We analyze this mechanism as we calculate the dynamics of monomeric and dimerized HIV protease, and free Insulin Growth Factor II Receptor (IGF2R) domain 11 and its IGF2R:IGF2 complex. We adopt a newly developed coarse-grained model, the Langevin Equation for Protein Dynamics (LE4PD), which predicts dynamical relevant mechanisms with high accuracy. Both simulation-derived and experimental NMR conformers are the input structural ensembles for the LE4PD. The use of the experimental NMR conformers requires minimal computational resources.