Perturbation-based Markovian Transmission Model for Probing Allosteric Dynamics of Large Macromolecular Assembling: A Study of GroEL-GroES

Abstract

Large macromolecular assemblies are often important for biological processes in cells. Allosteric communications between different parts of these molecular machines play critical roles in cellular signaling. Although studies of the topology and fluctuation dynamics of coarse-grained residue networks can yield important insights, they do not provide characterization of the time-dependent dynamic behavior of these macromolecular assemblies. Here we develop a novel approach called Perturbation-based Markovian Transmission (PMT) model to study globally the dynamic responses of the macromolecular assemblies. By monitoring simultaneous responses of all residues (>8,000) across many (>6) decades of time spanning from the initial perturbation until reaching equilibrium using a Krylov subspace projection method, we show that this approach can yield rich information. With criteria based on quantitative measurements of relaxation half-time, flow amplitude change, and oscillation dynamics, this approach can identify pivot residues that are important for macromolecular movement, messenger residues that are key to signal mediating, and anchor residues important for binding interactions. Based on a detailed analysis of the GroEL-GroES chaperone system, we found that our predictions have an accuracy of 71–84% judged by independent experimental studies reported in the literature. This approach is general and can be applied to other large macromolecular machineries such as the virus capsid and ribosomal complex.

Author Summary

Biological processes in a cell often require complex molecular machineries with large macromolecular assemblies as components. An example is the chaperone system in the bacterium E. coli, which helps proteins to fold correctly. In these macromolecular machineries, signals are transmitted dynamically in order for biological functions to be carried out. Studying the dynamic process of signal transmission helps us to identify key elements of the macromolecular assemblies that are pivots for dynamic motions, communicators for interfacing with other molecules, and anchors that are key for signal transmission. In this study, we describe a novel computational method that can globally survey the dynamic responses of the macromolecular machinery to perturbation over the full time course by monitoring simultaneously all the elements at the amino acid residue level and at multiple time spans, from the initial perturbation until the system reaches equilibrium. We show that the key residues predicted by our computational method in the chaperone system of E. coli to a large extent are correct, as they often coincide with the ones identified by experimental studies. We also show that this computational method can make novel predictions about the importance of additional amino acid residues previously uncharacterized, which can be further tested in experimental studies. This approach can be applied to study other large macromolecular assemblies such as the virus capsid and ribosomal complex.