Constructing Kinetic Network Models to Elucidate Mechanisms of Functional Conformational Changes of Enzymes and Their Recognition with Ligands.

Abstract

Enzymes are biological macromolecules that catalyze complex reactions in life. In order to perform their functions effectively and efficiently, enzymes undergo conformational changes between different functional states. Therefore, elucidating the dynamics between these states is essential to understand the molecular mechanisms of enzymes. Although experimental methods such as X-ray crystallography and cryoelectron microscopy can produce high-resolution structures, the detailed conformational dynamics of many enzymes still remain obscure. While molecular dynamics (MD) simulations are able to complement the experiments by providing structure-based dynamics at atomic resolution, it is usually difficult for them to reach the biologically relevant timescales (hundreds of microseconds or longer). Kinetic network models (KNMs), in particular Markov state models (MSMs), hold great promise to overcome this challenge because they can bridge the timescale gap between MD simulations and experimental observations. In this chapter, we review the procedure of constructing KNMs to elucidate the molecular mechanisms of enzymes. First, we will give a general introduction of MSMs, including the methods to construct and validate MSMs. Second, we will present the applications of KNMs to study two important enzymes: the human Argonaute protein and the RNA polymerase II. We conclude by discussing the future perspectives regarding the potential of KNMs to investigate the dynamics of enzymes' functional conformational changes.