Preface

Proteins exert a variety of fascinating functions to participate in virtually all cellular processes. Underlying these highly controlled activities are the ordered conformational changes, which lead to molecular events that drive efficient and precise regulation and control of these processes. Although it has been widely acknowledged that the conformational dynamics of proteins contributes enormously in these molecular events and their biological function, it remains a major challenge to unambiguously discern their working mechanism both experimentally and computationally. The difficulties primarily arise from the complexity and heterogeneity of protein assemblies, the ruggedness of free energy landscapes, and the largely varied scales of temporal and spatial changes in different events. Complementary to experimental studies, computational simulation has become a powerful and unique tool to dissect the working mechanism of proteins and provide information otherwise inaccessible to other methods. In recent years, the persistent progress in methodologies (super-computational resources, multiscale modeling, enhanced sampling methods, etc.) has demonstrated a number of important applications in biological processes, e.g. molecular recognition, enzyme catalysis, molecular transport, protein folding and aggregation, and signal transduction. In this book, we present an extensive review of the recent theoretical and computational advances as well as their applications to key biological questions. In Chaps, 1, 2, and 3, different methods are described to generate the conformational ensembles during the folding and function of proteins. Chapter 1 describes the generalized-ensemble algorithms and their applications to the protein folding problem. Chapter 2 introduces another powerful tool in conformational sampling, the Markov State Models (MSMs), which can increase the simulation length to microseconds or even milliseconds. This chapter explains the general concepts of MSMs, the model construct procedure, and its application to the long time-scale molecular dynamics simulation of biological macromolecules. As the conformational dynamics can now be characterized by advanced experimental methods, Chap. 3 shows how these information can be combined with computational simulation to build the conformational ensembles.