This book is the third volume in this highly successful series. Since the first volume in 1989 and the second in 1993, many exciting developments have occurred in the development of simulation techniques and their application to key biological problems such as protein folding, protein structure prediction and structure-based design, and in how, by combining experimental and theoretical approaches, very large biological systems can be studied at the molecular level. This series attempts to capture that progress. Volume 3 includes contributions that highlight developments in methodology which enable longer and more realistic simulations (e.g. multiple time steps and variable reduction techniques), a study of force fields for proteins and new force field development, a novel approach to the description of molecular shape and the use of molecular shape descriptors, the study of condensed phase chemical reactions, the use of electrostatic techniques in the study of protonation, equilibria and flexible docking studies, structure refinement using experimental data (X-ray, NMR, neutron, infrared) and theoretical methods (solvation models, normal mode analysis, MD simulations, MC lattice dynamics, and knowledge-based potentials). There are several chapters that show progress in the development of methodologies for the study of folding processes, binding affinities, and the prediction of ligand-protein complexes. The chapters, contributed by experienced researchers, many of whom are leaders in their field of study, are organised to cover developments in: simulation methodology the treatment of electrostatics protein structure refinement the combined experimental and theoretical approaches to the study of very large biological systems applications and methodology involved in the study of protein folding applications and methodology associated with structure-based design.

The third volume in the series on Computer Simulation of Biomolecular Systems continues with the format introduced in the first volume [1] and elaborated in the second volume [2]. The primary emphasis is on the methodological aspects of simulations, although there are some chapters that present the results obtained for specific systems of biological interest. The focus of this volume has changed somewhat since there are several chapters devoted to structure-based ligand design, which had only a single chapter in the second volume. It seems useful to set the stage for this volume by quoting from my preface to Volume 2 [2]. "The long-range 'goal of molecular approaches to biology is to describe living systems in terms of chemistry and physics. Over the last fifty years great progress has been made in applying the equations representing the underlying physical laws to chemical problems involv ing the structures and reactions of small molecules. Corresponding studies of mesoscopic systems have been undertaken much more recently. Molecular dynamics simulations, which are the primary focus of this volume, represent the most important theoretical approach to macromolecules of biological interest." ...

Short-range molecular dynamics simulations of molecular systems are commonly parallelized by replicated-data methods, where each processor stores a copy of all atom positions. This enables computation of bonded 2-, 3-, and 4-body forces within the molecular topology to be partitioned among processors straightforwardly. A drawback to such methods is that the inter-processor communication scales as N, the number of atoms, independent of P, the number of processors. Thus, their parallel efficiency falls off rapidly when large numbers of processors are used. In this paper a new parallel method called force-decomposition for simulating macromolecular or small-molecule systems is presented. Its memory and communication costs scale as N/(square root)P, allowing larger problems to be run faster on greater numbers of processors. Like replicated-data techniques, and in contrast to spatial-decomposition approaches, the new method can be simply load-balanced and performs well even for irregular simulation geometries. The implementation of the algorithm in a prototypical macromolecular simulation code ParBond is also discussed. On a 1024-processor Intel Paragon, ParBond runs a standard benchmark simulation of solvated myoglobin with a parallel efficiency of 61% and at 40 times the speed of a vectorized version of CHARMM running on a single Cray Y-MP processor.

Molecular simulation is a widely used tool in biology, chemistry, physics and engineering. This book contains a collection of articles by leading researchers who are developing new methods for molecular modelling and simulation. Topics addressed here include: multiscale formulations for biomolecular modelling, such as quantum-classical methods and advanced solvation techniques; protein folding methods and schemes for sampling complex landscapes; membrane simulations; free energy calculation; and techniques for improving ergodicity. The book is meant to be useful for practitioners in the simulation community and for those new to molecular simulation who require a broad introduction to the state of the art.

This book presents computer simulations using molecular dynamics techniques in statistical physics, with a focus on macromolecular systems. The numerical methods are introduced in the form of computer algorithms and can be implemented in computers using any desired computer programming language, such as Fortran 90, C/C++, and others. The book also explains how some of these numerical methods and their algorithms can be implemented in the existing computer programming software of macromolecular systems, such as the CHARMM program. In addition, it examines a number of advanced concepts of computer simulation techniques used in statistical physics as well as biological and physical systems. Discussing the molecular dynamics approach in detail to enhance readers understanding of the use of this method in statistical physics problems, it also describes the equations of motion in various statistical ensembles to mimic real-world experimental conditions. Intended for graduate students and research scientists working in the field of theoretical and computational biophysics, physics and chemistry, the book can also be used by postgraduate students of other disciplines, such as applied mathematics, computer sciences, and bioinformatics. Further, offering insights into fundamental theory, it as a valuable resource for expert practitioners and programmers and those new to the field.

The long-range goal of molecular approaches to biology is to describe living systems in terms of chemistry and physics. Over the last fifty years great progress has been made in applying the equations representing the underlying physical laws to chemical problems involving the structures and reactions of small molecules. Corresponding studies of mesoscopic systems have been undertaken much more recently. Molecular dynamics simulations, which are the primary focus of this volume, represent the most important theoretical approach to macromolecules of biological interest. Now that molecular dynamics of macromolecules is a flourishing field, serious questions have to be asked concerning what more can be done with the methodology. What is the present and the future role of molecular dynamics in the development of our knowledge of macromolecules of biological interest? How does the methodology need to be improved to make it applicable to important problems? The present volume is concerned with providing some answers with its primary focus on the methodology and its recent developments.

This textbook originated from the course 'Simulation, Modeling, and Computations in Biophysics' that I have taught at the University of Chicago since 2011. The students typically came from a wide range of backgrounds, including biology, physics, chemistry, biochemistry, and mathematics, and the course was intentionally adapted for senior undergraduate students and graduate students. This is not a highly technical book dedicated to specialists. The objective is to provide a broad survey from the physical description of a complex molecular system at the most fundamental level, to the type of phenomenological models commonly used to represent the function of large biological macromolecular machines.The key conceptual elements serving as building blocks in the formulation of different levels of approximations are introduced along the way, aiming to clarify as much as possible how they are interrelated. The only assumption is a basic familiarity with simple mathematics (calculus and integrals, ordinary differential equations, matrix linear algebra, and Fourier-Laplace transforms).

In much of biology, the search for understanding the relation between structure and function is now taking place at the macromolecular level. Proteins, nucleic acids, and polysaccharides are macromolecule--polymers formed from families of simpler subunits. Because of their size and complexity, the polymers are capable of both inter- and intramolecular interactions. These interactions confer upon the polymers distinctive three-dimensional shapes. These tertiary configurations, in turn, determine the function of the macromolecule. Computers have become so inextricably involved in empirical studies of three-dimensional macromolecular structure that mathematical modeling, or theory, and experimental approaches are interrelated aspects of a single enterprise.

Computer simulations have been widely used in studying macromolecular systems due to a rapid increase in computer power. These simulations allow one to explore the structure, function and dynamics of biomolecules at atomistic level details and to predict unknown molecular properties. The accuracy of a computer simulation is mainly determined by the quality of the force field and the degree of sampling achieved during a simulation. Furthermore, the accuracy of calculated properties or results will depend on the methodology used to calculate these properties. Most force fields are developed by fitting the bonded and non-bonded interaction parameters to the quantum mechanically or experimentally obtained data. In contrast, our effort to develop a simple, classical, non-polarizable, force field is based on fitting parameters, especially the partial atomic charges, to reproduce Kirkwood-Buff integrals (KBIs) for solution mixtures. Kirkwood-Buff (KB) theory is a theory of solution mixtures that can be applied to solutions with any number of molecules, regardless of their size and complexity. This theory allows us to obtain the correct balance between the solute-solute and solute-solvent interactions. A Kirkwood-Buff derived force field for polyols in solution will be discussed. Fluctuation solution theory (FST) is an extension of KB theory which provides information regarding the local composition of solutions, or the deviation of local composition from bulk solution. The KBIs can be expressed in terms of particle number fluctuations and this allows us to calculate the KBIs without integrating the pair correlation function. A FST approach is used to calculate the partial molar volume and compressibility of proteins at infinite dilution without any subjective definitions of the protein volume and compressibility. These properties are solely determined using the solvent/water fluctuations in the presence and absence of the protein. Furthermore, residue-based contributions to these properties are also available and are calculated. The results are compared among different proteins and force fields to establish trends. Pressure perturbation is a powerful technique to study the hydration of macromolecules. Molecular dynamics techniques are used to identify the effect of pressure on the conformations of LacI and some variants of LacI. The lac repressor protein (LacI) is the regulatory unit of lac operon and it binds to the target site of the operon to repress the transition of the genes. The mutations studied here correspond to an experimentally known rheostat position, and we attempt to correlate the changes in activity for different mutants with the corresponding hydration changes.