This collection of 22 chapters is divided into two main groups: theoretical approaches and applications.
Theoretical approaches are covered in chapters 2 through 9. Each chapter addresses different aspects of theoretical molecular simulation. Chapters 2 and 3 deal with coarse-grained methods for protein structure and dynamics. The methods are discussed in the context of protein structure prediction and simulation. Various types of models are discussed, from knowledge-based models like SICHO and CABS, to physics-based models like united residue (UNRES), to simple network models. The discussion about coarse-grained methods continues in chapter 4, which includes a detailed description of a fully physics-based model and its potential to be used in the development of a coarse-grained force field. Chapters 5 and 6 continue the coarse-grained approach by addressing coarse-grained methods for protein-DNA complexes (chapter 5) and the problem of implicit treatment of a solvent (chapter 6).
The most interesting chapters in the theoretical part of the book are chapters 7 through 9. Chapter 7 deals with the problem of optimizing force field parameters. Chapter 8 covers the development of conformational sampling techniques, with a particular focus on enhancing the capabilities of the Monte Carlo method, a classical molecular dynamics (MD) method to search rough conformational spaces. The theory and application of generalized-ensemble sampling methods are presented in significant detail. Chapter 9 completes the overview of theoretical methods by presenting a method for constructing the entire trajectory from a set of short independent trajectories, somehow exploring and extending the old idea from Grubmüller. Such an approach is naturally parallel and can be implemented on distributed architectures running the message-passing interface (MPI) paradigm. However, the approach is not easily transferable to streaming processors like graphical processing units (GPU), which in fact are quite common in many modern high-performance clusters.
Chapters 10 to 15 are devoted to applications. In Chapter 10, the popular structure-based Gö-like model is discussed. Chapter 11 discusses the simulation of lipid membranes by all-atom MD techniques. Chapters 12 to 14 present similar MD simulation techniques, but for amyloid formations. The last chapter in this part of a book, chapter 15, discusses simulation of the human Hsp70 chaperone. The verification procedure for the calculated dynamic profiles is the most interesting and valuable part of the chapter.
Chapters 16 to 19 address bioinformatics by describing a structural database for intrinsically disordered proteins (chapter 16). This discovery contradicts the paradigm of a well-defined 3D structure as a requirement for protein function. Chapter 17 discusses the alignment between protein structures, and a new method based on local descriptors is introduced. Chapter 18 presents a method for the simulation of protein folding pathways by using statistics of protein structures (like microdomains in the diffusion model), chain-energy optimization, and the maximization of polar residues.
The final three chapters, 20 through 22, are devoted to applications of molecular quantum mechanics.
In summary, the purpose of the book is to provide an overview of modern methods and techniques for simulating and modeling biological systems. The area is broad, so several important topics are not covered at all, including enhanced sampling metadynamics, Bayesian free energy methods, and machine learning methods. Nevertheless, the book is an excellent introductory text that could be very useful for graduate students who are just starting research projects and need to establish a broader view of simulation methodologies.
