Mechanisms Thermodynamics And Kinetics Of Ligand Binding Revealed From Molecular Simulations And Machine Learning

Ligand binding by macromolecules represents a core event of broad relevance to a range of systems, including catalytic systems alongside noncatalytic systems such as nucleic acid binding by transcription factors or extracellular ligand binding by proteins involved in signaling pathways. The scope of this primer is constrained to introduce only foundational models without significant discussion of more advanced topics such as allosteric or linkage effects. Linkage occurs when the binding of a ligand is influenced by the binding of another molecule of the same ligand (homotropic linkage), the binding of a different ligand (heterotropic linkage), physical variables such as temperature or pressure (physical linkage), or changes in macromolecular assembly state (polysteric linkage). Taking this into account, the foundational themes presented in this primer can be used to describe any macromolecule–ligand interaction either by direct use of the models and techniques described here or by applying them to develop more advanced models to explain additional complexities such as those allosteric or linkage effects just mentioned. The target audience of this primer is the senior undergraduate or junior graduate student who lacks a foundation in ligand-binding thermodynamics. As such, we have focused primarily on foundational thermodynamic treatments and presented only general discussions of relevant experimental designs. Readers of this primer will learn how to build a working understanding of common factors that promote energetic favorability for ligand binding; develop a functional toolbox to understand ligand binding from the perspective of collecting, plotting, and interpreting ligand-binding data; enhance proficiency in deriving thermodynamic mechanisms for ligand binding; and become comfortable in interpreting binding data reported in the literature and independently expanding knowledge beyond the scope introduced in this primer.

Machine learning methods such as neural networks, non-linear dimensionality reduction techniques, random forests and others meet in this research topic with biomolecular simulations. The authors of eight articles applied these methods to analyze simulation results, accelerate simulations or to make molecular mechanics force fields more accurate.

Free energy constitutes the most important thermodynamic quantity to understand how chemical species recognize each other, associate or react. Examples of problems in which knowledge of the underlying free energy behaviour is required, include conformational equilibria and molecular association, partitioning between immiscible liquids, receptor-drug interaction, protein-protein and protein-DNA association, and protein stability. This volume sets out to present a coherent and comprehensive account of the concepts that underlie different approaches devised for the determination of free energies. The reader will gain the necessary insight into the theoretical and computational foundations of the subject and will be presented with relevant applications from molecular-level modelling and simulations of chemical and biological systems. Both formally accurate and approximate methods are covered using both classical and quantum mechanical descriptions. A central theme of the book is that the wide variety of free energy calculation techniques available today can be understood as different implementations of a few basic principles. The book is aimed at a broad readership of graduate students and researchers having a background in chemistry, physics, engineering and physical biology.

This practical reference for medicinal and pharmaceutical chemists combines the theoretical background with modern methods as well as applications from recent lead finding and optimization projects. Divided into two parts on the thermodynamics and kinetics of drug-receptor interaction, the text provides the conceptual and methodological basis for characterizing binding mechanisms for drugs and other bioactive molecules. It covers all currently used methods, from experimental approaches, such as ITC or SPR, right up to the latest computational methods. Case studies of real-life lead or drug development projects are also included so readers can apply the methods learned to their own projects. Finally, the benefits of a thorough binding mode analysis for any drug development project are summarized in an outlook chapter written by the editors.

Proteins are the cell’s workers, their messengers and overseers. In these roles, proteins specifically bind small molecules, nucleic acid and other protein partners. Cellular systems are closely regulated and biologically significant changes in populations of particular protein complexes correspond to very small variations of their thermodynamics or kinetics of reaction. Interfering with the interactions of proteins is the dominant strategy in the development of new pharmaceuticals. Protein Ligand Interactions: Methods and Applications, Second Edition provides a complete introduction to common and emerging procedures for characterizing the interactions of individual proteins. From the initial discovery of natural substrates or potential drug leads, to the detailed quantitative understanding of the mechanism of interaction, all stages of the research process are covered with a focus on those techniques that are, or are anticipated to become, widely accessible and performable with mainstream commercial instrumentation. Written in the highly successful Methods in Molecular Biology series format, chapters contain introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and accessible, Protein Ligand Interactions: Methods and Applications, Second Edition serves as an ideal guide for researchers new to the field of biophysical characterization of protein interactions – whether they are beginning graduate students or experts in allied areas of molecular cell biology, microbiology, pharmacology, medicinal chemistry or structural biology.

The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models. 2) How to systematically gain insight from the resulting sea of data. MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states sets of rapidly interconverting conformations and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation.

Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.