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.

Innovative and forward-looking, this volume focuses on recent achievements in this rapidly progressing field and looks at future potential for development. The first part provides a basic understanding of the factors governing protein-ligand interactions, followed by a comparison of key experimental methods (calorimetry, surface plasmon resonance, NMR) used in generating interaction data. The second half of the book is devoted to insilico methods of modeling and predicting molecular recognition and binding, ranging from first principles-based to approximate ones. Here, as elsewhere in the book, emphasis is placed on novel approaches and recent improvements to established methods. The final part looks at unresolved challenges, and the strategies to address them. With the content relevant for all drug classes and therapeutic fields, this is an inspiring and often-consulted guide to the complexity of protein-ligand interaction modeling and analysis for both novices and experts.

With contributions by numerous experts

Gabriel Waksman Institute of Structural Molecular Biology, Birkbeck and University College London, Malet Street, London WC1E 7HX, United Kingdom Address for correspondence: Professor Gabriel Waksman Institute of Structural Molecular Biology Birkbeck and University College London Malet Street London WC1E 7H United Kingdom Email: g. waksman@bbk. ac. uk and g. waksman@ucl. ac. uk Phone: (+44) (0) 207 631 6833 Fax: (+44) (0) 207 631 6833 URL: http://people. cryst. bbk. ac. uk/?ubcg54a Gabriel Waksman is Professor of Structural Molecular Biology at the Institute of Structural Molecular Biology at UCL/Birkbeck, of which he is also the director. Before joining the faculty of UCL and Birkbeck, he was the Roy and Diana Vagelos Professor of Biochemistry and Molecular Biophysics at the Washington University School of Medicine in St Louis (USA). The rapidly evolving ?eld of protein science has now come to realize the ubiquity and importance of protein–protein interactions. It had been known for some time that proteins may interact with each other to form functional complexes, but it was thought to be the property of only a handful of key proteins. However, with the advent of hi- throughput proteomics to monitor protein–protein interactions at an organism level, we can now safely state that protein–protein interactions are the norm and not the exception.

This volume provides methods on microcalorimetry approaches to investigate complex biological molecular systems. Chapters guide readers through Differential Scanning Calorimetry (DSC), Isothermal Titration Calorimetry (ITC), and advanced data processing. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Microcalorimetry of Biological Molecules: Methods and Protocols aims to ensure successful results in the further study of this vital field.

The cellular function of proteins, and their targeting by drug applications, are both governed by biomolecular thermodynamics and kinetics. In order to make meaningful and efficient predictions of these mechanisms, molecular simulations must be able to estimate the binding affinity and rates of association and dissociation of a protein-ligand complex, or the populations and rates of exchange between distinct conformational states (i.e. folding and unfolding, binding and unbinding). The above studies are typically done using different, but complementary approaches. Alchemical methods, including free energy perturbation (FEP) and thermodynamic integration (TI), have become the dominant method for computing high-quality estimates of protein-ligand binding free energies. In particular, the widely-used approach of relative binding free energy calculation can deliver accuracies within 1 kcal mol−1. However, detailed physical pathways and kinetics are missing from these calculations. In principle, all-atom molecular dynamics (MD) simulation, with the help of Markov State Models (MSMs), can be used to obtain this information, yet finite sampling error still limits MSM approaches from making accurate predictions for very slow unfolding or unbinding processes. To overcome these issues, a new approach called multiensemble Markov models (MEMMs) have been developed, in which sampling from biased thermodynamic ensembles can be used to infer states populations and transition rates in unbiased ensembles. In this dissertation, two distinct biophysical problems are investigated. In the first part, we apply expanded ensemble (EE) methods to accurately predict relative binding free energies for a series of protein-ligand systems. Moreover, we propose a simple optimization scheme for choosing alchemical intermediates in free energy simulations. In the second part, we employ MEMMs to estimate the free energies and kinetics of protein folding and ligand binding, to achieve greatly improved predictions. Finally, we combine the above EE method and a maximum-caliber algorithm to study how sequence mutations perturb protein stability and folding kinetics. In summary, this work comprises a wide range of current methodology in biophysical simulation, complementing and improving upon existing approaches.

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.