This volume provides a collection of contemporary perspectives on using activity-based protein profiling (ABPP) for biological discoveries in protein science, microbiology, and immunology. A common theme throughout is the special utility of ABPP to interrogate protein function and small-molecule interactions on a global scale in native biological systems. Each chapter showcases distinct advantages of ABPP applied to diverse protein classes and biological systems. As such, the book offers readers valuable insights into the basic principles of ABPP technology and how to apply this approach to biological questions ranging from the study of post-translational modifications to targeting bacterial effectors in host-pathogen interactions.

From small beginnings in the early 1970s, the study of complement regulatory proteins has grown in the last decade to the point where it dominates the complement field. This growth has been fueled by the discovery of new regulators, the cloning of old and new regulators, the discovery that many of the regulators are structurally and evolutionarily related to each other and the development of recombinant forms for use in therapy. There are now more proteins known to be involved in controlling the complement system than there are components of the system and the list continues to grow. The time is ripe for a comprehensive review of our current knowledge of these intriguing proteins. This book does just that. The first few chapters discuss the "nuts-and-bolts" of the complement regulators, describing their structures, functional roles and modes of action. The roles of the complement regulators in vivo are then described, focusing on the consequences of deficiency, roles in the reproductive system, interactions with pathogens and exploitation for therapy. The interesting developments in defining the complement regulators expressed in other species are also discussed. The book is written as a monograph, albeit by two people. The text is as readable as possible without compromising on scientific accuracy and completeness. The conversational style very evident in some sections is deliberate! Placing all references in a single bibliography at the end of the text further improves readability. The reader will go to the book to discover a specific fact but be persuaded to read more and derive pleasure from the process. The authors' enthusiasm for the subject comes over strongly in the text, and this enthusiasm proves infectious. - Complement regulators--structure, functional roles and mode of action - Comprehensive reviews of each of the individual regulators - Roles of Complement regulators in vivo,in health and disease: - Consequences of deficiency - Roles in the reproductive system - Interactions with pathogens - Exploitation for therapy - Complement regulators in other species

The rational, structure-based approach has become standard in present-day drug design. As a consequence, the availability of high-resolution structures of target proteins is more often than not the basis for an entire drug development program. Protein structures suited for rational drug design are almost exclusively derived from crystallographic studies, and drug developers are relying heavily on the power of this method. Here, researchers from leading pharmaceutical companies present valuable first-hand information, much of it published for the first time. They discuss strategies to derive high-resolution structures for such important target protein classes as kinases or proteases, as well as selected examples of successful protein crystallographic studies. A special section on recent methodological developments, such as for high-throughput crystallography and microcrystallization, is also included. A valuable companion for crystallographers involved in protein structure determination as well as drug developers pursuing the structure-based approach for use in their daily work.

Fragment-based drug discovery is a rapidly evolving area of research, which has recently seen new applications in areas such as epigenetics, GPCRs and the identification of novel allosteric binding pockets. The first fragment-derived drug was recently approved for the treatment of melanoma. It is hoped that this approval is just the beginning of the many drugs yet to be discovered using this fascinating technique. This book is written from a Chemist's perspective and comprehensively assesses the impact of fragment-based drug discovery on a wide variety of areas of medicinal chemistry. It will prove to be an invaluable resource for medicinal chemists working in academia and industry, as well as anyone interested in novel drug discovery techniques.

The last decade has seen the confluence of several enabling technologies that have allowed protein crystallographic methods to live up to their true potential. Taken together, the numerous recent advances have made it possible to tackle difficult biological targets with a high probability of success: intact bacterial ribosomes have been structurally elucidated, as well as eukaryotic trans-membrane proteins like the potassium channel and GPCRs. It is now possible for medicinal chemists to have access to structural information on their latest small molecule candidates bound to the therapeutic target within days of compound synthesis, allowing structure guided ligand optimization to occur in "real time". Structure-Based Drug Discovery presents an array of methods used to generate crystal structures of biological macromolecules, how to leverage the structural information to design novel ligands anew, and how to iteratively optimize hits and convert them to leads. Written in the successful Methods in Molecular BiologyTM series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, Structure-Based Drug Discovery aims to provide scientists interested in adding SBDD to their arsenal of drug discovery methods with well-honed, up-to-date methodologies.

A panel of leading academic and pharmaceutical investigators takes stock of the remarkable work that has been accomplished to date with proteasome inhibitors in cancer, and examines emerging therapeutic possibilities. The topics range from a discussion of the chemistry and cell biology of the proteasome and the rationale for proteasome inhibitors in cancer to a review of current clinical trials underway. The discussion of rationales for testing proteasome inhibitors in cancer models covers the role of the proteasome in NF-kB activation, the combining of conventional chemotherapy and radiation with proteasome inhibition, notably PS-341, new proteasome methods of inhibiting viral maturation, and the role of protesome inhibition in the treatment of AIDS. The authors also document the development of bortezomib (VelcadeTM) in Phase I clinical trials and in a multicentered Phase II clinical trials in patients with relapsed and refractory myeloma.

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working