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Exploring the theories, methodologies and applications in simulations of enzymatic reactions, this book is a great resource for postgraduate students and researchers.
This practical reference explores computer modeling of enzyme reations--techniques that help chemists, biochemists and pharmaceutical researchers understand drug and enzyme action.
This book offers a fresh perspective on how computational tools can aid the chemical biology research community and drive new research.
This book describes the fundamental concepts, the latest developments and the outlook of the field of nanozymes (i.e., the catalytic nanomaterials with enzymatic characteristics). As one of today’s most exciting fields, nanozyme research lies at the interface of chemistry, biology, materials science and nanotechnology. Each of the book’s six chapters explores advances in nanozymes. Following an introduction to the rise of nanozymes research in the course of research on natural enzymes and artificial enzymes in Chapter 1, Chapters 2 through 5 discuss different nanomaterials used to mimic various natural enzymes, from carbon-based and metal-based nanomaterials to metal oxide-based nanomaterials and other nanomaterials. In each of these chapters, the nanomaterials’ enzyme mimetic activities, catalytic mechanisms and key applications are covered. In closing, Chapter 6 addresses the current challenges and outlines further directions for nanozymes. Presenting extensive information on nanozymes and supplemented with a wealth of color illustrations and tables, the book offers an ideal guide for readers from disparate areas, including analytical chemistry, materials science, nanoscience and nanotechnology, biomedical and clinical engineering, environmental science and engineering, green chemistry, and novel catalysis.
Computational methods are transforming the work of chemical and pharmaceutical laboratories. Increasingly faster and more exact simulation algorithms have made quantum chemistry a valuable tool in the search for active substances. Written by a team of leading international quantum chemists, this book is aimed at both beginners as well as experienced users of quantum chemical methods. All commonly used quantum chemical methods are treated here, including Density Functional Theory, quantum and molecular mechanical approaches. Numerous examples illustrate the use of these methods for dealing with problems in pharmaceutical practice, whether the study of inhibitor binding, identifying the surface load of active substances or deriving molecular descriptors using quantum chemical tools. For anyone striving to stay ahead in this rapidly evolving field.
A comprehensive overview of current empirical valence bond (EVB) theory and applications, one of the most powerful tools for studying chemical processes in the condensed phase and in enzymes. Discusses the application of EVB models to a broad range of molecular systems of chemical and biological interest, including reaction dynamics, design of artificial catalysts, and the study of complex biological problems Edited by a rising star in the field of computational enzymology Foreword by Nobel laureate Arieh Warshel, who first developed the EVB approach
“Multi-scale Quantum Models for Biocatalysis” explores various molecular modelling techniques and their applications in providing an understanding of the detailed mechanisms at play during biocatalysis in enzyme and ribozyme systems. These areas are reviewed by an international team of experts in theoretical, computational chemistry, and biophysics. This book presents detailed reviews concerning the development of various techniques, including ab initio molecular dynamics, density functional theory, combined QM/MM methods, solvation models, force field methods, and free-energy estimation techniques, as well as successful applications of multi-scale methods in the biocatalysis systems including several protein enzymes and ribozymes. This book is an excellent source of information for research professionals involved in computational chemistry and physics, material science, nanotechnology, rational drug design and molecular biology and for students exposed to these research areas.
This detailed book provides an overview of various classes of computational techniques, including machine learning techniques, commonly used for evaluating kinetic parameters of biological systems. Focusing on three distinct situations, the volume covers the prediction of the kinetics of enzymatic reactions, the prediction of the kinetics of protein-protein or protein-ligand interactions (binding rates, dissociation rates, binding affinities), and the prediction of relatively large set of kinetic rates of reactions usually found in quantitative models of large biological networks. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of expert implementation advice that leads to successful results. Authoritative and practical, Computational Methods for Estimating the Kinetic Parameters of Biological Systems will be of great interest for researchers working through the challenge of identifying the best type of algorithm and who would like to use or develop a computational method for the estimation of kinetic parameters.
One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.
This series presents critical reviews of the present position and future trends in modern chemical research. It contains short and concise reports on chemistry, each written by the world renowned experts. This series remains valid and useful after 5 or 10 years. More information as well as the electronic version of the whole content available at: springerlink.com.