Computational Approaches To Molecular Recognition And Bioorganic Mechanisms

This book covers applications of computational techniques to biological problems. These techniques are based by an ever-growing number of researchers with different scientific backgrounds - biologists, chemists, and physicists. The rapid development of molecular biology in recent years has been mirrored by the rapid development of computer hardware and software. This has resulted in the development of sophisticated computational techniques and a wide range of computer simulations involving such methods. Among the areas where progress has been profound is in the modeling of DNA structure and function, the understanding at a molecular level of the role of solvents in biological phenomena, the calculation of the properties of molecular associations in aqueous solutions, computationally assisted drug design, the prediction of protein structure, and protein - DNA recognition, to mention just a few examples. This volume comprises a balanced blend of contributions covering such topics. They reveal the details of computational approaches designed for biomoleucles and provide extensive illustrations of current applications of modern techniques. A broad group of readers ranging from beginning graduate students to molecular biology professions should be able to find useful contributions in this selection of reviews.

Molecular recognition is a very interesting problem, and foremost, a current challenge for biophysical chemistry. Having reliable predictions on the specific recognition between molecules is highly priority as it will provide an insight of fundamental problems and will raise relevant technological applications. The dissertation presented here is centered on a quantitative analysis of molecular recognition in solution for host-guest, protein-ligand binding and catalysis. The statistical mechanics framework used to describe the state-of-the-art for receptor-ligand binding is an inflection point for the developing of new improved and methods. In fact, a highly performanced and accurate model was obtained for the analysis of host-guest binding. Finally, the presented models were used as a reliable predictive tools for discovering new chemical entities for enhance catalysis in solution.

A breakthrough guide employing knowledge that unites cheminformatics and bioinformatics as innovation for the future Bridging the gap between cheminformatics and bioinformatics for the first time, Computational Approaches in Cheminformatics and Bioinformatics provides insight on how to blend these two sciences for progressive research benefits. It describes the development and evolution of these fields, how chemical information may be used for biological relations and vice versa, the implications of these new connections, and foreseeable developments in the future. Using algorithms and domains as workflow tools, this revolutionary text drives bioinformaticians to consider chemical structure, and similarly, encourages cheminformaticians to consider large biological systems such as protein targets and networks. Computational Approaches in Cheminformatics and Bioinformatics covers: Data sources available for modelling and prediction purposes Developments of conventional Quantitative Structure-Activity Relationships (QSAR) Computational tools for manipulating chemical and biological data Novel ways of probing the interactions between small molecules and proteins Also including insight from public (NIH), academic, and industrial sources (Novartis, Pfizer), this book offers expert knowledge to aid scientists through industry and academic study. The invaluable applications for drug discovery, cellular and molecular biology, enzymology, and metabolism make Computational Approaches in Cheminformatics and Bioinformatics the essential guidebook for evolving drug discovery research and alleviating the issue of chemical control and manipulation of various systems.

Molecular recognition is fundamentally important in biological chemistry. Nowadays, with the rapid development of computational technology and algorithm, molecular modeling has become a powerful tool in studying molecular recognition, such as exploring molecular interactions and understanding biological dynamic processes, making significant contributions to modern biology and drug discovery. The state-of-the-art techniques of computational chemistry and molecular modeling can be applied to study a wide range of chemical and biological systems of interest. This enables us to study structural details at the atomic level and obtain chemical/biological information which is not available by experimental measurements. This dissertation project focused on modeling the recognition mechanisms of biomolecules and their conjugated ligands. Multiple computational techniques, such as molecular dynamics simulation, enhanced sampling methods and free energy calculation were applied. The model systems included signaling domains (BRCT domain), kinase (p38 kinase), enzyme system (TRPS) and small biomolecular system (cyclodextrin). The details of protein-ligand interactions, including both enthalpic and entropic contribution within protein domain-phosphopeptide systems were investigated, based on which new inhibitors were proposed. Several enhanced sampling methods like accelerated molecular dynamics simulation, pathway search guided by internal motions (PSIM) and umbrella sampling, were applied to explore the dissociation pathway of kinase-ligand systems and the motions of kinase during dissociation process were studied both thermodynamically and kinetically, protein conformational rearrangement was found to differentiate slow and fast unbinding inhibitors, casting light on high efficacy inhibitor design. Furthermore, using full structural molecular modeling, we explored how the position of a single proton can change the overall protein dynamics and further activate or inactivate enzyme catalysis, elucidating the catalytic mechanism of TRPS. In addition, we performed systematically evaluation to the performance of umbrella sampling, investigated the influence of subtle changes in the dissociation pathways and conformational sampling methods that provide the initial conformations, paving way for future improvement of umbrella sampling. This project studies the details of receptor-ligand interaction and provides a more complete picture of molecular recognition.

The rapid development of efficient computational tools has allowed researchers to tackle biological problems and to predict, analyse and monitor, at an atomic level, molecular recognition processes. This book offers a fresh perspective on how computational tools can aid the chemical biology research community and drive new research. Chapters from internationally renowned leaders in the field introduce concepts and discuss the impact of technological advances in computer hardware and software in explaining and predicting phenomena involving biomolecules, from small molecules to macromolecular systems. Important topics from the understanding of biomolecules to the modification of their functions are addressed, as well as examples of the application of tools in drug discovery, glycobiology, protein design and molecular recognition. Not only are the cutting-the-edge methods addressed, but also their limitations and possible future development. For anyone wishing to learn how computational chemistry and molecular modelling can provide information not easily accessible through other experimental methods, this book will be a valuable resource. It will be of interest to postgraduates and researchers in the biological and chemical sciences, medicinal and pharmaceutical chemistry, and theoretical chemistry.

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.

Computational structural biology has made tremendous progress over the last two decades, and this book provides a recent and broad overview of such computational methods in structural biology. It covers the impact of computational structural biology on protein structure prediction methods, macromolecular function and protein design, and key methods in drug discovery. It also addresses the computational challenges of experimental approaches in structural biology. In addition to reviewing the current state of computational structural biology, each chapter ends with a brief, visionary discussion on the future outlook, whereby the main challenges for the coming years are elucidated. Written by an international panel of expert contributors, this book can serve as a reference manual for students and practitioners alike.

This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.