Download Multiscale Approaches To Protein Modeling full books in PDF, epub, and Kindle. Read online Multiscale Approaches To Protein Modeling ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
The book gives a comprehensive review of the most advanced multiscale methods for protein structure prediction, computational studies of protein dynamics, folding mechanisms and macromolecular interactions. It approaches span a wide range of the levels of coarse-grained representations, various sampling techniques and variety of applications to biomedical and biophysical problems. This book is intended to be used as a reference book for those who are just beginning their adventure with biomacromolecular modeling but also as a valuable source of detailed information for those who are already experts in the field of biomacromolecular modeling and in related areas of computational biology or biophysics.
Book Synopsis Multiscale Approaches to Protein Modeling by : Andrzej Kolinski
Download or read book Multiscale Approaches to Protein Modeling written by Andrzej Kolinski and published by Springer Science & Business Media. This book was released on 2010-10-13 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book gives a comprehensive review of the most advanced multiscale methods for protein structure prediction, computational studies of protein dynamics, folding mechanisms and macromolecular interactions. It approaches span a wide range of the levels of coarse-grained representations, various sampling techniques and variety of applications to biomedical and biophysical problems. This book is intended to be used as a reference book for those who are just beginning their adventure with biomacromolecular modeling but also as a valuable source of detailed information for those who are already experts in the field of biomacromolecular modeling and in related areas of computational biology or biophysics.
Book Synopsis Multiscale Approaches to Protein Modeling by :
Download or read book Multiscale Approaches to Protein Modeling written by and published by . This book was released on 2011-07-11 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mikē Theodōrakē istories written by and published by . This book was released on 2005 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Book Synopsis Multiscale Modeling From Macromolecules to Cell: Opportunities and Challenges of Biomolecular Simulations by : Valentina Tozzini
Download or read book Multiscale Modeling From Macromolecules to Cell: Opportunities and Challenges of Biomolecular Simulations written by Valentina Tozzini and published by Frontiers Media SA. This book was released on 2020-10-27 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
The Latest Developments on the Role of Dynamics in Protein FunctionsComputational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods presents modern biomolecular computational techniques that address protein flexibility/dynamics at all levels of theory. An international contingent of leading researchers in chemistry, physics, an
Book Synopsis Computational Approaches to Protein Dynamics by : Monika Fuxreiter
Download or read book Computational Approaches to Protein Dynamics written by Monika Fuxreiter and published by CRC Press. This book was released on 2014-12-24 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Latest Developments on the Role of Dynamics in Protein FunctionsComputational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods presents modern biomolecular computational techniques that address protein flexibility/dynamics at all levels of theory. An international contingent of leading researchers in chemistry, physics, an
Multiscale Modeling for Process Safety Applications is a new reference demonstrating the implementation of multiscale modeling techniques on process safety applications. It is a valuable resource for readers interested in theoretical simulations and/or computer simulations of hazardous scenarios. As multi-scale modeling is a computational technique for solving problems involving multiple scales, such as how a flammable vapor cloud might behave if ignited, this book provides information on the fundamental topics of toxic, fire, and air explosion modeling, as well as modeling jet and pool fires using computational fluid dynamics. The book goes on to cover nanomaterial toxicity, QPSR analysis on relation of chemical structure to flash point, molecular structure and burning velocity, first principle studies of reactive chemicals, water and air reactive chemicals, and dust explosions. Chemical and process safety professionals, as well as faculty and graduate researchers, will benefit from the detailed coverage provided in this book. Provides the only comprehensive source addressing the use of multiscale modeling in the context of process safety Bridges multiscale modeling with process safety, enabling the reader to understand mapping between problem detail and effective usage of resources Presents an overall picture of addressing safety problems in all levels of modeling and the latest approaches to each in the field Features worked out examples, case studies, and a question bank to aid understanding and involvement for the reader
Book Synopsis Multiscale Modeling for Process Safety Applications by : Arnab Chakrabarty
Download or read book Multiscale Modeling for Process Safety Applications written by Arnab Chakrabarty and published by Butterworth-Heinemann. This book was released on 2015-11-29 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiscale Modeling for Process Safety Applications is a new reference demonstrating the implementation of multiscale modeling techniques on process safety applications. It is a valuable resource for readers interested in theoretical simulations and/or computer simulations of hazardous scenarios. As multi-scale modeling is a computational technique for solving problems involving multiple scales, such as how a flammable vapor cloud might behave if ignited, this book provides information on the fundamental topics of toxic, fire, and air explosion modeling, as well as modeling jet and pool fires using computational fluid dynamics. The book goes on to cover nanomaterial toxicity, QPSR analysis on relation of chemical structure to flash point, molecular structure and burning velocity, first principle studies of reactive chemicals, water and air reactive chemicals, and dust explosions. Chemical and process safety professionals, as well as faculty and graduate researchers, will benefit from the detailed coverage provided in this book. Provides the only comprehensive source addressing the use of multiscale modeling in the context of process safety Bridges multiscale modeling with process safety, enabling the reader to understand mapping between problem detail and effective usage of resources Presents an overall picture of addressing safety problems in all levels of modeling and the latest approaches to each in the field Features worked out examples, case studies, and a question bank to aid understanding and involvement for the reader
Simulating protein complexes on large time and length scales is often intractable at the atomistic resolution. To address this challenge, we have developed new approaches to integrate coarse-grained (CG), mixed-resolution (referred to as AACG throughout this dissertation), and all-atom (AA) modeling for different stages in a single molecular simulation. First, we developed a top-down multiscale modeling approach -- a new approach, which combines CG, AACG, and AA modeling -- to simulate peptide self-assembly from monomers. We simulated the initial encounter stage with the CG model, while the further assembly and reorganization stages are simulated with the AACG and AA models. Further, a theory was developed to estimate the optimal simulation length for each stage. Finally, our approach and theory have been successfully validated with three amyloid peptides. which highlight the synergy from models at multiple resolutions. This approach improves the efficiency of simulating of peptide assembly process. Furthermore, it serves as proof of concept that applying flexible resolution during the simulation, to adapt to efficiency or accuracy. Second, we gained proof of principle from simulating five heterodimeric models of two G protein-coupled receptors (GPCRs) in the lipid-bilayer membrane on the ns-to-[mu]s timescales. In these simulations of different resolution levels, we observed consistent structural stability, while the AACG and CG models show two- and four-times faster protein diffusion than the AA models, in addition to 4- and 400-fold speedup in the simulation performance. Our findings enable synergy from the combination of AA, AACG, and CG models, which lay the foundation to combine these models in one single simulation. It is also feasible to alternate among different models to represent an efficient solution to investigate complex biophysical systems. To investigation of environmental sensing of histone-like nucleoid-structuring (H-NS) protein, we also apply AA models to simulate H-NS protein at multiple spatial scales. The environmental sensing ability is reflected by residues at binding sites or filaments mechanical properties. With AA simulation of dimers, we investigated potential of the mean force (PMF), to quantitively determine the sensitivity of the environmental change of binding site. The simulation of H-NS tetramers reveals that the site2 rather than site1 takes responsibility for environmental sensing. Through the simulation of H-NS filaments, we were able to reveal the movement of the DNA binding domain, which is sensitive to environmental sensing, also influence the H-NS stability. Then we extended our investigation to H-NS orthologs from different organism. Our findings revealed the adaptive evolution of H-NS in different organism. Our multiscale modeling approaches can be useful tools to simulate biological complexes. We applied different combination of AA, AACG, and CG models of the same system. Our new computational methodology advanced the ability to simulate large systems or long process more efficiently. Our methodology is readily adaptable to other systems, based on the need of sampling, properties of interest, and simulation efficiency. In any circumstances where balance will be reached between efficiency and high-resolution, multiscale modeling would be significantly valuable in molecular modeling.
Book Synopsis Multiscale Modeling of Biological Complexes by : Xiaochuan Zhao
Download or read book Multiscale Modeling of Biological Complexes written by Xiaochuan Zhao and published by . This book was released on 2021 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulating protein complexes on large time and length scales is often intractable at the atomistic resolution. To address this challenge, we have developed new approaches to integrate coarse-grained (CG), mixed-resolution (referred to as AACG throughout this dissertation), and all-atom (AA) modeling for different stages in a single molecular simulation. First, we developed a top-down multiscale modeling approach -- a new approach, which combines CG, AACG, and AA modeling -- to simulate peptide self-assembly from monomers. We simulated the initial encounter stage with the CG model, while the further assembly and reorganization stages are simulated with the AACG and AA models. Further, a theory was developed to estimate the optimal simulation length for each stage. Finally, our approach and theory have been successfully validated with three amyloid peptides. which highlight the synergy from models at multiple resolutions. This approach improves the efficiency of simulating of peptide assembly process. Furthermore, it serves as proof of concept that applying flexible resolution during the simulation, to adapt to efficiency or accuracy. Second, we gained proof of principle from simulating five heterodimeric models of two G protein-coupled receptors (GPCRs) in the lipid-bilayer membrane on the ns-to-[mu]s timescales. In these simulations of different resolution levels, we observed consistent structural stability, while the AACG and CG models show two- and four-times faster protein diffusion than the AA models, in addition to 4- and 400-fold speedup in the simulation performance. Our findings enable synergy from the combination of AA, AACG, and CG models, which lay the foundation to combine these models in one single simulation. It is also feasible to alternate among different models to represent an efficient solution to investigate complex biophysical systems. To investigation of environmental sensing of histone-like nucleoid-structuring (H-NS) protein, we also apply AA models to simulate H-NS protein at multiple spatial scales. The environmental sensing ability is reflected by residues at binding sites or filaments mechanical properties. With AA simulation of dimers, we investigated potential of the mean force (PMF), to quantitively determine the sensitivity of the environmental change of binding site. The simulation of H-NS tetramers reveals that the site2 rather than site1 takes responsibility for environmental sensing. Through the simulation of H-NS filaments, we were able to reveal the movement of the DNA binding domain, which is sensitive to environmental sensing, also influence the H-NS stability. Then we extended our investigation to H-NS orthologs from different organism. Our findings revealed the adaptive evolution of H-NS in different organism. Our multiscale modeling approaches can be useful tools to simulate biological complexes. We applied different combination of AA, AACG, and CG models of the same system. Our new computational methodology advanced the ability to simulate large systems or long process more efficiently. Our methodology is readily adaptable to other systems, based on the need of sampling, properties of interest, and simulation efficiency. In any circumstances where balance will be reached between efficiency and high-resolution, multiscale modeling would be significantly valuable in molecular modeling.
In this volume, a detailed description of cutting-edge computational methods applied to protein modeling as well as specific applications are presented. Chapters include: the application of Car-Parrinello techniques to enzyme mechanisms, the outline and application of QM/MM methods, polarizable force fields, recent methods of ligand docking, molecular dynamics related to NMR spectroscopy, computer optimization of absorption, distribution, metabolism and excretion extended by toxicity for drugs, enzyme design and bioinformatics applied to protein structure prediction. A keen emphasis is laid on the clear presentation of complex concepts, since the book is primarily aimed at Ph.D. students, who need an insight in up-to-date protein modeling. The inclusion of descriptive, color figures will allow the reader to get a pictorial representation of complicated structural issues.
Book Synopsis Protein Modelling by : Andrew Gamble
Download or read book Protein Modelling written by Andrew Gamble and published by Springer. This book was released on 2014-11-13 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this volume, a detailed description of cutting-edge computational methods applied to protein modeling as well as specific applications are presented. Chapters include: the application of Car-Parrinello techniques to enzyme mechanisms, the outline and application of QM/MM methods, polarizable force fields, recent methods of ligand docking, molecular dynamics related to NMR spectroscopy, computer optimization of absorption, distribution, metabolism and excretion extended by toxicity for drugs, enzyme design and bioinformatics applied to protein structure prediction. A keen emphasis is laid on the clear presentation of complex concepts, since the book is primarily aimed at Ph.D. students, who need an insight in up-to-date protein modeling. The inclusion of descriptive, color figures will allow the reader to get a pictorial representation of complicated structural issues.
This book focuses on the modeling and mathematical analysis of stochastic dynamical systems along with their simulations. The collected chapters will review fundamental and current topics and approaches to dynamical systems in cellular biology. This text aims to develop improved mathematical and computational methods with which to study biological processes. At the scale of a single cell, stochasticity becomes important due to low copy numbers of biological molecules, such as mRNA and proteins that take part in biochemical reactions driving cellular processes. When trying to describe such biological processes, the traditional deterministic models are often inadequate, precisely because of these low copy numbers. This book presents stochastic models, which are necessary to account for small particle numbers and extrinsic noise sources. The complexity of these models depend upon whether the biochemical reactions are diffusion-limited or reaction-limited. In the former case, one needs to adopt the framework of stochastic reaction-diffusion models, while in the latter, one can describe the processes by adopting the framework of Markov jump processes and stochastic differential equations. Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology will appeal to graduate students and researchers in the fields of applied mathematics, biophysics, and cellular biology.
Book Synopsis Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology by : David Holcman
Download or read book Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology written by David Holcman and published by Springer. This book was released on 2017-10-04 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the modeling and mathematical analysis of stochastic dynamical systems along with their simulations. The collected chapters will review fundamental and current topics and approaches to dynamical systems in cellular biology. This text aims to develop improved mathematical and computational methods with which to study biological processes. At the scale of a single cell, stochasticity becomes important due to low copy numbers of biological molecules, such as mRNA and proteins that take part in biochemical reactions driving cellular processes. When trying to describe such biological processes, the traditional deterministic models are often inadequate, precisely because of these low copy numbers. This book presents stochastic models, which are necessary to account for small particle numbers and extrinsic noise sources. The complexity of these models depend upon whether the biochemical reactions are diffusion-limited or reaction-limited. In the former case, one needs to adopt the framework of stochastic reaction-diffusion models, while in the latter, one can describe the processes by adopting the framework of Markov jump processes and stochastic differential equations. Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology will appeal to graduate students and researchers in the fields of applied mathematics, biophysics, and cellular biology.
A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Book Synopsis Introduction to Protein Structure Prediction by : Huzefa Rangwala
Download or read book Introduction to Protein Structure Prediction written by Huzefa Rangwala and published by John Wiley & Sons. This book was released on 2011-03-16 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
