Protein Structure Function Classification Using Hidden Markov Models
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Protein Structure Function Classification Using Hidden Markov Models
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Author : Moira Ellen Regelson
language : en
Publisher:
Release Date : 1997
Protein Structure Function Classification Using Hidden Markov Models written by Moira Ellen Regelson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Electronic dissertations categories.
Highly Specific Protein Function Classification Using Hidden Markov Models
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Author : Oliver Alexander Hampton
language : en
Publisher:
Release Date : 2004
Highly Specific Protein Function Classification Using Hidden Markov Models written by Oliver Alexander Hampton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Markov processes categories.
Sequence Based Protein Classification
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Author : Stefan Mutter
language : en
Publisher:
Release Date : 2011
Sequence Based Protein Classification written by Stefan Mutter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Bioinformatics categories.
Protein Homology Detection Through Alignment Of Markov Random Fields
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Author : Jinbo Xu
language : en
Publisher: Springer
Release Date : 2015-01-22
Protein Homology Detection Through Alignment Of Markov Random Fields written by Jinbo Xu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-22 with Computers categories.
This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.
Computational Methods For Protein Structure Prediction And Modeling
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Author : Ying Xu
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-24
Computational Methods For Protein Structure Prediction And Modeling written by Ying Xu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-24 with Science categories.
Volume One of this two-volume sequence focuses on the basic characterization of known protein structures, and structure prediction from protein sequence information. Eleven chapters survey of the field, covering key topics in modeling, force fields, classification, computational methods, and structure prediction. Each chapter is a self contained review covering definition of the problem and historical perspective; mathematical formulation; computational methods and algorithms; performance results; existing software; strengths, pitfalls, challenges, and future research.
Introduction To Protein Structure Prediction
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Author : Huzefa Rangwala
language : en
Publisher: John Wiley & Sons
Release Date : 2011-03-16
Introduction To Protein Structure Prediction written by Huzefa Rangwala and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-16 with Science categories.
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.
Protein Structure Prediction
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Author : Mohammed Zaki
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-12
Protein Structure Prediction written by Mohammed Zaki and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-12 with Science categories.
This book covers elements of both the data-driven comparative modeling approach to structure prediction and also recent attempts to simulate folding using explicit or simplified models. Despite the unsolved mystery of how a protein folds, advances are being made in predicting the interactions of proteins with other molecules. Also rapidly advancing are the methods for solving the inverse folding problem, the problem of finding a sequence to fit a structure. This book focuses on the various computational methods for prediction, their successes and their limitations, from the perspective of their most well known practitioners.
Application Of Hidden Markov Model Based Methods For Gaining Insights Into Protein Domain Evolution And Function
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Author : Amit Anil Upadhyay
language : en
Publisher:
Release Date : 2015
Application Of Hidden Markov Model Based Methods For Gaining Insights Into Protein Domain Evolution And Function written by Amit Anil Upadhyay and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Glycosidases categories.
With the explosion in the amount of available sequence data, computational methods have become indispensable for studying proteins. Domains are the fundamental structural, functional and evolutionary units that make up proteins. Studying protein domains is an important part of understanding protein function and evolution. Hidden Markov Models (HMM) are one of the most successful methods that have been applied for protein sequence and structure analysis. In this study, HMM based methods were applied to study the evolution of sensory domains in microbial signal transduction systems as well as functional characterization and identification of cellulases in metagenomics datasets. Use of HMM domain models enabled identification of the ambiguity in sequence and structure based definitions of the Cache domain family. Cache domains are extracellular sensory domains that are present in microbial signal transduction proteins and eukaryotic voltage gated calcium channels. The ambiguity in domain definitions was resolved and more accurate HMM models were built that detected more than 50,000 new members. It was discovered that Cache domains constitute the largest family of extracellular sensory domains in prokaryotes. Cache domains were also found to be remotely homologous to PAS domains at the level of sequence, a relationship previously suggested purely based on structural comparisons. We used HMM-HMM comparisons to study the diversity of extracellular sensory domains in prokaryotic signal transductions systems. This approach allowed annotation of more than 46,000 sequences and reduced the percentage of unknown domains from 64% to 15%. New relationships were also discovered between domain families that were otherwise thought to be unrelated. Finally, HMM models were used to retrieve Family 48 glycoside hydrolases (GH48) from sequence databases. Analysis of these sequences, enabled the identification of distinguishing features of cellulases. These features were used to identify GH48 cellulases from metagenomics datasets. In summary, HMM based methods enabled domain identification, remote homology detection and functional characterization of protein domains.
Protein Modeling Using Hidden Markov Models
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Author :
language : en
Publisher:
Release Date : 1992
Protein Modeling Using Hidden Markov Models written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Markov processes categories.
Abstract: "We apply Hidden Markov Models (HMMs) to the problem of statistical modeling and multiple sequence alignment of protein families. A variant of the Expectation Maximization (EM) algorithm known as the Viterbi algorithm is used to obtain the statistical model from the unaligned sequences. In a detailed series of experiments, we have taken 400 unaligned globin sequences, and produced a statistical model entirely automatically from the primary (unaligned) sequences. We use no prior knowledge of globin structure. Using this model, we obtained a multiple alignment of the 400 sequences and 225 other globin sequences that agrees almost perfectly with a structural alignment by Bashford et al. This model can also discriminate all these 625 globins from nonglobin protein sequences with greater than 99% accuracy, and can thus be used for database searches."
Multiple Alignments Of Protein Structures And Their Application To Sequence Annotation With Hidden Markov Models
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Author : Eric David Scheeff
language : en
Publisher:
Release Date : 2003
Multiple Alignments Of Protein Structures And Their Application To Sequence Annotation With Hidden Markov Models written by Eric David Scheeff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.