Protein Modeling Using Hidden Markov Models

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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."

Hidden Markov Models In Computational Biology

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Author : Anders Krogh
language : en
Publisher:
Release Date : 1993

Hidden Markov Models In Computational Biology written by Anders Krogh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Biological models 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.

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.

Hidden Markov Models For Bioinformatics

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Author : T. Koski
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-11-30

Hidden Markov Models For Bioinformatics written by T. Koski 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 2001-11-30 with Mathematics categories.

The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis. Audience: This book will be of interest to advanced undergraduate and graduate students with a fairly limited background in probability theory, but otherwise well trained in mathematics and already familiar with at least some of the techniques of algorithmic sequence analysis.

3d Hidden Markov Models And Normal Mode Analysis For Modeling Protein Structure And Motion In A Statistical Framework

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Author : Vadim Alexandrov
language : en
Publisher:
Release Date : 2004

3d Hidden Markov Models And Normal Mode Analysis For Modeling Protein Structure And Motion In A Statistical Framework written by Vadim Alexandrov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.

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 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.

Sensitive Detection Of Distant Protein Relationships Using Hidden Markov Model Alignment

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Author : Xiaobing Shi
language : en
Publisher:
Release Date : 1999

Sensitive Detection Of Distant Protein Relationships Using Hidden Markov Model Alignment written by Xiaobing Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with 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.