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New Approaches of Protein Function Prediction from Protein Interaction Networks

New Approaches of Protein Function Prediction from Protein Interaction Networks
  • Author : Jingyu Hou
  • Publisher :Unknown
  • Release Date :2017-01-13
  • Total pages :124
  • ISBN : 9780128099445
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Summary : New Approaches of Protein Function Prediction from Protein Interaction Networks contains the critical aspects of PPI network based protein function prediction, including semantically assessing the reliability of PPI data, measuring the functional similarity between proteins, dynamically selecting prediction domains, predicting functions, and establishing corresponding prediction frameworks. Functional annotation of proteins is vital to biological and clinical research and other applications due to the important roles proteins play in various biological processes. Although the functions of some proteins have been annotated via biological experiments, there are still many proteins whose functions are yet to be annotated due to the limitations of existing methods and the high cost of experiments. To overcome experimental limitations, this book helps users understand the computational approaches that have been rapidly developed for protein function prediction. Provides innovative approaches and new developments targeting key issues in protein function prediction Presents heuristic ideas for further research in this challenging area

Protein Function Prediction: Methods and Protocols

Protein Function Prediction: Methods and Protocols
  • Author : Daisuke Kihara
  • Publisher :Unknown
  • Release Date :2019-05-12
  • Total pages :252
  • ISBN : 1493983687
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Summary :

Sequence-based Protein Function Prediction

Sequence-based Protein Function Prediction
  • Author : Brett Poulin
  • Publisher :Unknown
  • Release Date :2004
  • Total pages :180
  • ISBN : OCLC:62408348
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Summary :

Prediction of Protein Structures, Functions, and Interactions

Prediction of Protein Structures, Functions, and Interactions
  • Author : Janusz M. Bujnicki
  • Publisher :Unknown
  • Release Date :2008-12-23
  • Total pages :302
  • ISBN : 0470741902
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Summary : The growing flood of new experimental data generated by genome sequencing has provided an impetus for the development of automated methods for predicting the functions of proteins that have been deduced by sequence analysis and lack experimental characterization. Prediction of Protein Structures, Functions and Interactions presents a comprehensive overview of methods for prediction of protein structure or function, with the emphasis on their availability and possibilities for their combined use. Methods of modeling of individual proteins, prediction of their interactions, and docking of complexes are put in the context of predicting gene ontology (biological process, molecular function, and cellular component) and discussed in the light of their contribution to the emerging field of systems biology. Topics covered include: first steps of protein sequence analysis and structure prediction automated prediction of protein function from sequence template-based prediction of three-dimensional protein structures: fold-recognition and comparative modelling template-free prediction of three-dimensional protein structures quality assessment of protein models prediction of molecular interactions: from small ligands to large protein complexes macromolecular docking integrating prediction of structure, function, and interactions Prediction of Protein Structures, Functions and Interactions focuses on the methods that have performed well in CASPs, and which are constantly developed and maintained, and are freely available to academic researchers either as web servers or programs for local installation. It is an essential guide to the newest, best methods for prediction of protein structure and functions, for researchers and advanced students working in structural bioinformatics, protein chemistry, structural biology and drug discovery.

PROTEIN FUNCTION PREDICTION BA

PROTEIN FUNCTION PREDICTION BA
  • Author : Yatong An,{273a67}亚{275c28}
  • Publisher :Unknown
  • Release Date :2017-01-26
  • Total pages :80
  • ISBN : 1361011637
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Summary : This dissertation, "Protein Function Prediction Based on Pocket-specific Noncontiguous Amino Acid Subsequences" by Yatong, An, {273a67}亚{275c28}, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Building a protein functional repertoire is important for many life sciences. Unfortunately, less than 1% of protein sequences have been annotated with reliable evidence. The use of computational methods to predict protein functions has become a common means to bridge this formidable gap. In this thesis, it is proposed to use pocket-specific noncontiguous amino acid subsequences for predicting protein functions. These subsequence patterns have a strong function classification capability and are also complementary to protein sequence alignment methods. On the basis of a benchmark of ∼1600 testing proteins from the Protein Data Bank (PDB), It is demonstrated that function prediction using pocket-specific noncontiguous amino acid subsequences can be much more accurate than using three-dimensional pocket structures. Because these noncontiguous amino acid subsequences are independent of protein or pocket structures, the method based on such subsequence patterns can be easily applied to proteins with unknown structures. Predictors achieve state-of-the-art performance on two benchmarks constructed using proteins from the PDB and SwissProt respectively. Then protein sequence alignment features are further integrated into our pocket-specific noncontiguous subsequence model. The maximum F-measure of the integrated predictor on the PDB-based benchmark is 0.844 for the molecular function (MF) ontology and 0.838 for the biological process (BP) ontology, representing respective performance improvements of 47.8% and 48.3% over best results achieved with existing methods. On the SwissProt-based benchmark, the maximum Fmeasure of the integrated predictor is 0.627 for MF and 0.468 for BP, representing respective performance improvements of 29.0% and 38.1% over best results achieved with existing methods. Subjects: Amino acid sequence Proteomics - Data processing

Protein Function Prediction for Omics Era

Protein Function Prediction for Omics Era
  • Author : Daisuke Kihara
  • Publisher :Unknown
  • Release Date :2011-04-19
  • Total pages :310
  • ISBN : 9400708815
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Summary : Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred

Protein Function Prediction by Integrating Sequence, Structure and Binding Affinity Information

Protein Function Prediction by Integrating Sequence, Structure and Binding Affinity Information
  • Author : Huiying Zhao
  • Publisher :Unknown
  • Release Date :2013
  • Total pages :354
  • ISBN : OCLC:869742922
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Summary : Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA, RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.

Protein Function Prediction for Omics Era

Protein Function Prediction for Omics Era
  • Author : Daisuke Kihara
  • Publisher :Unknown
  • Release Date :2011-04-20
  • Total pages :310
  • ISBN : 9400708807
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Summary : Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred

Protein Function Prediction from Protein Interaction Network

Protein Function Prediction from Protein Interaction Network
  • Author : Sovan Saha,Piyali Chatterjee
  • Publisher :Unknown
  • Release Date :2013
  • Total pages :148
  • ISBN : 3659402788
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Summary : Proteins perform every function in a cell. With the advent of genome sequencing projects for different organisms, large amounts of DNA and protein sequence data is available, whereas their biological function is still unknown in the most of the cases. Predicting protein function is the most challenging problem in post-genomic era. Using sequence homology, phylogenetic profiles, gene expression data, and function of unknown protein can be predicted. Recently, the large interaction networks constructed from high throughput techniques like Yeast2Hybrid experiments are also used in prediction of protein function. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Based on the concept that a protein performs similar function like its neighbor in protein interaction network, a method is proposed to predict protein function using protein-protein interaction data.This analysis should enlighten the path for predicting unannotated protein function hence identifying diseases and inventing methods of it's cureness.

Genome-Wide Prediction and Analysis of Protein-Protein Functional Linkages in Bacteria

Genome-Wide Prediction and Analysis of Protein-Protein Functional Linkages in Bacteria
  • Author : Vijaykumar Yogesh Muley,Vishal Acharya
  • Publisher :Unknown
  • Release Date :2012-07-28
  • Total pages :60
  • ISBN : 9781461447054
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Summary : ​​ ​Using genome sequencing, one can predict possible interactions among proteins. There are very few titles that focus on protein-protein interaction predictions in bacteria. The authors will describe these methods and further highlight its use to predict various biological pathways and complexity of the cellular response to various environmental conditions. Topics include analysis of complex genome-scale protein-protein interaction networks, effects of reference genome selection on prediction accuracy, and genome sequence templates to predict protein function.

Function Prediction from Protein Sequence and Protein Structure Comparison

Function Prediction from Protein Sequence and Protein Structure Comparison
  • Author : Harley Coleman
  • Publisher :Unknown
  • Release Date :2017-01-30
  • Total pages :38
  • ISBN : 154282530X
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Summary : Most commonly, protein function is inferred from the known functions of homologous proteins. For homologous proteins with easily recognizable sequence similarity, this type of prediction is based on the 'similar sequence-similar structure-similar function' paradigm. - Domains can be seen as 'units of evolution', and, therefore, both structural and functional similarity between proteins needs to be analyzed at the domain level. - Sequence comparison is most sensitive at the protein level and the detection of distantly related sequences is easier in protein translation. - To allow the identification of homologous domains with low sequence identity (

From Protein Structure to Function with Bioinformatics

From Protein Structure to Function with Bioinformatics
  • Author : Daniel J. Rigden
  • Publisher :Unknown
  • Release Date :2017-04-06
  • Total pages :503
  • ISBN : 9789402410693
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Summary : This book is about protein structural bioinformatics and how it can help understand and predict protein function. It covers structure-based methods that can assign and explain protein function based on overall folds, characteristics of protein surfaces, occurrence of small 3D motifs, protein-protein interactions and on dynamic properties. Such methods help extract maximum value from new experimental structures, but can often be applied to protein models. The book also, therefore, provides comprehensive coverage of methods for predicting or inferring protein structure, covering all structural classes from globular proteins and their membrane-resident counterparts to amyloid structures and intrinsically disordered proteins. The book is split into two broad sections, the first covering methods to generate or infer protein structure, the second dealing with structure-based function annotation. Each chapter is written by world experts in the field. The first section covers methods ranging from traditional homology modelling and fold recognition to fragment-based ab initio methods, and includes a chapter, new for the second edition, on structure prediction using evolutionary covariance. Membrane proteins and intrinsically disordered proteins are each assigned chapters, while two new chapters deal with amyloid structures and means to predict modes of protein-protein interaction. The second section includes chapters covering functional diversity within protein folds and means to assign function based on surface properties and recurring motifs. Further chapters cover the key roles of protein dynamics in protein function and use of automated servers for function inference. The book concludes with two chapters covering case studies of structure prediction, based respectively on crystal structures and protein models, providing numerous examples of real-world usage of the methods mentioned previously. This book is targeted at postgraduate students and academic researchers. It is most obviously of interest to protein bioinformaticians and structural biologists, but should also serve as a guide to biologists more broadly by highlighting the insights that structural bioinformatics can provide into proteins of their interest.

Human Protein Function Prediction

Human Protein Function Prediction
  • Author : A. E. Lobley
  • Publisher :Unknown
  • Release Date :2010
  • Total pages :229
  • ISBN : OCLC:829958853
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Summary : Experimental characterisation of protein cellular function can be prohibitively expensive and take years to complete. To address this problem, this thesis focuses on the development of computational approaches to predict function from sequence. For sequences with well characterised close relatives, annotation is trivial, orphans or distant homologues present a greater challenge. The use of a feature based method employing ensemble support vector machines to predict individual Gene Ontology classes is investigated. It is found that different combinations of feature inputs are required to recognise different functions. Although the approach is applicable to any human protein sequence, it is restricted to broadly descriptive functions. The method is well suited to prioritisation of candidate functions for novel proteins rather than to make highly accurate class assignments. Signatures of common function can be derived from different biological characteristics; interactions and binding events as well as expression behaviour. To investigate the hypothesis that common function can be derived from expression information, public domain human microarray datasets are assembled. The questions of how best to integrate these datasets and derive features that are useful in function prediction are addressed. Both co-expression and abundance information is represented between and within experiments and investigated for correlation with function. It is found that features derived from expression data serve as a weak but significant signal for recognising functions. This signal is stronger for biological processes than molecular function categories and independent of homology information. The protein domain has historically been coined as a modular evolutionary unit of protein function. The occurrence of domains that can be linked by ancestral fusion events serves as a signal for domain-domain interactions. To exploit this information for function prediction, novel domain architecture and fused architecture scores are developed. Architecture scores rather than single domain scores correlate more strongly with function, and both architecture and fusion scores correlate more strongly with molecular functions than biological processes. The final study details the development of a novel heterogeneous function prediction approach designed to target the annotation of both homologous and non-homologous proteins. Support vector regression is used to combine pair-wise sequence features with expression scores and domain architecture scores to rank protein pairs in terms of their functional similarities. The target of the regression models represents the continuum of protein function space empirically derived from the Gene Ontology molecular function and biological process graphs. The merit and performance of the approach is demonstrated using homologous and non-homologous test datasets and significantly improves upon classical nearest neighbour annotation transfer by sequence methods. The final model represents a method that achieves a compromise between high specificity and sensitivity for all human proteins regardless of their homology status. It is expected that this strategy will allow for more comprehensive and accurate annotations of the human proteome.

Year 2 Report

Year 2 Report
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2012
  • Total pages :21
  • ISBN : OCLC:1065786730
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Summary : Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fully automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.

Prediction of Protein Function Using Text Features Extracted from the Biomedical Literature

Prediction of Protein Function Using Text Features Extracted from the Biomedical Literature
  • Author : Andrew Wong
  • Publisher :Unknown
  • Release Date :2013
  • Total pages :166
  • ISBN : OCLC:845228440
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Summary : Proteins perform many important functions in the cell and are essential to the health of the cell and the organism. As such, there is much effort to understand the function of proteins. Due to the advances in sequencing technology, there are many sequences of proteins whose function is yet unknown. Therefore, computational systems are being developed and used to help predict protein function. Most computational systems represent proteins using features that are derived from protein sequence or protein structure to predict function. In contrast, there are very few systems that use the biomedical literature as a source of features. Earlier work demonstrated the utility of biomedical literature as a source of text features for predicting protein subcellular location. In this thesis we build on that earlier work, and examine the effectiveness of using text features to predict protein function. Using the molecular function and biological process terms from the Gene Ontology (GO) as our function classes, we trained two classifiers (k-Nearest Neighbour and Support Vector Machines) to predict protein function. The proteins were represented using text features that were extracted from biomedical abstracts based on statistical properties. For evaluation, the performance of our two classifiers was compared to that of two baseline classifiers: one that assigns function based solely on the prior distribution of protein function, and one that assigns function based on sequence similarity. The systems were trained and tested using 5-fold cross-validation over a dataset of more than 36,000 proteins. Overall, we show that text features extracted from biomedical literature can be used to predict protein function for any organism. Our results also show that our text-based classifier typically has comparable performance to the sequence-similarity baseline classifier. Based on our results and what previous work had shown, we believe that text features can be integrated with other types of features to provide more accurate predictions for protein function.

Protein Function Prediction

Protein Function Prediction
  • Author : Burkhard Rost
  • Publisher :Unknown
  • Release Date :2008
  • Total pages :229
  • ISBN : 0123695376
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Summary :

Computational Approaches to Predicting Protein Function from Structure

Computational Approaches to Predicting Protein Function from Structure
  • Author : Eric W. Stawiski
  • Publisher :Unknown
  • Release Date :2001
  • Total pages :240
  • ISBN : UCAL:X67533
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Summary :

Protein Function Prediction Using Decision Tree Technique

Protein Function Prediction Using Decision Tree Technique
  • Author : Venkata Rama Kumar Swamy Yedida
  • Publisher :Unknown
  • Release Date :2008
  • Total pages :80
  • ISBN : OCLC:1230480741
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Summary : The human genome project and numerous other genome projects have produced a large and ever increasing amount of sequence data. One of the main research challenges in the post-genomic era is to understand the relationship between the nucleotide sequences of genes and the functions of the proteins they encode. The objective of this thesis is to develop an automated protein function prediction system that is based on a set of homologous proteins and gene ontology categories. A novel measure based on a set of best local alignments is used to identify the homologues. The biological functions of the homologous proteins are characterized with gene ontology annotations. The protein function prediction is performed based on data mining models using decision trees. The models are trained and tested using the complete proteome of model organism yeast. The results show that the prediction accuracy depends on individual functional groups of proteins. There is a general trend of decreased model accuracy with the level of a group on the gene ontology graph, but the accuracy at a fix level varies from group to group. The prediction accuracy varies from group to group, no obvious accuracy changes from one level to another. These variations of accuracy illustrate certain limitations of sequence-based protein function prediction methods. But the fundamental assumption used in this thesis, similar amino acid sequences implying similar biological functions, is largely valid. The prediction results based on the proteome of yeast indicate that the accuracies for most of the functional groups are over 75%. We conclude that the decision tree model can be used as a preliminary tool for protein function prediction although the prediction results need to be verified through other means.

Bayesian Markov Random Field Analysis for Integrated Network-based Protein Function Prediction

Bayesian Markov Random Field Analysis for Integrated Network-based Protein Function Prediction
  • Author : Yiannis A. I. Kourmpetis
  • Publisher :Unknown
  • Release Date :2011
  • Total pages :113
  • ISBN : 908585959X
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Summary :

Prediction of Protein Structure and the Principles of Protein Conformation

Prediction of Protein Structure and the Principles of Protein Conformation
  • Author : G.D. Fasman
  • Publisher :Unknown
  • Release Date :2012-12-06
  • Total pages :798
  • ISBN : 9781461315711
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Summary : The prediction of the conformation of proteins has developed from an intellectual exercise into a serious practical endeavor that has great promise to yield new stable enzymes, products of pharmacological significance, and catalysts of great potential. With the application of predic tion gaining momentum in various fields, such as enzymology and immunology, it was deemed time that a volume be published to make available a thorough evaluation of present methods, for researchers in this field to expound fully the virtues of various algorithms, to open the field to a wider audience, and to offer the scientific public an opportunity to examine carefully its successes and failures. In this manner the practitioners of the art could better evaluate the tools and the output so that their expectations and applications could be more realistic. The editor has assembled chapters by many of the main contributors to this area and simultaneously placed their programs at three national resources so that they are readily available to those who wish to apply them to their personal interests. These algorithms, written by their originators, when utilized on pes or larger computers, can instantaneously take a primary amino acid sequence and produce a two-or three-dimensional artistic image that gives satisfaction to one's esthetic sensibilities and food for thought concerning the structure and function of proteins. It is in this spirit that this volume was envisaged.

Network-based Information Integration for Protein Function Prediction

Network-based Information Integration for Protein Function Prediction
  • Author : Xiaoyu Jiang
  • Publisher :Unknown
  • Release Date :2009
  • Total pages :182
  • ISBN : OCLC:606828841
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Summary : Abstract: Protein function prediction is a fundamental problem in computational biology. For protein activities described by terms in databases such as the Gene Ontology (GO), this task is typically pursued as a binary classification problem. As a result of an astonishing increase in the available genome-wide protein information, integrating different protein datasets has become a significant opportunity and a major focus to infer functionality. This dissertation contains three novel approaches to integrate popular protein information to classify proteins into functional categories. A probabilistic method, Hierarchical Binomial-Neighborhood (HBN), combining proteins' relational information from the protein-protein interaction (PPI) network, together with the GO hierarchical structure, is proposed first. Results from comparing analogous models on terms from the biological process ontology and genes from the yeast genome show substantial improvement and further analysis illustrates that such an improvement is uniformly consistent with the GO depth. Being aware of the fact that the gene interaction knowledge is still incomplete in most organisms, the second approach we develop is an aggressively integrative probabilistic framework, Probabilistic Hierarchical Inferences for Protein Activity (PHIPA), with improved data usage efficiency, for combining protein relational network, categorical motif and cellular localization information and the GO hierarchy. We implement it on a network extracted from an integrative protein-protein association databases STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). Being based on Nearest-Neighbor, or the "guilt-by-association" counting principle, both HBN and PHIPA use only the local neighborhood information, and are therefore built on local probabilistic models. In contrast, we develop a third approach, a fully Bayesian network-based auto-probit framework encoding the functional similarity influenced by the network topology. We not only show that the auto-probit model works equally well in prediction as the "local" methods, but also demonstrate its capability of producing more potentially interesting protein predictions by taking advantage of GO annotation uncertainty, which is critical in using and improving the GO database but yet has been ignored by most existing methodologies in this context.