Predicting Protein Protein Interactions And Their Interacting Interfaces With Statistical Learning Techniques
DOWNLOAD
Download Predicting Protein Protein Interactions And Their Interacting Interfaces With Statistical Learning Techniques PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Predicting Protein Protein Interactions And Their Interacting Interfaces With Statistical Learning Techniques book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Predicting Protein Protein Interactions And Their Interacting Interfaces With Statistical Learning Techniques
DOWNLOAD
Author : Alvaro J. Gonzalez
language : en
Publisher:
Release Date : 2012
Predicting Protein Protein Interactions And Their Interacting Interfaces With Statistical Learning Techniques written by Alvaro J. Gonzalez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Protein-protein interactions categories.
Anywhere you look in the cell, you will see proteins at work. Proteins are molecular machines built in a multitude of shapes and sizes. They execute nearly all of the cell's functions. Typically proteins do not carry out their functions as isolated entities. They bind other proteins--other molecules in general--to create chemical factories with a definite spatial structure. With advances in genome sequencing scientists have made a good stride in defining what are all the proteins that may be found in an specific organism, i.e. a partial parts list of the organism's cellular components is now known. However, it is still far from clear how these proteins interact with one another to form the machineries that ultimately perform living functions in the cell. High-throughput experimental methods, such as yeast two hybrid (Y2H) and mass spectroscopy (MS), have been developed to screen a large number of proteins in a cell and assess their potential interactions. Yet, major drawbacks exist for these experimental methods, including the low interaction coverage, the experimental biases toward certain protein types and cellular localizations, and the high cost, both in money and time. These problems have motivated the development of computational methods as alternative and supplemental approaches to predicting protein-protein interaction (PPI). In this dissertation I propose new computational methods to address protein-protein interaction prediction. Specifically, this work tackles the following questions: given two proteins, (i) whether they interact, (ii) if they interact, where are the interacting residues, and (iii) how these interacting residues are paired up, namely the contact matrix. First, I developed a PPI predictor based on hidden Markov models (HMM) and support vector machines (SVM) to predict if two proteins interact. The method builds models of known interacting interfaces based on domain-domain interacting (DDI) families, i.e. groups of protein pairs that bind through the same pair of domains. Each interacting domain family is modeled with a HMM that differentiates interacting residues from non-interacting residues. The proposed algorithm is a two-stage pipeline that combines the flexibility of a generative learning model in the first stage--the domains' HMMs--with the differentiation power of a discriminator--a SVM--in the second stage, connected by a feature selection mechanism based on singular vector decomposition applied to the attributes extracted from domain HMMs as measured by the Fisher score. Once trained, the model can predict if two new proteins interact or not. The method significantly outperformed a previously proposed technique that uses the same input data. Second, I tackled the problem of predicting the binding/functional sites in protein-ligand and enzyme-substrate interactions. These functional residues actually correspond to a protein's binding surface that connects to a chemical, or the active (recognition) site in an enzyme. In the context of a family of related proteins, of which a quantitative measure of the functional relationships among member proteins is available, I developed statistical learning methods that predict the binding/functional residues by finding those positions in the family's multiple sequence alignment with highest correlation to the functional codification of the family. The methods utilize canonical correlation analysis (CCA), kernel CCA (kCCA) and multi-positional kCCA to incorporate non linear correlations between residues and also to analyze clusters of residues as a whole. When tested on benchmark datasets, the proposed methods significantly outperformed known algorithms that treat residues individually and independently. Third, I proposed a method to further predict how the residues are paired up across the interface, namely the contact matrix, whose rows and columns correspond to the residues in the two interacting domains respectively and whose values (1 or 0) indicate whether the corresponding residues (do or do not) interact. The method is based on the platform developed in the first part, the PPI predictor. Instead of using Fisher scores to represent the whole domains as modeled by HMMs, they are reformulated to represent individual residues. Each element of the contact matrix for a sequence-pair is now represented by a feature vector from concatenating the vectors of the two corresponding residues, and the task is to predict the element value (1 or 0) from the feature vector. The sequence-pairs in a DDI family are split into two sets, one for training and one for testing. A support vector machine is trained for a given DDI, using either a consensus contact matrix or contact matrices for individual sequence pairs. The method significantly outperformed a previous multiple sequence alignment based method. Our proposed algorithm is capable of extracting characteristic features and at the same time untying the residues from the rigid multiple sequence alignments that are used in the previous methods. This enables handling residues corresponding to delete and insert states, and allows for a supervised learning on individual contact points, eliminating the need of a consensus contact matrix for the domain families, which has been a main source for false predictions. While designed for predicting contact points between interacting protein domains, the method may be useful as a module in protein folding and docking.
Computational Intelligence In Protein Ligand Interaction Analysis
DOWNLOAD
Author : Bing Wang
language : en
Publisher: Academic Press
Release Date : 2024-03-22
Computational Intelligence In Protein Ligand Interaction Analysis written by Bing Wang and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Science categories.
Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein interaction sites, and identifying protein drug targets. The book emphasizes novel approaches to protein-ligand interactions, including machine learning and deep learning, presenting a state-of-the-art suite of skills for researchers. The volume represents a resource for scientists, detailing the fundamentals of computational methods, showing how to use computational algorithms to study protein interaction data, and giving scientific explanations for biological data through computational intelligence. Fourteen chapters offer a comprehensive guide to protein interaction data and computational intelligence methods for protein-ligand interactions. Presents a guide to computational techniques for protein-ligand interaction analysis Guides researchers in developing advanced computational intelligence methods for the protein-ligand problem Identifies appropriate computational tools for various problems Demonstrates the use of advanced techniques such as vector machine, neural networks, and machine learning Offers the computational, mathematical and statistical skills researchers need
Machine Learning For The Prediction Of Protein Protein Interactions
DOWNLOAD
Author : Jos? Antonio Reyes
language : en
Publisher:
Release Date : 2010
Machine Learning For The Prediction Of Protein Protein Interactions written by Jos? Antonio Reyes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
The prediction of protein-protein interactions (PPI) has recently emerged as an important problem in the fields of bioinformatics and systems biology, due to the fact that most essential cellular processes are mediated by these kinds of interactions. In this thesis we focussed in the prediction of co-complex interactions, where the objective is to identify and characterize protein pairs which are members of the same protein complex. Although high-throughput methods for the direct identification of PPI have been developed in the last years. It has been demonstrated that the data obtained by these methods is often incomplete and suffers from high false-positive and false-negative rates. In order to deal with this technology-driven problem, several machine learning techniques have been employed in the past to improve the accuracy and trustability of predicted protein interacting pairs, demonstrating that the combined use of direct and indirect biological insights can improve the quality of predictive PPI models. This task has been commonly viewed as a binary classification problem. However, the nature of the data creates two major problems. Firstly, the imbalanced class problem due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly, the selection of negative examples is based on some unreliable assumptions which could introduce some bias in the classification results. The first part of this dissertation addresses these drawbacks by exploring the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilize examples of just one class to generate a predictive model which is consequently independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We designed and carried out a performance evaluation study of several OCC methods for this task. We also undertook a comparative performance evaluation with several conventional learning techniques. Furthermore, we pay attention to a new potential drawback which appears to affect the performance of PPI prediction. This is associated with the composition of the positive gold standard set, which contain a high proportion of examples associated with interactions of ribosomal proteins. We demonstrate that this situation indeed biases the classification task, resulting in an over-optimistic performance result. The prediction of non-ribosomal PPI is a much more difficult task. We investigate some strategies in order to improve the performance of this subtask, integrating new kinds of data as well as combining diverse classification models generated from different sets of data. In this thesis, we undertook a preliminary validation study of the new PPI predicted by using OCC methods. To achieve this, we focus in three main aspects: look for biological evidence in the literature that support the new predictions; the analysis of predicted PPI networks properties; and the identification of highly interconnected groups of proteins which can be associated with new protein complexes. Finally, this thesis explores a slightly different area, related to the prediction of PPI types. This is associated with the classification of PPI structures (complexes) contained in the Protein Data Bank (PDB) data base according to its function and binding affinity. Considering the relatively reduced number of crystalized protein complexes available, it is not possible at the moment to link these results with the ones obtained previously for the prediction of PPI complexes. However, this could be possible in the near future when more PPI structures will be available.
Protein Protein Interactions And Networks
DOWNLOAD
Author : Anna Panchenko
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-06
Protein Protein Interactions And Networks written by Anna Panchenko 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 2010-04-06 with Science categories.
The biological interactions of living organisms, and protein-protein interactions in particular, are astonishingly diverse. This comprehensive book provides a broad, thorough and multidisciplinary coverage of its field. It integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a comprehensive global view of the diverse data on protein-protein interactions and protein interaction networks.
Protein Protein Interaction Networks
DOWNLOAD
Author : Stefan Canzar
language : en
Publisher: Humana
Release Date : 2019-10-04
Protein Protein Interaction Networks written by Stefan Canzar and has been published by Humana this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-04 with Science categories.
This volume explores techniques that study interactions between proteins in different species, and combines them with context-specific data, analysis of omics datasets, and assembles individual interactions into higher-order semantic units, i.e., protein complexes and functional modules. The chapters in this book cover computational methods that solve diverse tasks such as the prediction of functional protein-protein interactions; the alignment-based comparison of interaction networks by SANA; using the RaptorX-ComplexContact webserver to predict inter-protein residue-residue contacts; the docking of alternative confirmations of proteins participating in binary interactions and the visually-guided selection of a docking model using COZOID; the detection of novel functional units by KeyPathwayMiner and how PathClass can use such de novo pathways to classify breast cancer subtypes. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary hardware- and software, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Protein-Protein Interaction Networks: Methods and Protocols is a valuable resource for both novice and expert researchers who are interested in learning more about this evolving field.
Protein Interactions Computational Methods Analysis And Applications
DOWNLOAD
Author : M Michael Gromiha
language : en
Publisher: World Scientific
Release Date : 2020-03-05
Protein Interactions Computational Methods Analysis And Applications written by M Michael Gromiha and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-05 with Science categories.
This book is indexed in Chemical Abstracts ServiceThe interactions of proteins with other molecules are important in many cellular activities. Investigations have been carried out to understand the recognition mechanism, identify the binding sites, analyze the the binding affinity of complexes, and study the influence of mutations on diseases. Protein interactions are also crucial in structure-based drug design.This book covers computational analysis of protein-protein, protein-nucleic acid and protein-ligand interactions and their applications. It provides up-to-date information and the latest developments from experts in the field, using illustrations to explain the key concepts and applications. This volume can serve as a single source on comparative studies of proteins interacting with proteins/DNAs/RNAs/carbohydrates and small molecules.
Protein Protein Interactions And Networks
DOWNLOAD
Author : Anna Panchenko
language : en
Publisher: Springer
Release Date : 2009-08-29
Protein Protein Interactions And Networks written by Anna Panchenko and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-29 with Science categories.
The biological interactions of living organisms, and protein-protein interactions in particular, are astonishingly diverse. This comprehensive book provides a broad, thorough and multidisciplinary coverage of its field. It integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a comprehensive global view of the diverse data on protein-protein interactions and protein interaction networks.
Proteomics Data Analysis
DOWNLOAD
Author : Daniela Cecconi
language : en
Publisher:
Release Date : 2021
Proteomics Data Analysis written by Daniela Cecconi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Proteomics categories.
This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.
Protein Interactions
DOWNLOAD
Author : Volkhard Helms
language : en
Publisher: John Wiley & Sons
Release Date : 2023-02-06
Protein Interactions written by Volkhard Helms 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 2023-02-06 with Science categories.
A fundamental guide to the burgeoning field of protein interactions From enzymes to transcription factors to cell membrane receptors, proteins are at the heart of biological cell function. Virtually all cellular processes are governed by their interactions, with one another, with cell bodies, with DNA, or with small molecules. The systematic study of these interactions is called Interactomics, and research within this new field promises to shape the future of molecular cell biology. Protein Interactions goes beyond any existing guide to protein interactions, presenting the first truly comprehensive overview of the field. Edited by two leading scholars in the field of protein bioinformatics, this book covers all known categories of protein interaction, stable as well as transient, as well as the effect of mutations and post-translational modifications on the interaction behavior. Protein Interactions readers will also find: Introductory chapters on protein structure, conformational dynamics, and protein-protein binding interfaces A data-driven approach incorporating machine learning and integrating experimental data into computational models An outlook on the current challenges in the field and suggestions for future research Protein Interactions will serve as a fundamental resource for novice researchers who want a systematic introduction to interactomics, as well as for experienced cell biologists and bioinformaticians who want to gain an edge in this exciting new field.
Protein Protein Interactions
DOWNLOAD
Author : Cheryl L. Meyerkord
language : en
Publisher: Humana
Release Date : 2016-10-29
Protein Protein Interactions written by Cheryl L. Meyerkord and has been published by Humana this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-29 with Science categories.
The second edition covers a wide range of protein-protein interaction detection topics. Protein-Protein Interactions: Methods and Applications focuses on core technological platforms used to study protein-protein interactions and cutting-edge technologies that reflect recent scientific advances and the emerging focus on therapeutic discovery. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of necessary materials and reagents, step-by-step laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. These well-detailed protocols describe methods for identifying protein-protein interaction partners, analyzing of protein-protein interactions quantitatively and qualitatively, monitoring protein-protein interactions in live cells, and predicting and determining interaction interfaces. Authoritative and cutting-edge, Protein-Protein Interactions: Methods and Applications, Second Edition is a valuable resource that will enable readers to elucidate the mechanisms of protein-protein interactions, determine the role of these interactions in diverse biological processes, and target protein-protein interactions for therapeutic discovery.