Interacting Protein Domains
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Interacting Protein Domains
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Author : Ludwig Heilmeyer
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Interacting Protein Domains written by Ludwig Heilmeyer 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 2013-06-29 with Science categories.
This is now the fourth time that protein phosphorylation has been the focus of a NATO Advanced Study Institute. The first meeting with the topic "Signal Trans duction and Protein Phosphorylation" was held on the island of Spezai, Greece, in September 1986. The second one took place in Chateau La Londe, France, in September 1989 on "Cellular Regulation by Protein Phosphorylation", the third one on " Tyrosine Phosphorylation/Dephosphorylation and Downstream Signaling" was in September 1992 in Maratea, Italy. The titles of these books clearly mirror the developments that have taken place in the last decade. Beginning with the recognition that protein phosphorylation is at the center of signaling -clearly established in 1990 -it became apparent that many cellular processes are regulated by this mode. A new focus then emerged when it was recognized that growth factors are bound to corresponding receptors trigger protein tyrosine phosphorylation which controls cell prolifera tion. This was the topic of the third meeting in this series. It is now evident that further progress depends on understanding the three dimensional structure of the proteins involved. It goes without saying, for example, that understanding the location of proteins by adaptor proteins is only possible on the basis of the three dimensional protein structure. Therefore, the fourth meeting in this series concentrated on the protein structure of signaling molecules as well as on the elucidation of the principles of protein domain interactions.
Modular Protein Domains
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Author : Giovanni Cesareni
language : en
Publisher: John Wiley & Sons
Release Date : 2006-03-06
Modular Protein Domains written by Giovanni Cesareni 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 2006-03-06 with Science categories.
Since the full functionality of any given protein can only be understood in terms of its interaction with other, often regulatory proteins, this unique reference source covers all relevant protein domains, including SH2, SH3, PDZ, WW, PTB, EH, PH and PX. Its user-oriented concept combines broad coverage with easy retrieval of essential information, and includes a special section on Web-based tools and databases covering protein modules and functional peptide motifs. Essential for the study of protein-protein interactions in vivo or in silico, and a prerequisite for successful functional proteomics studies. With a prologue by Sir Tom Blundell.
Protein Protein And Domain Domain Interactions
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Author : Pandjassarame Kangueane
language : en
Publisher: Springer
Release Date : 2018-02-16
Protein Protein And Domain Domain Interactions written by Pandjassarame Kangueane and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-16 with Medical categories.
This book illustrates the importance and significance of the molecular (physical and chemical) and evolutionary (gene fusion) principles of protein-protein and domain-domain interactions towards the understanding of cell division, disease mechanism and target definition in drug discovery. It describes the complex issues associated with this phenomenon using cutting edge advancement in Bioinformatics and Bioinformation Discovery. The chapters provide current information pertaining to the types of protein-protein complexes (homodimers, heterodimers, multimer complexes) in context with various specific and sensitive biological functions. The significance of such complex formation in human biology in the light of molecular evolution is also highlighted using several examples. The chapters also describe recent advancements on the molecular principles of protein-protein interaction with reference to evolution towards target identification in drug discovery. Finally, the book also elucidates a comprehensive yet a representative description of a large number of challenges associated with the molecular interaction of proteins.
Predicting Protein Protein Interactions And Their Interacting Interfaces With Statistical Learning Techniques
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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.
Protein Protein Interactions And Networks
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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 Domain Linker Prediction A Direction For Detecting Protein Protein Interactions
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Author :
language : en
Publisher:
Release Date :
Protein Domain Linker Prediction A Direction For Detecting Protein Protein Interactions written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Protein chains are generally long and consist of multiple domains. Domains are the basic elements of protein structures that can exist, evolve, and function independently. The accurate and reliable identification of protein domains and their interactions has very important impacts in several protein research areas. The accurate prediction of protein domains is a fundamental stage in both experimental and computational proteomics. The knowledge of domains is an initial stage of protein tertiary structure prediction which can give insight into the way in which proteins work. The knowledge of domains is also useful in classifying proteins, understanding their structures, functions and evolution, and predicting proteinprotein interactions (PPI). However, predicting structural domains within proteins is a challenging task in computational biology. A promising direction of domain prediction is detecting inter-domain linkers and then predicting the reigns of the protein sequence in which the structural domains are located accordingly. Protein-protein interactions occur at almost every level of cell function. The identification of interaction among proteins and their associated domains provide a global picture of cellular functions and biological processes. It is also an essential step in the construction of PPI networks for human and other organisms. PPI prediction has been considered as a promising alternative to the traditional drug design techniques. The identification of possible viral-host protein interactions can lead to a better understanding of infection mechanisms and, in turn, to the development of several medication drugs and treatment optimization. In this work, a compact and accurate approach for inter-domain linker prediction is developed based solely on protein primary structure information. Then, inter-domain linker knowledge is used in predicting structural domains and detecting PPI. The research work in this dissertation can be summarized in three main contributions. The first contribution is predicting protein inter-domain linker regions by introducing the concept of amino acid compositional index and refining the prediction by using the Simulated Annealing optimization technique. The second contribution is identifying structural domains based on inter-domain linker knowledge. The inter-domain linker knowledge, represented by the compositional index, is enhanced by the incorporation of biological knowledge, represented by amino acid physiochemical properties, to develop a well-optimized Random Forest classifier for predicting novel domains and inter-domain linkers. In the third contribution, the domain information knowledge is utilized to predict protein-protein interactions. This is achieved by characterizing structural domains within protein sequences, analyzing their interactions, and predicting protein interactions based on their interacting domains. The experimental studies and the higher accuracy achieved is a valid argument in favor of the proposed framework.
Protein Interaction Networks In Health And Disease
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Author : Spyros Petrakis
language : en
Publisher: Frontiers Media SA
Release Date : 2016-10-19
Protein Interaction Networks In Health And Disease written by Spyros Petrakis and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-19 with Genetics categories.
The identification and mapping of protein-protein interactions (PPIs) is a major goal in systems biology. Experimental data are currently produced in large scale using a variety of high-throughput assays in yeast or mammalian systems. Analysis of these data using computational tools leads to the construction of large protein interaction networks, which help researchers identify novel protein functions. However, our current view of protein interaction networks is still limited and there is an active field of research trying to further develop this concept to include important processes: the topology of interactions and their changes in real time, the effects of competition for binding to the same protein region, PPI variation due to alternative splicing or post-translational modifications, etc. In particular, a clinically relevant topic for development of the concept of protein interactions networks is the consideration of mutant isoforms, which may be responsible for a pathological condition. Mutations in proteins may result in loss of normal interactions and appearance of novel abnormal interactions that may affect a protein’s function and biological cycle. This Research Topic presents novel findings and recent achievements in the field of protein interaction networks with a focus on disease. Authors describe methods for the identification and quantification of PPIs, the annotation and analysis of networks, considering PPIs and protein complexes formed by mutant proteins associated with pathological conditions or genetic diseases.
Computational Prediction Of Protein Protein Interaction
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Author : Ranjan Kumar Barman
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2011-12
Computational Prediction Of Protein Protein Interaction written by Ranjan Kumar Barman and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12 with categories.
Protein-protein interactions are extremely valuable towards protein functions and cellular processes or we can say that protein-protein interactions play an important role in living cells. Therefore, if we can control the interactions between proteins, as a result, we can control some functionality of cells. Main functionality of a protein is carried out by its domains. So, domain is a structural or/and functional unit of protein. Behind protein-protein interactions there exist some domain-domain interactions. Therefore, under standing protein-protein interaction at domain level gives a global view of protein-protein interaction network.In this book, we have made an attempt to infer domain-domain interactions from interacting and non-interacting protein pairs then we have predicted protein-protein interactions based on inferred domain-domain interactions.
Membrane Binding And Mechanistic Insights Into The Phosophoinositide Interacting Protein Domains
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Author : Debasis Manna
language : en
Publisher:
Release Date : 2008
Membrane Binding And Mechanistic Insights Into The Phosophoinositide Interacting Protein Domains written by Debasis Manna and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
A Computational Study Of The Role Of Conserved Domains In Protein Interactions Microform
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Author : Doron Betel
language : en
Publisher: Library and Archives Canada = Bibliothèque et Archives Canada
Release Date : 2005
A Computational Study Of The Role Of Conserved Domains In Protein Interactions Microform written by Doron Betel and has been published by Library and Archives Canada = Bibliothèque et Archives Canada this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Complex organisms that are capable of inter-cellular communication and occupy various ecological niches are believed to evolve through the generation of novel cellular pathways. The myriad of processes in a cell are facilitated by proteins that form the building blocks of complex pathways through a set of carefully orchestrated interactions between functionally conserved regions in the proteins. The central notion that underlies this work is that these conserved elements of the proteins (domains) are the basic units of interaction. The objective of this thesis is to explore the role of domains in determining the interactions between proteins. The thesis outlines the necessary computational infrastructure for domain annotation and a number of computational methods that investigate the role of domains in protein interactions from visual, large-scale and individual perspectives. The first of these methods is a graphical program for the depiction of domains in a set of interacting proteins. This provides a visual tool to classify proteins and identify common elements. In the second study, protein complexes are used to identify domain pairs that co-occur in concert in a statistically significant manner. These domain co-occurrences are used to generate a network of domain correlations that represent functional networks in contrast to protein interaction networks. Such networks provide insight into new functional relationships between domains that are otherwise non-obvious and represent a first approximation of domain-domain interactions. Domain correlations are also used to analyze and compare datasets of protein complexes that are either curated or generated via high-throughput experiments. In the final study, the binding specificity of domains is inferred from a combination of protein structure complexes and other experimental interactions. The binding motifs are extracted from 3D structures with interacting domains and converted to a more informative PSSM representation by the use of the Gibbs sampling algorithm. The resulting domain binding-profiles are used to predict novel interactions for a number of proteins as well as to predict interactions within protein complexes.