Towards A Better Understanding Of Protein Protein Interaction Networks
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Towards A Better Understanding Of Protein Protein Interaction Networks
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Author : Tatiana A. Gutiérrez-Bunster
language : en
Publisher:
Release Date : 2014
Towards A Better Understanding Of Protein Protein Interaction Networks written by Tatiana A. Gutiérrez-Bunster and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Proteins participate in the majority of cellular processes. To determine the function of a protein it is not sufficient to solely know its sequence, its structure in isolation, or how it works individually. Additionally, we need to know how the protein interacts with other proteins in biological networks. This is because most of the proteins perform their main function through interactions. This thesis sets out to improve the understanding of protein-protein interaction networks (PPINs). For this, we propose three approaches: (1) Studying measures and methods used in social and complex networks. The methods, measures, and properties of social networks allow us to gain an understanding of PPINs via the comparison of different types of network families. We studied models that describe social networks to see which models are useful in describing biological networks. We investigate the similarities and differences in terms of the network community profile and centrality measures. (2) Studying PPINs and their role in evolution. We are interested in the relationship of PPINs and the evolutionary changes between species. We investigate whether the centrality measures are correlated with the variability and similarity in orthologous proteins. (3) Studying protein features that are important to evaluate, classify, and predict interactions. Interactions can be classified according to different characteristics. One characteristic is the energy (that is the attraction or repulsion of the molecules) that occurs in interacting proteins. We identify which type of energy values contributes better to predicting PPIs. We argue that the number of energetic features and their contribution to the interactions can be a key factor in predicting transient and permanent interactions.
Predicting Transcription Factor Complexes
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Author : Thorsten Will
language : en
Publisher: Springer
Release Date : 2014-12-05
Predicting Transcription Factor Complexes written by Thorsten Will and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-05 with Science categories.
In his master thesis Thorsten Will proposes the substantial information content of protein complexes involving transcription factors in the context of gene regulatory networks, designs the first computational approaches to predict such complexes as well as their regulatory function and verifies the practicability using data of the well-studied yeast S.cereviseae. The novel insights offer extensive capabilities towards a better understanding of the combinatorial control driving transcriptional regulation.
Protein Protein Interaction Networks
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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 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.
Understanding Protein Protein Interaction Networks
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Author : Ariel Jaimovich
language : en
Publisher:
Release Date : 2010
Understanding Protein Protein Interaction Networks written by Ariel Jaimovich and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Protein-protein interactions categories.
Protein Interaction Networks
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Author : Rossen Donev
language : en
Publisher: Elsevier
Release Date : 2022-07-07
Protein Interaction Networks written by Rossen Donev and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-07 with Science categories.
Protein Interaction Networks, Volume 131 in the Advances in Protein Chemistry and Structural Biology series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Advances in Protein Chemistry and Structural Biology series Includes the latest information on protein design and structure
Algorithms For The Analysis Of Protein Interaction Networks
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Author : Rohit Singh (Ph.D.)
language : en
Publisher:
Release Date : 2012
Algorithms For The Analysis Of Protein Interaction Networks written by Rohit Singh (Ph.D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
In the decade since the human genome project, a major research trend in biology has been towards understanding the cell as a system. This interest has stemmed partly from a deeper appreciation of how important it is to understand the emergent properties of cellular systems (e.g., they seem to be the key to understanding diseases like cancer). It has also been enabled by new high-throughput techniques that have allowed us to collect new types of data at the whole-genome scale. We focus on one sub-domain of systems biology: the understanding of protein interactions. Such understanding is valuable: interactions between proteins are fundamental to many cellular processes. Over the last decade, high-throughput experimental techniques have allowed us to collect a large amount of protein-protein interaction (PPI) data for many species. A popular abstraction for representing this data is the protein interaction network: each node of the network represents a protein and an edge between two nodes represents a physical interaction between the two corresponding proteins. This abstraction has proven to be a powerful tool for understanding the systems aspects of protein interaction. We present some algorithms for the augmentation, cleanup and analysis of such protein interaction networks: 1. In many species, the coverage of known PPI data remains partial. Given two protein sequences, we describe an algorithm to predict if two proteins physically interact, using logistic regression and insights from structural biology. We also describe how our predictions may be further improved by combining with functional-genomic data. 2. We study systematic false positives in a popular experimental protocol, the Yeast 2-Hybrid method. Here, some "promiscuous" proteins may lead to many false positives. We describe a Bayesian approach to modeling and adjusting for this error. 3. Comparative analysis of PPI networks across species can provide valuable insights. We describe IsoRank, an algorithm for global network alignment of multiple PPI networks. The algorithm first constructs an eigenvalue problem that encapsulates the network and sequence similarity constraints. The solution of the problem describes a k-partite graph that is further processed to find the alignment. 4. For a given signaling network, we describe an algorithm that combines RNA-interference data with PPI data to produce hypotheses about the structure of the signaling network. Our algorithm constructs a multi-commodity flow problem that expresses the constraints described by the data and finds a sparse solution to it.
Structure Based Algorithms For Protein Protein Interaction Prediction
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Author : Raghavendra Hosur
language : en
Publisher:
Release Date : 2012
Structure Based Algorithms For Protein Protein Interaction Prediction written by Raghavendra Hosur and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. PPIs are critical in maintenance of cellular integrity, metabolism, transcription/ translation, and cell-cell communication. This thesis develops new methods that significantly advance our efforts at structure- based approaches to predict PPIs and boost confidence in emerging high-throughput (HTP) data. The aims of this thesis are, 1) to utilize physicochemical properties of protein interfaces to better predict the putative interacting regions and increase coverage of PPI prediction, 2) increase confidence in HTP datasets by identifying likely experimental errors, and 3) provide residue-level information that gives us insights into structure-function relationships in PPIs. Taken together, these methods will vastly expand our understanding of macromolecular networks. In this thesis, I introduce two computational approaches for structure-based proteinprotein interaction prediction: iWRAP and Coev2Net. iWRAP is an interface threading approach that utilizes biophysical properties specific to protein interfaces to improve PPI prediction. Unlike previous structure-based approaches that use single structures to make predictions, iWRAP first builds profiles that characterize the hydrophobic, electrostatic and structural properties specific to protein interfaces from multiple interface alignments. Compatibility with these profiles is used to predict the putative interface region between the two proteins. In addition to improved interface prediction, iWRAP provides better accuracy and close to 50% increase in coverage on genome-scale PPI prediction tasks. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly - processes known to be critical in cancer. Coev2Net addresses some of the limitations of iWRAP, and provides techniques to increase coverage and accuracy even further. Unlike earlier sequence and structure profiles, Coev2Net explicitly models long-distance correlations at protein interfaces. By formulating interface co-evolution as a high-dimensional sampling problem, we enrich sequence/structure profiles with artificial interacting homologus sequences for families which do not have known multiple interacting homologs. We build a spanning-tree based graphical model induced by the simulated sequences as our interface profile. Cross-validation results indicate that this approach is as good as previous methods at PPI prediction. We show that Coev2Net's predictions correlate with experimental observations and experimentally validate some of the high-confidence predictions. Furthermore, we demonstrate how analysis of the predicted interfaces together with human genomic variation data can help us understand the role of these mutations in disease and normal cells.
Protein Protein Interactions And Networks
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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.
Mining Biological Networks Towards Protein Complex Detection And Gene Disease Association
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Author : Eileen Marie Hanna
language : en
Publisher:
Release Date : 2015
Mining Biological Networks Towards Protein Complex Detection And Gene Disease Association written by Eileen Marie Hanna and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Data mining categories.
Large amounts of biological data are continuously generated nowadays, thanks to the advancements of high-throughput experimental techniques. Mining valuable knowledge from such data still motivates the design of suitable computational methods, to complement the experimental work which is often bound by considerable time and cost requirements. Protein complexes, or groups of interacting proteins, are key players in most cellular events. The identification of complexes not only allows to better understand normal biological processes but also to uncover disease-triggering malfunctions. Ultimately, findings in this research branch can highly enhance the design of effective medical treatments. The aim of this research is to detect protein complexes in protein-protein interaction networks and to associate the detected entities to diseases. The work is divided into three main objectives: first, develop a suitable method for the identification of protein complexes in static interaction networks; second, model the dynamic aspect of protein interaction networks and detect complexes accordingly; and third, design a learning model to link proteins, and subsequently protein complexes, to diseases. In response to these objectives, we present, ProRank+, a novel complex-detection approach based on a ranking algorithm and a merging procedure. Then, we introduce DyCluster, which uses gene expression data, to model the dynamics of the interaction networks, and we adapt the detection algorithm accordingly. Finally, we integrate network topology attributes and several biological features of proteins to form a classification model for gene-disease association. The reliability of the proposed methods is supported by various experimental studies conducted to compare them with existing approaches. ProRank+ detects more protein complexes than other state-of-the-art methods. DyCluster goes a step further and achieves a better performance than similar techniques. Then, our learning model shows that combining topological and biological features can greatly enhance the gene-disease association process. Finally, we present a comprehensive case study of breast cancer in which we pinpoint disease gene using our learning model; subsequently, we detect favorable groupings of those genes in a protein interaction network using the ProRank+ algorithm.