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#acl BaderLabGroup:read,write,revert,delete All: | #acl All:read == PDZ Domain-Peptide Interaction Prediction == |
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[[TableOfContents()]] | <<TableOfContents>> |
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== Goals == * Predict specificity of peptide recognition domain from the primary amino acid sequence. * Analyze PDZ, WW and then SH3 domains |
=== Background === The human genome contains approximately 26,000 protein-coding genes, which through alternative splicing can direct the synthesis of thousands of different proteins. The majority of these proteins interact with other proteins to coordinate a variety of cellular processes including DNA replication, cell cycle control, and signal transduction. The ability to accurately detect these interactions enables the assembly of protein interaction networks which can be used to better understand and study the biochemistry of the cell. |
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== Strategy == ## [wiki:/Strategy Strategy Log] |
=== Computational PPI Prediction === Computational methods to predict (protein protein interactions) PPIs have been developed and can be used to support or prioritize experiments. Such methods fall into a range of categories from physics to statistics-based method, however they all face several challenges. For physics-based prediction methods, the structures of the proteins are often unavailable or protein flexibility is not taken into consideration. Sequence based methods like PWMs can only represent short binding motifs and often do not account for interdependencies between residues and positions. In general, the computational prediction of PPIs is considered an extremely difficult problem that is not fully addressed by any existing method. |
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== Status == * [wiki:/Log Status Log] |
Many PPIs are mediated by peptide recognition domains (PRDs), which are evolutionary conserved modular interaction domains often found combined in different ways to form larger proteins. Proteins containing PRDs are used by the cell for numerous processes such as the co-localization of proteins, regulation of signaling processes or recognition of protein post-translational modifications. Interactions usually occur through the recognition of short linear sequences in the target protein such as proline-rich or C terminal motifs. Because of their simpler binding sites and straightforward modes of target recognition, it is easier to computationally predict peptide-PRD interactions than it is to predict PPIs more generally. === Computational Prediction of PDZ Domain Interactions === The PSD95/DlgA/Zo-1 (PDZ) domain is an ideal model for studying the computational prediction of peptide-PRD interactions since they are have important biological roles, are well studied and one of the simplest binding sites among PRDs. PDZ domains are found in bacteria, yeast, plants, and metazoans with 250 found in humans. They often interact with ion channels, adhesion molecules, and neurotransmitter receptors in signaling and scaffolding proteins. The biological roles include maintaining cell polarity, facilitating signal coupling, and regulating synaptic development. Their importance is emphasized, as mutations of the PDZ domain in different proteins have been associated with various diseases. ==== Sequence Based Prediction ==== Recently, two high through put experiments have been performed to study different PDZ domains. This has enabled the development of computational predictors of PDZ domain interactions. My current project focuses on using a machine learning method called support vector machines to computationally predict PDZ domain interactions directly from a given proteome. [[Data/PDZProteomeScanning|[Read More]]] ==== Sequence and Structure Based Prediction ==== Work in progress [[/Strategy|[Read More]]] ## == Goals == ## * Computationally predict specificity of peptide recognition domain from the primary amino acid sequences ## * Analyze PDZ, WW and then SH3 domains ## == Background == ## * [[/PDZ|PDZ Domains]] ## * [[/MachineLearning|Machine Learning]] ## == Strategy == ## * [[/Strategy|Strategy]] ## == Ideas == ## * [[/Ideas|Ideas]] ## == Data == ## * [[/PDZData|PDZ Data]] ## == Experiments == ## * [[/Experiments|Experiments and Results]] ## == Status == ## * [[/Log|Status]] |
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## * [wiki:/Meeting Notes] | ## * [[/Meeting|Notes]] ## == Tools/Resources == ## * [[/ToolsResources|Tools and Resources]] ## == Reading Notes == ## * [[/../ShirleyHui/MBCReadings|Molecular Biology of the Cell]] ## * [[/../ShirleyHui/PPIReadings|Protein-protein Interaction Detection]] ## * Support Vector Machines ## == Related Literature == ## * [[http://www.connotea.org/rss/user/s2hui?download=view|Literature List on Connotea]] ## * [[http://www.baderlab.org/DomainSpecificityPredictionProject/Reading|Molecular Biology of the Cell]] |
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== Tools/Resources == === Domains === * [wiki:/PDZ PDZ Domain] === Databases === * [http://www.ensembl.org/ Ensembl] * Software system which produces and maintains automatic annotation on selected eukaryotic genomes. * [http://www.ebi.ac.uk/interpro/ InterPro] * Database of protein families, domains and functional sites in which identifiable features found in known proteins can be applied to unknown protein sequences. * [http://www.biomart.org/ BioMart] * Query-oriented data management system that simplifies the task of creation and maintenance of advanced query interfaces backed by a relational database. It is particularly suited for providing the 'data mining' like searches of complex descriptive (e.g. biological) data. === Sequence Alignment === ==== Multiple ==== ===== Hierarhical Methods ===== * [http://www.compbio.dundee.ac.uk/Software/Amps/amps.html/ AMPS] 1990 * Calculates Z-scores through pairwise sequences comparison with randomization * Generates alignments without having to generate trees * [http://www.ebi.ac.uk/clustalw/ ClustalW] 1997 * Uses a series of different pair-score matrices * Biases location of gaps based on secondary structure mask * Allows for realigning to refine the alignment * Can infer phylogeny * Problems: * Time required to complete first all against all comparison to create guide tree * [http://www.drive5.com/muscle/ MUSCLE] 2004 * MUltiple Sequence Comparison by Log-Expectation * Uses a quick hashing comparison based on identical matches * [http://www.biophys.kyoto-u.ac.jp/~katoh/programs/align/mafft/ MAFFT] 2005 * Calculates guide tree faster by using fast Fourier transform method on AA properites to identify regions of similarity * Uses these regions to guide dynamic programming alignment of the sequences ===== Non Hierarchical Methods ===== * [http://www.ncbi.nlm.nih.gov/BLAST/ PSI-BLAST] 1997 * Searches a database with a single sequence * High scoring sequences are built into a multiple alignment which is used to derive a search profile for subsequent search of the database * Repeat until no new sequences are added to the profile or a specified number of iterations have been performed * [http://tcoffee.vital-it.ch/cgi-bin/Tcoffee/tcoffee_cgi/index.cgi T-Coffee] 2000 * Builds a library of pairwise alignments for the sequences of interest * Uses library to inform hierarchical method to find a multiple alignment that preserves consistency between the pairwise alignments * Can align sequences of varying lengths * [http://baboon.math.berkeley.edu/amap/ AMAP] 2007 * Multiple sequence alignment by sequence annealing ===== Probabilistic Methods ===== * [http://probcons.stanford.edu/ Probcons] 2005 * [http://probalign.njit.edu/probalign/login ProbAlign] 2006 * Estimates amino acid posterior probabilities using a partition function of the alignments. * Computes the maximum expected accuracy alignment after applying the probability consistency transformation of Probcons. * Improvements best seen with datasets of variable and long length sequences. === Viewers === * [http://www.jalview.org/ JalView] * Multiple alignment viewer/editor written in Java == Background Literature == [http://www.connotea.org/rss/user/s2hui?download=view Literature List on Connotea] === Textbook === * [http://www.baderlab.org/DomainSpecificityPredictionProject/Reading Molecular Biology of the Cell] === Other === * http://proteinkeys.org |
PDZ Domain-Peptide Interaction Prediction
Table of Contents
Contents
Background
The human genome contains approximately 26,000 protein-coding genes, which through alternative splicing can direct the synthesis of thousands of different proteins. The majority of these proteins interact with other proteins to coordinate a variety of cellular processes including DNA replication, cell cycle control, and signal transduction. The ability to accurately detect these interactions enables the assembly of protein interaction networks which can be used to better understand and study the biochemistry of the cell.
Computational PPI Prediction
Computational methods to predict (protein protein interactions) PPIs have been developed and can be used to support or prioritize experiments. Such methods fall into a range of categories from physics to statistics-based method, however they all face several challenges. For physics-based prediction methods, the structures of the proteins are often unavailable or protein flexibility is not taken into consideration. Sequence based methods like PWMs can only represent short binding motifs and often do not account for interdependencies between residues and positions. In general, the computational prediction of PPIs is considered an extremely difficult problem that is not fully addressed by any existing method.
Many PPIs are mediated by peptide recognition domains (PRDs), which are evolutionary conserved modular interaction domains often found combined in different ways to form larger proteins. Proteins containing PRDs are used by the cell for numerous processes such as the co-localization of proteins, regulation of signaling processes or recognition of protein post-translational modifications. Interactions usually occur through the recognition of short linear sequences in the target protein such as proline-rich or C terminal motifs. Because of their simpler binding sites and straightforward modes of target recognition, it is easier to computationally predict peptide-PRD interactions than it is to predict PPIs more generally.
Computational Prediction of PDZ Domain Interactions
The PSD95/DlgA/Zo-1 (PDZ) domain is an ideal model for studying the computational prediction of peptide-PRD interactions since they are have important biological roles, are well studied and one of the simplest binding sites among PRDs. PDZ domains are found in bacteria, yeast, plants, and metazoans with 250 found in humans. They often interact with ion channels, adhesion molecules, and neurotransmitter receptors in signaling and scaffolding proteins. The biological roles include maintaining cell polarity, facilitating signal coupling, and regulating synaptic development. Their importance is emphasized, as mutations of the PDZ domain in different proteins have been associated with various diseases.
Sequence Based Prediction
Recently, two high through put experiments have been performed to study different PDZ domains. This has enabled the development of computational predictors of PDZ domain interactions. My current project focuses on using a machine learning method called support vector machines to computationally predict PDZ domain interactions directly from a given proteome. [Read More]
Sequence and Structure Based Prediction
Work in progress [Read More]
Team
- Shirley Hui
- Gary Bader