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| == Goals == * Predict specificity of peptide recognition domain from the primary amino acid sequence. * Analyze PDZ, WW and then SH3 domains |
== Peptide Recognition Domain Interaction Prediction == |
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| == Strategy == | == Table of Contents == <<TableOfContents>> |
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| == Status == | === 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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| == Tasks == | === Computation 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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| 1. Learn SVN, Brain code (ResidueResidueCorrelation) 1. Literature review related to domain specificity (background activity) 1. Run ResidueResidue correlation analysis on PDZ domain data: 1-1 version + try others e.g. 1-2 (Requires: PDZ profiles from Gary) 1. Implement new feature: amino acid groups (learn amino acid groups) + run on PDZ data 1. Think about new PDZ domain features that can be used for prediction. |
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. |
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| == Ideas == * Use of structural data (PDZ domain structures) (may require homology modeling) * Use of machine learning methods (SVM for classification and boosting decision tree for interpretable learning model) * Analysis of correlation within domain and peptide (inter-residue correlation) maybe correspondence analysis |
=== 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. 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. [[/PDZInteractionPredictionProject|[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]] ## == Tasks == ## ## 1. --(Learn SVN, Brain code (!ResidueResidueCorrelation))-- ## 1. Literature review related to domain specificity (background activity), PDZ domains (from Ioana's project) ## 1. --(Run !ResidueResidue correlation analysis on PDZ domain data: 1-1 version + try others e.g. 1-2 (Requires: PDZ profiles from Gary))-- ## 1. MSA subproject ## 1. --(Learn basics of multiple sequence alignment (Baxevanis, chapter 12))-- ## 1. Find and evaluate MSA algorithms (compare notes with Stacy) + evaluate Superfamily, PFAM databases of protein family alignments ## 1. Try different multiple sequence alignment algorithms (MSA) on the PDZ domain sequences to see if they affect the correlation results. ## 1. Benchmark/validate correlation subproject ## 1. We know H (PDZ), T @-2 (peptide) correlation ## 1. Look at structures (e.g. 1N7T and 1BE9) to see if correlated residues/positions are close to each other and compatible (physicochemically). We need to focus on ## PDZ structures that have bound peptides (search in PDB) ## 1. Build set of known true and false correlations for use in evaluating prediction algorithm (Note: also ask Dev Sidhu, when available). See [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=10871264 Baldi et al. review] ## 1. Amino acid group subproject ## 1. Learn about amino acid groups ## 1. Define an initial aa grouping (reasonable grouping from Levy paper) ## 1. Add new feature to !ResidueResidueCorrelation class so it considers grouping + run on PDZ data. This involves implementing the groups as a reduced alphabet (amino acids in a group are considered equivalent) ## 1. Try all groupings to see how it affects the results (from Levy paper) ## 1. See if we can incorporate aa similarity defined by substitution matrix approach (e.g. BLOSUM, PAM, GONNET) into our method, instead of grouping ## 1. Similarly, evaluate aa similarity defined by factor analysis (Atchley et al paper) ## 1. Think about new PDZ domain features that can be used for prediction. ## == Ideas == ## * [wiki:/MachineLearning Machine Learning Page] ## * With current correlation counting calculation, Weight calculation by how many peptides are in the peptides file (i.e. normalize the correlation calculation in some way) ## * Build tools to help interpret correlations in the context of multiple sequence alignments (and later structures). ## * Use of structural data (PDZ domain structures) (may require homology modeling) ## * Use of machine learning methods (SVM for classification and boosting decision tree for interpretable learning model) ## * Analysis of correlation within domain and peptide (inter-residue correlation) maybe correspondence analysis ## * Analysis of SNPs and how they affect domain binding (including correlations between SNPs) ## * Define the binding site of the PDZ domain based on phage display data. Given that identical binding sites between two PDZ domains should correspond to identical ## binding specificities, find the set of PDZ domain sites that correlate perfectly with binding specificity. ## == Courses == ## === Biology === ## * [http://bio250y.chass.utoronto.ca/ BIO250] - Cell and Molecular Biology ## * Classes: Tues/Thurs - 1-2 PM (Convocation Hall) OR Mon - 6-8 PM (MC 102-Mechanical Engineering Building) ## * Textbook: [http://www.amazon.com/Molecular-Biology-Fourth-Bruce-Alberts/dp/0815332181/ref=pd_sim_b_1/105-5132391-0345258?ie=UTF8&qid=1188913552&sr=1-4 Molecular Biology of the Cell 4th Ed.] Alberts et al. ## === Protein Structure === ## * BCH340H1 - Proteins: from Structure to Proteomics ## * Classes: Winter 2008 ## * Textbook: ? ## * Previous Course Web Pages: ## * [http://arrhenius.med.utoronto.ca/~chan/bch340h04-outline.html 2004 Chan] ## * [http://xtal.uhnres.utoronto.ca/prive/BCH340/ 2006 Prive] ## === Machine Learning === ## * CSC2515 - Machine Learning ## * Previous Course Web Pages: ## * [http://www.cs.toronto.edu/~roweis/csc2515/ 2003-2006 Roweis] ## == Committee Meetings == ## * [[/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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== Documents == == Background Literature == |
* Shirley Hui * Gary Bader |
Peptide Recognition Domain 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.
Computation 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.
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]
Team
- Shirley Hui
- Gary Bader