#acl All:read == PDZ Domain-Peptide Interaction Prediction == == Table of 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 === Several 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. This 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]]] ==== Structure Based Prediction ==== While the previously developed sequence based predictor is able to more accurately and precisely scan proteomes of different organisms for PDZ domain binders, its performance relies on the sequence similarity between testing and training domains. On the other hand, it is known that the domain structure can play a big role in determining PDZ domain binding specificity. Therefore, we developed a structure based predictor of PDZ domain peptide interactions which is trained using PDZ domain structure features. [[Data/StructurePDZProteomeScanning|[Read More]]] ==== POW! PDZ Domain-Peptide Interaction Prediction ==== The sequence-based and structure-based predictors are now available via a website called POW! and support the majority of human, mouse, fly and worm PDZ domains. POW! Website: http://webservice.baderlab.org/domains/POW/ A simple command line user interface that allows users to run POW! locally on their computer is also available. It is written in Java and can be downloaded below. Please unzip the file below and consult the Readme for more details. POW! CLUI: [[attachment:POWCLUI.zip|POWCLUI.zip]] ## == 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]] == Team == * Shirley Hui * Gary Bader ---- CategoryProject