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| == Motivation == PDZ domains mediate important biological processes through the recognition of short linear motifs. Two recent independent high through put protein microarray and phage display experiments have been used to detect PDZ domain interactions. Several computational predictors of PDZ domain interactions have also been developed, however they are trained using only protein microarray data or focus on limited subsets of PDZ domains. An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require not only an accurate but precise predictor due to the thousands of possible interactors in a given proteome. However, once validated these predictions would increase the coverage of current PDZ domain interaction networks and further our understanding of the biologically processes they mediate. |
== Background == An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will greatly increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes. |
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| We developed a PDZ domain interaction predictor using SVMs trained with both protein microarray and phage display data. In order to use the phage display data for training, we developed a method to deterministically generate artificial negative interactions for the phage display data since it consisted of positive interactions only. Through extensive blind testing we showed that the SVM could predict interactions in different organisms. We then used the SVM to scan the proteomes of different organisms to predict binders for several PDZ domains. Predictions were validated using PDZBase or protein microarray data and a comparison of F1 measures and FPRs between the SVM and published or commonly used predictors demonstrated the SVM’s improved accuracy and precision. | We built an SVM using mouse and human experimental training data to predict PDZ domain interactions. We showed that it correctly predicts known interactions from proteomes of different organisms and compared to published state of art predictors, is more accurate and precise. |
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| == Supplementary Data == a. Supplementary Document (Link) a. PDZSVM Data Files [[attachment:PDZSVMData.zip]] |
== Supplementary == a. Supplementary Information (pdf) a. [[attachment:PDZSVMData.zip|Data Files (zip)]] |
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| == Java Implementation == a. Source and Binaries * License: [[attachment:PDZSVM_LICENSE.txt]] * Code: [[attachment:PDZSVM_1.0_src.zip]] * Classes [[attachment:PDZSVM_1.0.jar]] a. Dependencies [[attachment:PDZSVMDep.zip]] * jfreechart 1.0.12 (and dependencies) * weka 3.9.1 * auc calculator (Davis & Goadrich, 2006) * !BioJava 1.5 * iText 2.1.3 * jmatio * BRAIN 1.0.5 (pdzsvm) * libSVM 2.8.9 (pdzsvm) |
== Source Code == *[[attachment:PDZSVM_LICENSE.txt|GNU LGP License (txt)]] *[[attachment:PDZSVM_1.0_src.zip|Java Code (zip)]] *[[attachment:PDZSVMDep.zip|Dependency Jars (zip)]] * jfreechart 1.0.12 (and dependencies) * weka 3.9.1 * auc calculator (Davis & Goadrich, 2006) * !BioJava 1.5 * iText 2.1.3 * jmatio * BRAIN 1.0.5 (pdzsvm) * libSVM 2.8.9 (pdzsvm) |
Proteome scanning of PDZ domain interactions using support vector machines
Background
An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will greatly increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes.
Results
We built an SVM using mouse and human experimental training data to predict PDZ domain interactions. We showed that it correctly predicts known interactions from proteomes of different organisms and compared to published state of art predictors, is more accurate and precise.
Supplementary
- Supplementary Information (pdf)
- Models
- Chen model parameter and binding site encoding files
- Stiffler model parameter files
- Proteomes
- Ensembl proteome files for Human, Worm and Fly
- Experiment Interaction files (in peptide file format)
- Fly files from Chen
- Human files from Sidhu
- Mouse files from Stiffler
- Worm files from Chen
- Curated Interaction files (flat files)
- PDZBase for Human (Worm and Fly included, but not used)
- Human Protein Reference Database
- Phage codon bias files
- Models
Source Code
- jfreechart 1.0.12 (and dependencies)
- weka 3.9.1
auc calculator (Davis & Goadrich, 2006)
BioJava 1.5
- iText 2.1.3
- jmatio
- BRAIN 1.0.5 (pdzsvm)
- libSVM 2.8.9 (pdzsvm)
Team
- Shirley Hui
- Gary Bader