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| == Your Name == | == Structure based proteome scanning prediction of PDZ domain peptide interactions == |
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| Email: <<MailTo(you AT SPAMFREE example DOT com)>> | Shirley Hui, Xiang Xing, and Gary D. Bader |
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| ... | === Website === * URL goes here |
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| === Background === PDZ domains are peptide recognition domains that are involved in important biological processes that bind their targets through the recognition of simple linear motifs. The recent availability of high throughput PDZ domain peptide interaction data has prompted the development of sequence based predictors of PDZ domain peptide interactions. However, the performance of these predictors depends on how similar in sequence a given domain is to the training domains. On the other hand, domain structure features are known to play roles in determining PDZ domain binding specificity and can also be used for training. When used for proteome scanning, such a predictor may be able to predict more novel interactions and increase the coverage of PDZ domain mediated protein protein interactions that can be currently predicted. === Results === We developed a structure based predictor of PDZ domain peptide interactions. We use domain structure features for training which are known to facilitate protein folding and stability and protein interactions. We also computationally generate additional negative interactions for training and show that this reduces the number of potential false positives returned by the predictor. Through multiple cross validation strategies and a series of blind tests we show that the predictor is estimated to have improved generalization performance and can correctly predict interactions in different organisms. Through proteome scanning in human we show that the structure based predictions correspond to known PDZ domain peptide interactions and known protein protein interactions in curated databases. We also show that a large number of validated hits are novel, representing a 53% increase in PDZ domain mediated PPIs that could be predicted before. A functional enrichment analysis shows that the biological process terms associated with these hits are also novel. === SVM Predictions === SVM predictions were validated using known interactions from PDZBase, a domain peptide interaction database and known protein-protein interactions (PPIs) from iRefIndex. iRefIndex is a PPI database which consolidates PPIs from different databases including BIND, BioGRID, CORUM, DIP, HPRD, IntAct, MINT. The following are SVM proteome scanning predictions for 175 human, 7 fly and 6 worm PDZ domains. * [[attachment:HumanPredictions.zip|Human 175 (zip)]] * [[attachment:FlyPredictions.zip|Fly 7 (zip)]] * [[attachment:WormPredictions.zip|Worm 6 (zip)]] === Supplementary === === Source Code === === Team === * Shirley Hui * Xiang Xing * Gary Bader |
Structure based proteome scanning prediction of PDZ domain peptide interactions
Shirley Hui, Xiang Xing, and Gary D. Bader
Website
- URL goes here
Background
PDZ domains are peptide recognition domains that are involved in important biological processes that bind their targets through the recognition of simple linear motifs. The recent availability of high throughput PDZ domain peptide interaction data has prompted the development of sequence based predictors of PDZ domain peptide interactions. However, the performance of these predictors depends on how similar in sequence a given domain is to the training domains. On the other hand, domain structure features are known to play roles in determining PDZ domain binding specificity and can also be used for training. When used for proteome scanning, such a predictor may be able to predict more novel interactions and increase the coverage of PDZ domain mediated protein protein interactions that can be currently predicted.
Results
We developed a structure based predictor of PDZ domain peptide interactions. We use domain structure features for training which are known to facilitate protein folding and stability and protein interactions. We also computationally generate additional negative interactions for training and show that this reduces the number of potential false positives returned by the predictor. Through multiple cross validation strategies and a series of blind tests we show that the predictor is estimated to have improved generalization performance and can correctly predict interactions in different organisms. Through proteome scanning in human we show that the structure based predictions correspond to known PDZ domain peptide interactions and known protein protein interactions in curated databases. We also show that a large number of validated hits are novel, representing a 53% increase in PDZ domain mediated PPIs that could be predicted before. A functional enrichment analysis shows that the biological process terms associated with these hits are also novel.
SVM Predictions
SVM predictions were validated using known interactions from PDZBase, a domain peptide interaction database and known protein-protein interactions (PPIs) from iRefIndex. iRefIndex is a PPI database which consolidates PPIs from different databases including BIND, BioGRID, CORUM, DIP, HPRD, IntAct, MINT.
The following are SVM proteome scanning predictions for 175 human, 7 fly and 6 worm PDZ domains.
Supplementary
Source Code
Team
- Shirley Hui
- Xiang Xing
- Gary Bader