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| Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, contiguous sequence motifs which can be identified using high-throughput experimental screens such as phage display and then used to computationally predict protein interactions mediated by these domains. Most protein-protein interaction prediction approaches either lack the ability to predict peptide recognition module mediated interactions or they do not take into account different constraints governing physiologically relevant interactions between two proteins. | Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein-protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins. |
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| A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein-protein interactions in \textit{S. cerevisae} using phage display data is presented. This method is based on the fact that domain-peptide mediated interactions do not occur in isolation. They are influenced by many sequential and cellular constraints. Therefore, by combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy (F-score) between 0.98 and 0.96 for different thresholds. | A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein-protein interactions in ''S. cerevisae'' using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy (F-score) between $0.98$ and $0.96$ across a range of interaction score thresholds. |
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''DoMo-Pred'': (April 21, 2015) <<BR>> Source: ==== Datasets ==== |
Source: [[attachment:DoMo-Pred.zip]] <<BR>> Binary: [[attachment:DoMo-Pred-Binary.zip]] |
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| [[attachment:SH3_PPI_Predictions.zip]] Text file format: || Domain || Peptide || Start || Stop || Sequence || Score || || P11710 || P53861 || 313 || 318 || RTTSH ||1.0 || || P11710 || P32909 || 80 || 85 || RTSSL ||1.0 || || ... || ... || ... || ... || ... ||... || |
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| [[attachment:supplementary.pdf]] |
Predicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast
Shobhit Jain and Gary Bader
Motivation
Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein-protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins.
Results
A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein-protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy (F-score) between $0.98$ and $0.96$ across a range of interaction score thresholds.
Downloads
Latest Release
Source: DoMo-Pred.zip
Binary: DoMo-Pred-Binary.zip
Predictions
Text file format:
Domain |
Peptide |
Start |
Stop |
Sequence |
Score |
P11710 |
P53861 |
313 |
318 |
RTTSH |
1.0 |
P11710 |
P32909 |
80 |
85 |
RTSSL |
1.0 |
... |
... |
... |
... |
... |
... |