MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains
<p>Abstract</p> <p>Background</p> <p>Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-05-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/11/243 |
| _version_ | 1811295744941883392 |
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| author | Tonikian Raffi Mok Janine Kim Philip M Lam Hugo YK Sidhu Sachdev S Turk Benjamin E Snyder Michael Gerstein Mark B |
| author_facet | Tonikian Raffi Mok Janine Kim Philip M Lam Hugo YK Sidhu Sachdev S Turk Benjamin E Snyder Michael Gerstein Mark B |
| author_sort | Tonikian Raffi |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic domains. One straightforward way of identifying these interactions is by scanning for matches to the motif against all the sequences in a target proteome. However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy.</p> <p>Results</p> <p>We developed an efficient search algorithm to scan the target proteome for potential domain targets and to increase the accuracy of each hit by integrating a variety of pre-computed features, such as conservation, surface propensity, and disorder. The integration is performed using naïve Bayes and a training set of validated experiments.</p> <p>Conclusions</p> <p>By integrating a variety of biologically relevant features to predict domain targets, we demonstrated a notably improved prediction of modular protein domain targets. Combined with emerging high-resolution data of domain specificities, we believe that our approach can assist in the reconstruction of many signaling pathways.</p> |
| first_indexed | 2024-04-13T05:38:05Z |
| format | Article |
| id | doaj.art-f963be0018a44927a2ecbb30a8f94451 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T05:38:05Z |
| publishDate | 2010-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f963be0018a44927a2ecbb30a8f944512022-12-22T03:00:13ZengBMCBMC Bioinformatics1471-21052010-05-0111124310.1186/1471-2105-11-243MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein DomainsTonikian RaffiMok JanineKim Philip MLam Hugo YKSidhu Sachdev STurk Benjamin ESnyder MichaelGerstein Mark B<p>Abstract</p> <p>Background</p> <p>Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic domains. One straightforward way of identifying these interactions is by scanning for matches to the motif against all the sequences in a target proteome. However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy.</p> <p>Results</p> <p>We developed an efficient search algorithm to scan the target proteome for potential domain targets and to increase the accuracy of each hit by integrating a variety of pre-computed features, such as conservation, surface propensity, and disorder. The integration is performed using naïve Bayes and a training set of validated experiments.</p> <p>Conclusions</p> <p>By integrating a variety of biologically relevant features to predict domain targets, we demonstrated a notably improved prediction of modular protein domain targets. Combined with emerging high-resolution data of domain specificities, we believe that our approach can assist in the reconstruction of many signaling pathways.</p>http://www.biomedcentral.com/1471-2105/11/243 |
| spellingShingle | Tonikian Raffi Mok Janine Kim Philip M Lam Hugo YK Sidhu Sachdev S Turk Benjamin E Snyder Michael Gerstein Mark B MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains BMC Bioinformatics |
| title | MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains |
| title_full | MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains |
| title_fullStr | MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains |
| title_full_unstemmed | MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains |
| title_short | MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains |
| title_sort | motips automated motif analysis for predicting targets of modular protein domains |
| url | http://www.biomedcentral.com/1471-2105/11/243 |
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