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A Correlated Motif Approach for Finding Short Linear Motifs from Protein-Protein Interaction Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Soon-Heng Willy, Hugo Wing-Kin, Sung See-Kiong |
| Copyright Year | 2006 |
| Abstract | Background: An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on a protein sequence set interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches. Results: We propose a novel approach of finding correlated short sequence motifs from protein-protein interaction data to effectively circumvent the above-mentioned limitations. Correlated motifs are those motifs that consistently co-occur only in pairs of interacting protein sequences which could possibly interact with each other directly or indirectly to mediate interactions. We adopted the (l, d)-motif model and formulate finding the correlated motifs as an (l, d)-motif pair finding problem. We present both an exact algorithm, D-MOTIF, as well as its approximation algorithm, D-STAR to solve this problem. Evaluation on extensive simulated data showed that our approach not only eliminated the need for any prior protein grouping, but is also more robust in extracting motifs from noisy interaction data. Application on two biological datasets (SH3 interaction network and TGFβ signaling network) demonstrates that the approach can extract correlated motifs that correspond to actual interacting |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.comp.nus.edu.sg/~ksung/papers/D-Star_BMC-bioinformatics/bmc_DSTAR-191006-camready-01.pdf |
| Alternate Webpage(s) | https://www.comp.nus.edu.sg/~ksung/papers/D-Star_BMC-bioinformatics/bmc_DSTAR-191006-camready-01.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
