Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone
The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequen...
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2011
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| Online Access: | http://hdl.handle.net/1721.1/66190 https://orcid.org/0000-0003-4074-8980 https://orcid.org/0000-0002-2724-7228 |
| _version_ | 1811087052865798144 |
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| author | Gutwin, Karl Keating, Amy E. Trigg, Jason Berger Leighton, Bonnie |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Gutwin, Karl Keating, Amy E. Trigg, Jason Berger Leighton, Bonnie |
| author_sort | Gutwin, Karl |
| collection | MIT |
| description | The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from http://multicoil2.csail.mit.edu. |
| first_indexed | 2024-09-23T13:39:02Z |
| format | Article |
| id | mit-1721.1/66190 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:39:02Z |
| publishDate | 2011 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/661902022-09-28T15:17:32Z Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone Gutwin, Karl Keating, Amy E. Trigg, Jason Berger Leighton, Bonnie Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biology Massachusetts Institute of Technology. Department of Mathematics Berger, Bonnie Trigg, Jason A. Gutwin, Karl Keating, Amy E. Berger, Bonnie The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from http://multicoil2.csail.mit.edu. National Institutes of Health (U.S.) (Grant 1R01GM081871) National Science Foundation (U.S.) (Grant MCB-0347203) National Science Foundation (U.S.) (Grant 0821391) 2011-10-05T17:34:39Z 2011-10-05T17:34:39Z 2011-08 2011-04 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/66190 Trigg, Jason et al. “Multicoil2: Predicting Coiled Coils and Their Oligomerization States from Sequence in the Twilight Zone.” Ed. Ozlem Keskin. PLoS ONE 6 (8) (2011): e23519. © 2011 Trigg et al. https://orcid.org/0000-0003-4074-8980 https://orcid.org/0000-0002-2724-7228 en_US http://dx.doi.org/10.1371/journal.pone.0023519 PLoS one Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Gutwin, Karl Keating, Amy E. Trigg, Jason Berger Leighton, Bonnie Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone |
| title | Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone |
| title_full | Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone |
| title_fullStr | Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone |
| title_full_unstemmed | Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone |
| title_short | Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone |
| title_sort | multicoil2 predicting coiled coils and their oligomerization states from sequence in the twilight zone |
| url | http://hdl.handle.net/1721.1/66190 https://orcid.org/0000-0003-4074-8980 https://orcid.org/0000-0002-2724-7228 |
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