Simultaneous Alignment and Folding of Protein Sequences
Accurate comparative analysis tools for low-homology proteins remains a difficult challenge in computational biology, especially sequence alignment and consensus folding problems. We present partiFold-Align, the first algorithm for simultaneous alignment and consensus folding of unaligned protein se...
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| Format: | Article |
| Language: | en_US |
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Mary Ann Liebert
2015
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| Online Access: | http://hdl.handle.net/1721.1/100002 https://orcid.org/0000-0001-8253-7714 https://orcid.org/0000-0002-2724-7228 |
| _version_ | 1826205252783702016 |
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| author | O'Donnell, Charles William Will, Sebastian Devadas, Srinivas Backofen, Rolf Berger, Bonnie Waldispuhl, Jerome |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory O'Donnell, Charles William Will, Sebastian Devadas, Srinivas Backofen, Rolf Berger, Bonnie Waldispuhl, Jerome |
| author_sort | O'Donnell, Charles William |
| collection | MIT |
| description | Accurate comparative analysis tools for low-homology proteins remains a difficult challenge in computational biology, especially sequence alignment and consensus folding problems. We present partiFold-Align, the first algorithm for simultaneous alignment and consensus folding of unaligned protein sequences; the algorithm's complexity is polynomial in time and space. Algorithmically, partiFold-Align exploits sparsity in the set of super-secondary structure pairings and alignment candidates to achieve an effectively cubic running time for simultaneous pairwise alignment and folding. We demonstrate the efficacy of these techniques on transmembrane β-barrel proteins, an important yet difficult class of proteins with few known three-dimensional structures. Testing against structurally derived sequence alignments, partiFold-Align significantly outperforms state-of-the-art pairwise and multiple sequence alignment tools in the most difficult low-sequence homology case. It also improves secondary structure prediction where current approaches fail. Importantly, partiFold-Align requires no prior training. These general techniques are widely applicable to many more protein families (partiFold-Align is available at http://partifold.csail.mit.edu/). |
| first_indexed | 2024-09-23T13:09:46Z |
| format | Article |
| id | mit-1721.1/100002 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:09:46Z |
| publishDate | 2015 |
| publisher | Mary Ann Liebert |
| record_format | dspace |
| spelling | mit-1721.1/1000022022-10-01T13:28:53Z Simultaneous Alignment and Folding of Protein Sequences O'Donnell, Charles William Will, Sebastian Devadas, Srinivas Backofen, Rolf Berger, Bonnie Waldispuhl, Jerome Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mathematics Massachusetts Institute of Technology. Research Laboratory of Electronics Waldispuhl, Jerome O'Donnell, Charles William Will, Sebastian Devadas, Srinivas Berger, Bonnie Accurate comparative analysis tools for low-homology proteins remains a difficult challenge in computational biology, especially sequence alignment and consensus folding problems. We present partiFold-Align, the first algorithm for simultaneous alignment and consensus folding of unaligned protein sequences; the algorithm's complexity is polynomial in time and space. Algorithmically, partiFold-Align exploits sparsity in the set of super-secondary structure pairings and alignment candidates to achieve an effectively cubic running time for simultaneous pairwise alignment and folding. We demonstrate the efficacy of these techniques on transmembrane β-barrel proteins, an important yet difficult class of proteins with few known three-dimensional structures. Testing against structurally derived sequence alignments, partiFold-Align significantly outperforms state-of-the-art pairwise and multiple sequence alignment tools in the most difficult low-sequence homology case. It also improves secondary structure prediction where current approaches fail. Importantly, partiFold-Align requires no prior training. These general techniques are widely applicable to many more protein families (partiFold-Align is available at http://partifold.csail.mit.edu/). National Institutes of Health (U.S.) (Grant R01GM081871) 2015-11-23T16:59:17Z 2015-11-23T16:59:17Z 2014-04 Article http://purl.org/eprint/type/JournalArticle 1066-5277 1557-8666 http://hdl.handle.net/1721.1/100002 Waldispuhl, Jerome, Charles W. O’Donnell, Sebastian Will, Srinivas Devadas, Rolf Backofen, and Bonnie Berger. “Simultaneous Alignment and Folding of Protein Sequences.” Journal of Computational Biology 21, no. 7 (July 2014): 477–491. © Mary Ann Liebert, Inc. https://orcid.org/0000-0001-8253-7714 https://orcid.org/0000-0002-2724-7228 en_US http://dx.doi.org/10.1089/cmb.2013.0163 Journal of Computational Biology Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Mary Ann Liebert Mary Ann Leibert |
| spellingShingle | O'Donnell, Charles William Will, Sebastian Devadas, Srinivas Backofen, Rolf Berger, Bonnie Waldispuhl, Jerome Simultaneous Alignment and Folding of Protein Sequences |
| title | Simultaneous Alignment and Folding of Protein Sequences |
| title_full | Simultaneous Alignment and Folding of Protein Sequences |
| title_fullStr | Simultaneous Alignment and Folding of Protein Sequences |
| title_full_unstemmed | Simultaneous Alignment and Folding of Protein Sequences |
| title_short | Simultaneous Alignment and Folding of Protein Sequences |
| title_sort | simultaneous alignment and folding of protein sequences |
| url | http://hdl.handle.net/1721.1/100002 https://orcid.org/0000-0001-8253-7714 https://orcid.org/0000-0002-2724-7228 |
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