Data-driven computational protein design
Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structure...
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| Formato: | Artigo |
| Idioma: | English |
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Elsevier BV
2021
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| Acceso en liña: | https://hdl.handle.net/1721.1/131227 |
| _version_ | 1826188495061778432 |
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| author | Frappier, Vincent Keating, Amy E. |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Frappier, Vincent Keating, Amy E. |
| author_sort | Frappier, Vincent |
| collection | MIT |
| description | Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design. |
| first_indexed | 2024-09-23T08:00:30Z |
| format | Article |
| id | mit-1721.1/131227 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:00:30Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1312272022-09-23T10:15:34Z Data-driven computational protein design Frappier, Vincent Keating, Amy E. Massachusetts Institute of Technology. Department of Biology Massachusetts Institute of Technology. Department of Biological Engineering Molecular Biology Structural Biology Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design. National Institutes of Health (Award R01GM132117) 2021-09-01T14:38:09Z 2021-09-01T14:38:09Z 2021-08 2021-08-06T17:24:54Z Article http://purl.org/eprint/type/JournalArticle 0959-440X https://hdl.handle.net/1721.1/131227 Frappier, Vincent and Amy E. Keating. "Data-driven computational protein design." Current Opinion in Structural Biology 69 (August 2021): 63-69. © 2021 Elsevier Ltd en http://dx.doi.org/10.1016/j.sbi.2021.03.009 Current Opinion in Structural Biology Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Prof. Amy Keating |
| spellingShingle | Molecular Biology Structural Biology Frappier, Vincent Keating, Amy E. Data-driven computational protein design |
| title | Data-driven computational protein design |
| title_full | Data-driven computational protein design |
| title_fullStr | Data-driven computational protein design |
| title_full_unstemmed | Data-driven computational protein design |
| title_short | Data-driven computational protein design |
| title_sort | data driven computational protein design |
| topic | Molecular Biology Structural Biology |
| url | https://hdl.handle.net/1721.1/131227 |
| work_keys_str_mv | AT frappiervincent datadrivencomputationalproteindesign AT keatingamye datadrivencomputationalproteindesign |