The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation
Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2020
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| Online Access: | https://hdl.handle.net/1721.1/125166 |
| _version_ | 1811076461200670720 |
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| author | Hsu, Claire C. Buehler, Markus J. Tarakanova, Anna |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Hsu, Claire C. Buehler, Markus J. Tarakanova, Anna |
| author_sort | Hsu, Claire C. |
| collection | MIT |
| description | Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease. |
| first_indexed | 2024-09-23T10:22:31Z |
| format | Article |
| id | mit-1721.1/125166 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:22:31Z |
| publishDate | 2020 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1251662022-09-30T20:42:52Z The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation Hsu, Claire C. Buehler, Markus J. Tarakanova, Anna Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease. ONR (grant # N00014–16–1–651 2333) NIH U01 EB014976 2020-05-11T20:45:02Z 2020-05-11T20:45:02Z 2020-02 2019-07 2020-05-11T18:33:21Z Article http://purl.org/eprint/type/JournalArticle 2045-2322 https://hdl.handle.net/1721.1/125166 Hsu, Claire C., Markus J. Buehler, and Anna Tarakanova. "The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation." Scientific Reports, 10 (February 2020): 2068. © 2020, The Author(s). en http://dx.doi.org/10.1038/s41598-020-58868-w Scientific Reports Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Scientific Reports |
| spellingShingle | Hsu, Claire C. Buehler, Markus J. Tarakanova, Anna The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation |
| title | The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation |
| title_full | The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation |
| title_fullStr | The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation |
| title_full_unstemmed | The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation |
| title_short | The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation |
| title_sort | order disorder continuum linking predictions of protein structure and disorder through molecular simulation |
| url | https://hdl.handle.net/1721.1/125166 |
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