Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling
We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitch...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/132192 |
| _version_ | 1811089320685076480 |
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| author | Yu, Chi Hua Buehler, Markus J |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Yu, Chi Hua Buehler, Markus J |
| author_sort | Yu, Chi Hua |
| collection | MIT |
| description | We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering. |
| first_indexed | 2024-09-23T14:17:20Z |
| format | Article |
| id | mit-1721.1/132192 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:17:20Z |
| publishDate | 2021 |
| publisher | AIP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1321922022-09-28T19:47:24Z Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling Yu, Chi Hua Buehler, Markus J Massachusetts Institute of Technology. Department of Civil and Environmental Engineering We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering. 2021-09-20T18:21:18Z 2021-09-20T18:21:18Z 2020-03 2020-05-19T15:23:21Z Article http://purl.org/eprint/type/JournalArticle 2473-2877 https://hdl.handle.net/1721.1/132192 Yu, Chi-Hua, and Markus J. Buehler. “Sonification Based de Novo Protein Design Using Artificial Intelligence, Structure Prediction, and Analysis Using Molecular Modeling.” APL Bioengineering 4, 1 (March 2020): 016108. © 2020 Authors en http://dx.doi.org/10.1063/1.5133026 APL Bioengineering Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf AIP Publishing American Institute of Physics (AIP) |
| spellingShingle | Yu, Chi Hua Buehler, Markus J Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling |
| title | Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling |
| title_full | Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling |
| title_fullStr | Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling |
| title_full_unstemmed | Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling |
| title_short | Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling |
| title_sort | sonification based de novo protein design using artificial intelligence structure prediction and analysis using molecular modeling |
| url | https://hdl.handle.net/1721.1/132192 |
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