Designing Novel DNA-Binding Proteins with Generative Deep Learning
Protein-DNA interactions play a critical role in various biological processes, such as gene regulation and genome maintenance. Designing protein backbones specifically tailored for DNA binding remains a challenging task, requiring the exploration of novel computational approaches. This thesis presen...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/152003 https://orcid.org/0009-0007-4177-4415 |
| _version_ | 1826211200710475776 |
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| author | Calman, Ido |
| author2 | Jacobson, Joseph |
| author_facet | Jacobson, Joseph Calman, Ido |
| author_sort | Calman, Ido |
| collection | MIT |
| description | Protein-DNA interactions play a critical role in various biological processes, such as gene regulation and genome maintenance. Designing protein backbones specifically tailored for DNA binding remains a challenging task, requiring the exploration of novel computational approaches. This thesis presents a novel framework for gen- erating protein backbones that exhibit affinity for DNA molecules. The proposed methodology leverages Graph Neural Networks (GNNs) for encoding protein struc- tures and diffusion models for conditional sampling. The GNNs capture the intricate relationships between amino acids in the protein backbone, allowing for the effective encoding of structural information relevant to DNA binding. The diffusion models enable the conditional generation of protein backbones, given specific DNA sequences as input. The thesis proposes a Transformer architecture and provides a practical way to diffuse from its protein encoding. The findings from this research have significant implications for the design and engineering of DNA binding proteins, facilitating ad- vancements in fields such as synthetic biology, gene therapy, and drug development. |
| first_indexed | 2024-09-23T15:02:10Z |
| format | Thesis |
| id | mit-1721.1/152003 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:02:10Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1520032023-09-01T03:12:25Z Designing Novel DNA-Binding Proteins with Generative Deep Learning Calman, Ido Jacobson, Joseph Program in Media Arts and Sciences (Massachusetts Institute of Technology) Protein-DNA interactions play a critical role in various biological processes, such as gene regulation and genome maintenance. Designing protein backbones specifically tailored for DNA binding remains a challenging task, requiring the exploration of novel computational approaches. This thesis presents a novel framework for gen- erating protein backbones that exhibit affinity for DNA molecules. The proposed methodology leverages Graph Neural Networks (GNNs) for encoding protein struc- tures and diffusion models for conditional sampling. The GNNs capture the intricate relationships between amino acids in the protein backbone, allowing for the effective encoding of structural information relevant to DNA binding. The diffusion models enable the conditional generation of protein backbones, given specific DNA sequences as input. The thesis proposes a Transformer architecture and provides a practical way to diffuse from its protein encoding. The findings from this research have significant implications for the design and engineering of DNA binding proteins, facilitating ad- vancements in fields such as synthetic biology, gene therapy, and drug development. S.M. 2023-08-30T15:58:40Z 2023-08-30T15:58:40Z 2023-06 2023-08-16T20:34:00.537Z Thesis https://hdl.handle.net/1721.1/152003 https://orcid.org/0009-0007-4177-4415 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Calman, Ido Designing Novel DNA-Binding Proteins with Generative Deep Learning |
| title | Designing Novel DNA-Binding Proteins with Generative Deep Learning |
| title_full | Designing Novel DNA-Binding Proteins with Generative Deep Learning |
| title_fullStr | Designing Novel DNA-Binding Proteins with Generative Deep Learning |
| title_full_unstemmed | Designing Novel DNA-Binding Proteins with Generative Deep Learning |
| title_short | Designing Novel DNA-Binding Proteins with Generative Deep Learning |
| title_sort | designing novel dna binding proteins with generative deep learning |
| url | https://hdl.handle.net/1721.1/152003 https://orcid.org/0009-0007-4177-4415 |
| work_keys_str_mv | AT calmanido designingnoveldnabindingproteinswithgenerativedeeplearning |