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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2411.18568 |
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| _version_ | 1866916839464894464 |
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| author | Zheng, Tianyuan Rondina, Alessandro Micklem, Gos Liò, Pietro |
| author_facet | Zheng, Tianyuan Rondina, Alessandro Micklem, Gos Liò, Pietro |
| contents | Deep generative models show promise for $\textit{de novo}$ protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally relevant, and dynamically stable remains challenging. We present a deep generative modeling pipeline for early $\textit{de novo}$ design of monomeric proteins, based on Score Matching and Flow Matching. We apply this pipeline to four diverse protein families with an adaptable evaluation protocol. Generated structures display realistic, clash-free conformations enriched with family-specific features, while the designed sequences preserve essential functional residues while retaining variability. Molecular dynamics and binding simulations show dynamic stability, with wild-type-like binding pockets that interact favorably with family-specific ligands. These results provide practical guidelines for integrating generative models into protein design workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18568 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies Zheng, Tianyuan Rondina, Alessandro Micklem, Gos Liò, Pietro Biomolecules Deep generative models show promise for $\textit{de novo}$ protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally relevant, and dynamically stable remains challenging. We present a deep generative modeling pipeline for early $\textit{de novo}$ design of monomeric proteins, based on Score Matching and Flow Matching. We apply this pipeline to four diverse protein families with an adaptable evaluation protocol. Generated structures display realistic, clash-free conformations enriched with family-specific features, while the designed sequences preserve essential functional residues while retaining variability. Molecular dynamics and binding simulations show dynamic stability, with wild-type-like binding pockets that interact favorably with family-specific ligands. These results provide practical guidelines for integrating generative models into protein design workflows. |
| title | Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2411.18568 |