AI-predicted protein deformation encodes energy landscape
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909262255489024 |
|---|---|
| author | Mcbride, John M Tlusty, Tsvi |
| author_facet | Mcbride, John M Tlusty, Tsvi |
| contents | AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy $ΔΔG$. Unexpectedly, we find that physical strain alone -- without any additional data or computation -- correlates almost as well with $ΔΔG$ as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18222 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI-predicted protein deformation encodes energy landscape Mcbride, John M Tlusty, Tsvi Biomolecules Biological Physics Quantitative Methods AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy $ΔΔG$. Unexpectedly, we find that physical strain alone -- without any additional data or computation -- correlates almost as well with $ΔΔG$ as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution. |
| title | AI-predicted protein deformation encodes energy landscape |
| topic | Biomolecules Biological Physics Quantitative Methods |
| url | https://arxiv.org/abs/2311.18222 |