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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.00508 |
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| _version_ | 1866909472037797888 |
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| author | Ford, Colby T. Ullah, Samee Antunes, Dinler Amaral Ferreira, Tarsis Gesteira |
| author_facet | Ford, Colby T. Ullah, Samee Antunes, Dinler Amaral Ferreira, Tarsis Gesteira |
| contents | PyMOLfold is a flexible and open-source plugin designed to seamlessly integrate AI-based protein structure prediction and visualization within the widely used PyMOL molecular graphics system. By leveraging state-of-the-art protein folding models such as ESM3, Boltz-1, and Chai-1, PyMOLfold allows researchers to directly predict protein tertiary structures from amino acid sequences without requiring external tools or complex workflows. Furthermore, with certain models, users can provide a SMILES string of a ligand and have the small molecule placed in the protein structure. This unique capability bridges the gap between computational folding and structural visualization, enabling users to input a primary sequence, perform a folding prediction, and immediately explore the resulting 3D structure within the same intuitive platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00508 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOL Ford, Colby T. Ullah, Samee Antunes, Dinler Amaral Ferreira, Tarsis Gesteira Biomolecules PyMOLfold is a flexible and open-source plugin designed to seamlessly integrate AI-based protein structure prediction and visualization within the widely used PyMOL molecular graphics system. By leveraging state-of-the-art protein folding models such as ESM3, Boltz-1, and Chai-1, PyMOLfold allows researchers to directly predict protein tertiary structures from amino acid sequences without requiring external tools or complex workflows. Furthermore, with certain models, users can provide a SMILES string of a ligand and have the small molecule placed in the protein structure. This unique capability bridges the gap between computational folding and structural visualization, enabling users to input a primary sequence, perform a folding prediction, and immediately explore the resulting 3D structure within the same intuitive platform. |
| title | PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOL |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2502.00508 |