AlphaFold2 for protein structure prediction: Best practices and critical analyses
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916331796824064 |
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| author | Radjasandirane, Ragousandirane de Brevern, Alexandre G. |
| author_facet | Radjasandirane, Ragousandirane de Brevern, Alexandre G. |
| contents | AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high quality biological model suitable for applications such as drug design, protein interaction or molecular dynamics studies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_12668 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AlphaFold2 for protein structure prediction: Best practices and critical analyses Radjasandirane, Ragousandirane de Brevern, Alexandre G. Biomolecules AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high quality biological model suitable for applications such as drug design, protein interaction or molecular dynamics studies. |
| title | AlphaFold2 for protein structure prediction: Best practices and critical analyses |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2403.12668 |