Improving AlphaFlow for Efficient Protein Ensembles Generation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909259137024000 |
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| author | Li, Shaoning Li, Mingyu Wang, Yusong He, Xinheng Zheng, Nanning Zhang, Jian Heng, Pheng-Ann |
| author_facet | Li, Shaoning Li, Mingyu Wang, Yusong He, Xinheng Zheng, Nanning Zhang, Jian Heng, Pheng-Ann |
| contents | Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFlow stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFlow still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFlow-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFlow-Lit performs on-par with AlphaFlow and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFlow-Lit in enabling faster and more scalable generation of protein ensembles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12053 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving AlphaFlow for Efficient Protein Ensembles Generation Li, Shaoning Li, Mingyu Wang, Yusong He, Xinheng Zheng, Nanning Zhang, Jian Heng, Pheng-Ann Machine Learning Artificial Intelligence Quantitative Methods Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFlow stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFlow still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFlow-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFlow-Lit performs on-par with AlphaFlow and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFlow-Lit in enabling faster and more scalable generation of protein ensembles. |
| title | Improving AlphaFlow for Efficient Protein Ensembles Generation |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2407.12053 |