MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916461325320192 |
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| author | Luo, Yizhen Nie, Zikun Hong, Massimo Zhao, Suyuan Zhou, Hao Nie, Zaiqing |
| author_facet | Luo, Yizhen Nie, Zikun Hong, Massimo Zhao, Suyuan Zhou, Hao Nie, Zaiqing |
| contents | Studying protein mutations within amino acid sequences holds tremendous significance in life sciences. Protein language models (PLMs) have demonstrated strong capabilities in broad biological applications. However, due to architectural design and lack of supervision, PLMs model mutations implicitly with evolutionary plausibility, which is not satisfactory to serve as explainable and engineerable tools in real-world studies. To address these issues, we present MutaPLM, a unified framework for interpreting and navigating protein mutations with protein language models. MutaPLM introduces a protein delta network that captures explicit protein mutation representations within a unified feature space, and a transfer learning pipeline with a chain-of-thought (CoT) strategy to harvest protein mutation knowledge from biomedical texts. We also construct MutaDescribe, the first large-scale protein mutation dataset with rich textual annotations, which provides cross-modal supervision signals. Through comprehensive experiments, we demonstrate that MutaPLM excels at providing human-understandable explanations for mutational effects and prioritizing novel mutations with desirable properties. Our code, model, and data are open-sourced at https://github.com/PharMolix/MutaPLM. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_22949 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering Luo, Yizhen Nie, Zikun Hong, Massimo Zhao, Suyuan Zhou, Hao Nie, Zaiqing Machine Learning Biomolecules 68T07 Studying protein mutations within amino acid sequences holds tremendous significance in life sciences. Protein language models (PLMs) have demonstrated strong capabilities in broad biological applications. However, due to architectural design and lack of supervision, PLMs model mutations implicitly with evolutionary plausibility, which is not satisfactory to serve as explainable and engineerable tools in real-world studies. To address these issues, we present MutaPLM, a unified framework for interpreting and navigating protein mutations with protein language models. MutaPLM introduces a protein delta network that captures explicit protein mutation representations within a unified feature space, and a transfer learning pipeline with a chain-of-thought (CoT) strategy to harvest protein mutation knowledge from biomedical texts. We also construct MutaDescribe, the first large-scale protein mutation dataset with rich textual annotations, which provides cross-modal supervision signals. Through comprehensive experiments, we demonstrate that MutaPLM excels at providing human-understandable explanations for mutational effects and prioritizing novel mutations with desirable properties. Our code, model, and data are open-sourced at https://github.com/PharMolix/MutaPLM. |
| title | MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering |
| topic | Machine Learning Biomolecules 68T07 |
| url | https://arxiv.org/abs/2410.22949 |