Multi-Modal CLIP-Informed Protein Editing
Background: Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2024-01-01
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| Series: | Health Data Science |
| Online Access: | https://spj.science.org/doi/10.34133/hds.0211 |
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| author | Mingze Yin Hanjing Zhou Yiheng Zhu Miao Lin Yixuan Wu Jialu Wu Hongxia Xu Chang-Yu Hsieh Tingjun Hou Jintai Chen Jian Wu |
| author_facet | Mingze Yin Hanjing Zhou Yiheng Zhu Miao Lin Yixuan Wu Jialu Wu Hongxia Xu Chang-Yu Hsieh Tingjun Hou Jintai Chen Jian Wu |
| author_sort | Mingze Yin |
| collection | DOAJ |
| description | Background: Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback. Methods: To fill these gaps, we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning. Our approach comprises 2 stages: In the pretraining stage, contrastive learning aligns protein–biotext representations encoded by 2 large language models (LLMs). Subsequently, during the protein editing stage, the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences. Results: Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains, including enzyme catalytic activity, protein stability, and antibody-specific binding ability. ProtET improves the state-of-the-art results by a large margin, leading to substantial stability improvements of 16.67% and 16.90%. Conclusions: This capability positions ProtET to advance real-world artificial protein editing, potentially addressing unmet academic, industrial, and clinical needs. |
| format | Article |
| id | doaj-art-5d076180547f4fe1a9d009c80353247f |
| institution | Kabale University |
| issn | 2765-8783 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Health Data Science |
| spelling | doaj-art-5d076180547f4fe1a9d009c80353247f2024-12-20T01:44:45ZengAmerican Association for the Advancement of Science (AAAS)Health Data Science2765-87832024-01-01410.34133/hds.0211Multi-Modal CLIP-Informed Protein EditingMingze Yin0Hanjing Zhou1Yiheng Zhu2Miao Lin3Yixuan Wu4Jialu Wu5Hongxia Xu6Chang-Yu Hsieh7Tingjun Hou8Jintai Chen9Jian Wu10School of Medicine, Zhejiang University, Hangzhou, China.College of Computer Science and Technology, Zhejiang University, Hangzhou, China.College of Computer Science and Technology, Zhejiang University, Hangzhou, China.Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.School of Medicine, Zhejiang University, Hangzhou, China.Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.AI Thrust, Information Hub, HKUST (Guangzhou), Guangzhou, China.Second Affiliated Hospital School of Medicine, Hangzhou, China.Background: Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback. Methods: To fill these gaps, we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning. Our approach comprises 2 stages: In the pretraining stage, contrastive learning aligns protein–biotext representations encoded by 2 large language models (LLMs). Subsequently, during the protein editing stage, the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences. Results: Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains, including enzyme catalytic activity, protein stability, and antibody-specific binding ability. ProtET improves the state-of-the-art results by a large margin, leading to substantial stability improvements of 16.67% and 16.90%. Conclusions: This capability positions ProtET to advance real-world artificial protein editing, potentially addressing unmet academic, industrial, and clinical needs.https://spj.science.org/doi/10.34133/hds.0211 |
| spellingShingle | Mingze Yin Hanjing Zhou Yiheng Zhu Miao Lin Yixuan Wu Jialu Wu Hongxia Xu Chang-Yu Hsieh Tingjun Hou Jintai Chen Jian Wu Multi-Modal CLIP-Informed Protein Editing Health Data Science |
| title | Multi-Modal CLIP-Informed Protein Editing |
| title_full | Multi-Modal CLIP-Informed Protein Editing |
| title_fullStr | Multi-Modal CLIP-Informed Protein Editing |
| title_full_unstemmed | Multi-Modal CLIP-Informed Protein Editing |
| title_short | Multi-Modal CLIP-Informed Protein Editing |
| title_sort | multi modal clip informed protein editing |
| url | https://spj.science.org/doi/10.34133/hds.0211 |
| work_keys_str_mv | AT mingzeyin multimodalclipinformedproteinediting AT hanjingzhou multimodalclipinformedproteinediting AT yihengzhu multimodalclipinformedproteinediting AT miaolin multimodalclipinformedproteinediting AT yixuanwu multimodalclipinformedproteinediting AT jialuwu multimodalclipinformedproteinediting AT hongxiaxu multimodalclipinformedproteinediting AT changyuhsieh multimodalclipinformedproteinediting AT tingjunhou multimodalclipinformedproteinediting AT jintaichen multimodalclipinformedproteinediting AT jianwu multimodalclipinformedproteinediting |