@inproceedings{zhou-etal-2025-large,
title = "Large Language and Protein Assistant for Protein-Protein Interactions Prediction",
author = "Zhou, Peng and
Ma, Pengsen and
Wang, Jianmin and
Cai, Xibao and
Huang, Haitao and
Liu, Wei and
Wang, Longyue and
Tim, Lai Hou and
Zeng, Xiangxiang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.554/",
doi = "10.18653/v1/2025.acl-long.554",
pages = "11312--11327",
ISBN = "979-8-89176-251-0",
abstract = "Predicting the types and affinities of protein-protein interactions (PPIs) is crucial for understanding biological processes and developing novel therapeutic approaches. While encoding proteins themselves is essential, PPI networks can also provide rich prior knowledge for these predictive tasks. However, existing methods oversimplify the problem of PPI prediction in a semi-supervised manner when utilizing PPI networks, limiting their practical application. Furthermore, how to effectively use the rich prior knowledge of PPI networks for novel proteins not present in the network remains an unexplored issue. Additionally, due to inflexible architectures, most of existing methods cannot handle complexes containing an flexible number of proteins. To overcome these limitations, we introduce LLaPA (Large Language and Protein Assistant), a multimodal large language model that integrates proteins and PPI networks. LLaPA offers a more rational approach to utilizing PPI networks for PPI prediction and can fully exploit the information of PPI networks for unseen proteins. Through natural language instructions, LLaPA can accept flexible number of protein sequences and has the potential to perform various protein tasks. Experiments show that LLaPA achieves state-of-the-art performance in multi-label PPI (mPPI) type prediction and is capable of predicting the binding affinity between multiple interacting proteins based on sequence data."
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<abstract>Predicting the types and affinities of protein-protein interactions (PPIs) is crucial for understanding biological processes and developing novel therapeutic approaches. While encoding proteins themselves is essential, PPI networks can also provide rich prior knowledge for these predictive tasks. However, existing methods oversimplify the problem of PPI prediction in a semi-supervised manner when utilizing PPI networks, limiting their practical application. Furthermore, how to effectively use the rich prior knowledge of PPI networks for novel proteins not present in the network remains an unexplored issue. Additionally, due to inflexible architectures, most of existing methods cannot handle complexes containing an flexible number of proteins. To overcome these limitations, we introduce LLaPA (Large Language and Protein Assistant), a multimodal large language model that integrates proteins and PPI networks. LLaPA offers a more rational approach to utilizing PPI networks for PPI prediction and can fully exploit the information of PPI networks for unseen proteins. Through natural language instructions, LLaPA can accept flexible number of protein sequences and has the potential to perform various protein tasks. Experiments show that LLaPA achieves state-of-the-art performance in multi-label PPI (mPPI) type prediction and is capable of predicting the binding affinity between multiple interacting proteins based on sequence data.</abstract>
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%0 Conference Proceedings
%T Large Language and Protein Assistant for Protein-Protein Interactions Prediction
%A Zhou, Peng
%A Ma, Pengsen
%A Wang, Jianmin
%A Cai, Xibao
%A Huang, Haitao
%A Liu, Wei
%A Wang, Longyue
%A Tim, Lai Hou
%A Zeng, Xiangxiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhou-etal-2025-large
%X Predicting the types and affinities of protein-protein interactions (PPIs) is crucial for understanding biological processes and developing novel therapeutic approaches. While encoding proteins themselves is essential, PPI networks can also provide rich prior knowledge for these predictive tasks. However, existing methods oversimplify the problem of PPI prediction in a semi-supervised manner when utilizing PPI networks, limiting their practical application. Furthermore, how to effectively use the rich prior knowledge of PPI networks for novel proteins not present in the network remains an unexplored issue. Additionally, due to inflexible architectures, most of existing methods cannot handle complexes containing an flexible number of proteins. To overcome these limitations, we introduce LLaPA (Large Language and Protein Assistant), a multimodal large language model that integrates proteins and PPI networks. LLaPA offers a more rational approach to utilizing PPI networks for PPI prediction and can fully exploit the information of PPI networks for unseen proteins. Through natural language instructions, LLaPA can accept flexible number of protein sequences and has the potential to perform various protein tasks. Experiments show that LLaPA achieves state-of-the-art performance in multi-label PPI (mPPI) type prediction and is capable of predicting the binding affinity between multiple interacting proteins based on sequence data.
%R 10.18653/v1/2025.acl-long.554
%U https://aclanthology.org/2025.acl-long.554/
%U https://doi.org/10.18653/v1/2025.acl-long.554
%P 11312-11327
Markdown (Informal)
[Large Language and Protein Assistant for Protein-Protein Interactions Prediction](https://aclanthology.org/2025.acl-long.554/) (Zhou et al., ACL 2025)
ACL
- Peng Zhou, Pengsen Ma, Jianmin Wang, Xibao Cai, Haitao Huang, Wei Liu, Longyue Wang, Lai Hou Tim, and Xiangxiang Zeng. 2025. Large Language and Protein Assistant for Protein-Protein Interactions Prediction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11312–11327, Vienna, Austria. Association for Computational Linguistics.