PLMSearch: Protein language model powers accurate and fast sequence search for remote homology

Wei Liu, Ziye Wang, Ronghui You, Chenghan Xie, Hong Wei, Yi Xiong, Jianyi Yang () and Shanfeng Zhu ()
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Wei Liu: Fudan University
Ziye Wang: Fudan University
Ronghui You: Fudan University
Chenghan Xie: Fudan University
Hong Wei: Nankai University
Yi Xiong: Shanghai Jiao Tong University
Jianyi Yang: Shandong University
Shanfeng Zhu: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .

Date: 2024
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DOI: 10.1038/s41467-024-46808-5

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