Fast, sensitive detection of protein homologs using deep dense retrieval

The identification of protein homologs in large databases using conventional methods, such as protein sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting homologs on the basis of a protein language model and dense retrieval techniques. Its dual-encoder architecture generates different embeddings for the same protein sequence and easily locates homologs by comparing these representations. Its alignment-free nature improves speed and the protein language model incorporates rich evolutionary and structural information within DHR embeddings. DHR achieves a >10% increase in sensitivity compared to previous methods and a >56% increase in sensitivity at the superfamily level for samples that are challenging to identify using alignment-based approaches. It is up to 22 times faster than traditional methods such as PSI-BLAST and DIAMOND and up to 28,700 times faster than HMMER. The new remote homologs exclusively found by DHR are useful for revealing connections between well-characterized proteins and improving our knowledge of protein evolution, structure and function.

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Publication Details

Journal
Nature Biotechnology
Published
2024-08-09
DOI
https://doi.org/10.1038/s41587-024-02353-6
Citations
36
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
8.42

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article

Fast, sensitive detection of protein homologs using deep dense retrieval

Liang Hong, Siqi Sun, Liangzhen Zheng, Irwin King et al.
36 citations
Nature Biotechnology
Machine Learning in Bioinformatics
8.42
article

Fast, sensitive detection of protein homologs using deep dense retrieval

Liang Hong, Siqi Sun, Liangzhen Zheng, Irwin King, Mark Gerstein, Jiuming Wang, Yu Li, Sheng Wang, Sheng Xu, Zhigang Hu, Qingxiong Tan, Xiangru Tang
article en
36 citations

Abstract

The identification of protein homologs in large databases using conventional methods, such as protein sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting homologs on the basis of a protein language model and dense retrieval techniques. Its dual-encoder architecture generates different embeddings for the same protein sequence and easily locates homologs by comparing these representations. Its alignment-free nature improves speed and the protein language model incorporates rich evolutionary and structural information within DHR embeddings. DHR achieves a >10% increase in sensitivity compared to previous methods and a >56% increase in sensitivity at the superfamily level for samples that are challenging to identify using alignment-based approaches. It is up to 22 times faster than traditional methods such as PSI-BLAST and DIAMOND and up to 28,700 times faster than HMMER. The new remote homologs exclusively found by DHR are useful for revealing connections between well-characterized proteins and improving our knowledge of protein evolution, structure and function.

Nature BiotechnologyVol. 43(6)
Broad Institute (US), Shanghai Lixin University of Accounting and Finance (CN), Chinese University of Hong Kong (HK), Chinese Academy of Sciences (CN), Fudan University (CN), Yale University (US), Midea Group (China) (CN), Chinese University of Hong Kong, Shenzhen (CN), Shenzhen Institutes of Advanced Technology (CN), Inspire Institute (US), Shanghai Artificial Intelligence Laboratory (CN), Massachusetts Institute of Technology (US)
Research Grants Council, University Grants Committee, Fudan University, Innovation and Technology Commission, Chinese University of Hong Kong, Innovation and Technology Fund
Openalex Percentile: Top 1%
Machine Learning in Bioinformatics
8.42
DOIOpen Access PDFOpenAlex
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