To handle increasingly large protein databases, a new ultrafast, highly sensitive method — Dense Homolog Retriever (DHR) — detects remote homologs using dense retrieval and protein language models. Its alignment-free nature makes it much faster than traditional approaches, and the newly found remote homologs benefit our understanding of protein evolution, structure and function..

为了处理越来越大的蛋白质数据库，一种新的超快，高灵敏度的方法-密集同源检索器（DHR）-使用密集检索和蛋白质语言模型检测远程同源物。它的无比对性质使其比传统方法快得多，新发现的远程同源物有助于我们对蛋白质进化，结构和功能的理解。。

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Fig. 1: DHR for fast detection of remote homologs and an improved understanding of proteins.

图1:DHR用于快速检测远程同源物并提高对蛋白质的理解。

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Download referencesAdditional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Hong, L. et al. Fast, sensitive detection of protein homologs using deep dense retrieval.

下载参考其他信息出版商的注释Springer Nature对于已发布地图和机构隶属关系中的管辖权主张保持中立。这是Hong，L。等人的总结。使用深度密集检索快速，灵敏地检测蛋白质同源物。

Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02353-6 (2024).Rights and permissionsReprints and permissionsAbout this articleCite this article Protein language model enables fast and sensitive remote homolog detection..

美国国家生物技术公司。https://doi.org/10.1038/s41587-024-02353-6（2024年）。权利和许可打印和许可本文引用本文蛋白质语言模型可实现快速灵敏的远程同源检测。。

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