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Updated: Jun 29, 2025

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PLMSearch:蛋白质语言模型为远程同源性提供了准确和快速的序列搜索能力

Wei Liu¹, Ziye Wang¹, Ronghui You¹

¹Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, 200433, Shanghai, China.

Nature communications

|March 30, 2024

概括

PLMSearch是一种新的蛋白质搜索方法,使用对序列的深度学习来找到遥远的进化关系. 它显著提高了同类蛋白质识别的灵敏度和速度.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
结构生物学结构生物学

背景情况:

同源蛋白质的搜索对于蛋白质的注释和分析至关重要.
仅从蛋白质序列中确定遥远的进化关系仍然是一个重大挑战.
当前基于序列的方法往往难以检测远程同源性.

研究的目的:

介绍PLMSearch,一种新的同源蛋白质搜索方法,仅使用蛋白质序列.
利用预训练的蛋白质语言模型的深度表示,以增强同质检测.
为了提高识别远端同类蛋白质的灵敏度和速度.

主要方法:

PLMSearch使用来自预训练的蛋白质语言模型的深度表示.
一个相似性预测模型是使用广泛的真实结构相似性数据进行训练的.
该方法的重点是直接从序列中捕获远程同质信息.

主要成果:

PLMSearch的搜索速度与MMseqs2相当,可以快速处理数百万个蛋白质对.
与传统方法相比,该方法显示灵敏度增加了三倍.
PLMSearch有效地识别了具有不同序列但相似结构的远程同类对,超过了传统的序列搜索.

结论:

PLMSearch提供了一种强大的仅序列的方法,用于同源蛋白质搜索.
该方法增强了远程进化关系的检测,弥合了序列和结构相似性之间的差距.
PLMSearch为蛋白质分析和注释提供了一个灵敏而高效的工具.

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