THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model

文献类型： 外文期刊

第一作者： Gong, Jianting

作者： Gong, Jianting;Jiang, Lili;Chen, Yongbing;Zhang, Yixiang;Ma, Zhiqiang;Fu, Zhiguo;He, Fei;Sun, Pingping;Ren, Zilin;Tian, Mingyao;Gong, Jianting;Jiang, Lili;Chen, Yongbing;Zhang, Yixiang;Li, Xue;Ren, Zilin;Tian, Mingyao;Ma, Zhiqiang

作者机构： Northeast Normal Univ, Inst Computat Biol, Sch Informat Sci & Technol, Changchun 130117, Peoples R China;Chinese Acad Agr Sci, Changchun Vet Res Inst, Changchun 130122, Peoples R China;Northeast Normal Univ, Coll Humanities & Sci, Dept Comp Sci, Changchun 130117, Peoples R China

期刊名称：BIOINFORMATICS （ 2022影响因子：5.8； 五年影响因子：8.3 ）

ISSN： 1367-4803

年卷期： 2023 年 39 卷 11 期

页码：

收录情况： SCI

摘要： Motivation: Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level structural information, it can help to understand how single-point variations cause functional changes. Results: Here, we proposed THPLM, a sequence-based deep learning model for stability change prediction using Meta's ESM-2. With ESM-2 and a simple convolutional neural network, THPLM achieved comparable or even better performance than most methods, including sequencebased and structure-based methods. Furthermore, the experimental results indicate that the PLM's ability to generate representations of sequence can effectively improve the ability of protein function prediction. Availability and implementation: The source code of THPLM and the testing data can be accessible through the following links: https://github. com/FPPGroup/THPLM.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序