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:
期刊名称:BIOINFORMATICS ( 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.
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