SLAM: Structure-aware lysine β-hydroxybutyrylation prediction with protein language model

文献类型： 外文期刊

第一作者： Qin, Zhaohui

作者： Qin, Zhaohui;Liu, Huixia;Wang, Kaiyuan;Ren, Haoran;Miao, Chunbo;Li, Junzhou;Chen, Zhen;Zhao, Pei;Chen, Yong-Zi;Chen, Yong-Zi

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关键词： Lysine beta-hydroxybutyrylation; Graph neural network; Protein language model

期刊名称：INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES （ 影响因子：8.5； 五年影响因子：8.7 ）

ISSN： 0141-8130

年卷期： 2024 年 280 卷

页码：

收录情况： SCI

摘要： Post-translational modifications (PTMs) diversify protein functions by adding/removing chemical groups to certain amino acid. As a newly-reported PTM, lysine beta-hydroxybutyrylation (Kbhb) presents a new avenue to functional proteomics. Therefore, accurate and efficient prediction of Kbhb sites is imperative. However, the current experimental methods for identifying PTM sites are often expensive and time-consuming. Up to now, there is no computational method proposed for Kbhb sites detection. To this end, we present the first deep learning-based method, termed SLAM, to in silico identify lysine beta-hydroxybutyrylation sites. The performance of SLAM is evaluated on both 5-fold cross-validation and independent test, achieving 0.890, 0.899, 0.907 and 0.923 in terms of AUROC values, on the general and species-specific independent test sets, respectively. As one example, we predicted the potential Kbhb sites in human S-adenosyl-L-homocysteine hydrolase, which is in agreement with experimentally-verified Kbhb sites. In summary, our method could enable accurate and efficient characterization of novel Kbhb sites that are crucial for the function and stability of proteins and could be applied in the structure-guided identification of other important PTM sites. The SLAM online service and source code is available at https://ai4bio.online/SLAMand https://github.com/Gabriel-QIN/SLAM, respectively.

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