Qin Zhaohui, Liu Huixia, Zhao Pei, Wang Kaiyuan, Ren Haoran, Miao Chunbo, Li Junzhou, Chen Yong-Zi, Chen Zhen
Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China.
State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China.
Int J Biol Macromol. 2024 Sep 16;280(Pt 1):135741. doi: 10.1016/j.ijbiomac.2024.135741.
Post-translational modifications (PTMs) diversify protein functions by adding/removing chemical groups to certain amino acid. As a newly-reported PTM, lysine β-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 β-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/SLAM and https://github.com/Gabriel-QIN/SLAM, respectively.
翻译后修饰（PTMs）通过向特定氨基酸添加/去除化学基团来使蛋白质功能多样化。作为一种新报道的翻译后修饰，赖氨酸β-羟基丁酰化（Kbhb）为功能蛋白质组学提供了一条新途径。因此，准确高效地预测Kbhb位点势在必行。然而，目前用于鉴定翻译后修饰位点的实验方法往往昂贵且耗时。到目前为止，尚未提出用于检测Kbhb位点的计算方法。为此，我们提出了第一种基于深度学习的方法，称为SLAM，用于在计算机上识别赖氨酸β-羟基丁酰化位点。在5折交叉验证和独立测试中对SLAM的性能进行了评估，在通用和物种特异性独立测试集上，AUROC值分别达到了0.890、0.899、0.907和0.923。例如，我们预测了人类S-腺苷-L-高半胱氨酸水解酶中的潜在Kbhb位点，这与实验验证的Kbhb位点一致。总之，我们的方法能够准确高效地表征对蛋白质功能和稳定性至关重要的新型Kbhb位点，并可应用于其他重要翻译后修饰位点的结构导向鉴定。SLAM在线服务和源代码分别可在https://ai4bio.online/SLAM和https://github.com/Gabriel-QIN/SLAM获取。
