Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S.
Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S.
复制标题
使用MIND-S对翻译后修饰进行预测、解释和突变评估的方案。
DOI：
10.1016/j.xpro.2023.102682
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发表时间：
2023-12-15
期刊：
影响因子：
--
通讯作者：
Ping P
中科院分区：
文献类型：
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作者：
Yan Y;Wang D;Xin R;Soriano RA;Ng DCM;Wang W;Ping P
翻译后修饰(PTM)是各种细胞过程中的关键调节机制；PTMS的改变可能会导致人类疾病。我们提出了一种使用Mind-S(多标签可解释的深度学习方法for PTM预测结构版)来研究PTMS的协议。该方案由循序渐进的指南组成，包括Mind-S的三个关键应用：基于蛋白质序列的PTM预测，重要氨基酸的识别，以及阐明分子突变导致的PTM格局的改变。关于该议定书的使用和执行的完整细节，请参考严氏等人(2023)。一种关于Mind-S的协议，该软件程序支持对PTMS的多重计算分析执行多蛋白质多PTM预测的步骤PTM发生的重要氨基酸的评估对PTMS的SNP影响的检查出版者注：进行任何实验协议都需要遵守当地实验室安全和伦理的机构指南。翻译后修饰(PTM)是各种细胞过程中的关键调节机制；PTMS的改变可能会导致人类疾病。我们提出了一种使用Mind-S(多标签可解释的深度学习方法for PTM预测结构版)来研究PTMS的协议。该方案由循序渐进的指南组成，包括Mind-S的三个关键应用：基于蛋白质序列的PTM预测，重要氨基酸的识别，以及阐明分子突变导致的PTM格局的改变。
Post-translational modifications (PTMs) serve as key regulatory mechanisms in various cellular processes; altered PTMs can potentially lead to human diseases. We present a protocol for using MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version), to study PTMs. This protocol consists of step-by-step guide and includes three key applications of MIND-S: PTM predictions based on protein sequences, important amino acids identification, and elucidation of altered PTM landscape resulting from molecular mutations. For complete details on the use and execution of this protocol, please refer to Yan et al (2023). A protocol on MIND-S, a software program that supports multiple computational analyses on PTMs Steps described for performing multi-protein multi-PTM prediction Evaluation of important amino acids for PTM occurrences Examination of the SNP effect on PTMs Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Post-translational modifications (PTMs) serve as key regulatory mechanisms in various cellular processes; altered PTMs can potentially lead to human diseases. We present a protocol for using MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version), to study PTMs. This protocol consists of step-by-step guide and includes three key applications of MIND-S: PTM predictions based on protein sequences, important amino acids identification, and elucidation of altered PTM landscape resulting from molecular mutations.