通过多粒度结构与多尺度序列表示耦合改进蛋白质翻译后修饰位点预测
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation
AI总结：
本文提出PTM-CMGMS方法，通过耦合多粒度结构表示（氨基酸、原子、蛋白质粒度）与多尺度序列表示，并利用对比学习和基序信息，在三个数据集上实现了优于现有方法的PTM位点预测性能。
摘要：
蛋白质翻译后修饰（PTM）位点预测是生物信息学中的一项基础任务。目前已开发出多种计算方法来预测PTM位点。然而，现有方法忽略了结构信息，仅利用蛋白质序列。此外，由于PTM是一种发生在原子粒度上的生物学事件，迫切需要设计一种更细粒度的结构表示学习方法。在本文中，我们提出了一种通过多粒度结构与多尺度序列表示耦合的PTM位点预测方法，简称为PTM-CMGMS。具体而言，设计了多粒度结构感知表示学习，从AlphaFold预测的结构中学习氨基酸、原子和整个蛋白质粒度上的邻域结构表示，随后利用对比学习优化结构表示。此外，多尺度序列表示学习用于提取上下文序列信息，通过比对PTM位点的所有上下文序列生成的基序辅助预测。在三个数据集上的大量实验表明，PTM-CMGMS优于最先进的方法。
英文摘要：
Protein post-translational modification (PTM) site prediction is a fundamental task in bioinformatics. Several computational methods have been developed to predict PTM sites. However, existing methods ignore the structure information and merely utilize protein sequences. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom granularity. In this paper, we propose a PTM site prediction method by Coupling of Multi-Granularity structure and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
