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Protein post-translational modification (PTM) site prediction is afundamental task in bioinformatics. Several computational methods have beendeveloped to predict PTM sites. However, existing methods ignore the structureinformation and merely utilize protein sequences. Furthermore, designing a morefine-grained structure representation learning method is urgently needed as PTMis a biological event that occurs at the atom granularity. In this paper, wepropose a PTM site prediction method by Coupling of Multi-Granularity structureand Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically,multigranularity structure-aware representation learning is designed to learnneighborhood structure representations at the amino acid, atom, and wholeprotein granularity from AlphaFold predicted structures, followed by utilizingcontrastive learning to optimize the structure representations.Additionally,multi-scale sequence representation learning is used to extract contextsequence information, and motif generated by aligning all context sequences ofPTM sites assists the prediction. Extensive experiments on three datasets showthat PTM-CMGMS outperforms the state-of-the-art methods.

Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation