technical paper

AAAI 2024

February 24, 2024

Vancouver , Canada

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

keywords:

post-translational modification (ptm),multi-granularity structure ,multi-scale sequence representation

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. These methods consequently fail to correctly identify PTM sites that show low conservation at the sequence level. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom scale. 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, a multi-granularity structure-aware representation learning, including the atom, amino acid, and whole-protein granularity, is designed to learn neighborhood structure representations from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations. Additionally, the multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of positive sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods.

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