Improving PTM Site Prediction by Coupling of Multi-Granularity Structure
  and Multi-Scale Sequence Representation
Zhengyi Li Menglu Li Lida Zhu Wen Zhang
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.