A hybrid deep learning-based method for rice protein lysine acylation (PLA) site prediction
As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights.
The developed geometric deep learning framework includes 1) a multi-track encoder module to concurrently embed the protein structure and sequence features into a latent representation; 2) a decoder layer consisting of an attention layer and a multi-layer perceptron followed with a sigmoid function for downstream classification. The sequence encoder is designed as hybrid deep learning neural networks to learn dependencies between residues with two-track feature encoders and two-track adaptive encoders. Adaptive encoders enable learn-from-data for SLAM by using learnable word embeddings, and feature encoders provide expert-level information and evolutionary constraints extracted from protein language model. For structure encoder, a multi-layer graph neural network (GNN) is implemented to capture high-level residue relationships considering geometry.
