Protein-peptide interaction region residues prediction using a generative sampling technique and ensemble deep learning-based models

Motivation

Predicting protein-peptide interactions advances the understanding of drug design, protein biological functions, and cellular processes. Researchers have proposed various experimental and computational methods to identify interactions between proteins and peptides. However, traditional experimental approaches are laborious, time-consuming, and inefficient. Motivated by these challenges, a novel computational method is developed to detect protein-peptide interaction region residues from protein data, providing a complementary approach to experimental techniques.

Method

We designed a computational method for identifying protein-peptide interaction region residues, by incorporating a generative sampling technique with ensemble deep learning (DL) model using various features derived from protein sequences and structures. The proposed method relied on three pipelines: pre-processing, processing, and post-processing. The pre-processing pipeline converted the amino acid sequence into an image-like input representation to capture vital residue interactions. Also to overcome class imbalance challenge and non-binding over-predicting drawback, it employs a generative sampling technique for balancing the training data. Afterwards, to achieve more reliable prediction of protein-peptide interaction, a processing pipeline is designed that incorporates three independent DL sub-models. Subsequently, in the post-processing pipeline to obtain final prediction results, the outputs of ensemble DL modules are applied to three layers convolutional neural network.

Results

Compared to state-of-the-art sequence- and structure-based methods, the proposed method achieved the highest performance in F-measures (improved by 22.1 %), precision (improved by 3.9 %), and better balance between sensitivity and specificity. Eventually, our various experiments validated the effectiveness of the proposed method as a reliable computational assistant for predicting protein-peptide interaction region residues.