Journal Article

CRBPSA: CircRNA-RBP interaction sites identification using sequence structural attention model

Dai, Qi, Wang, Tao, Zou, Quan, Cao, Chao, Wang, Chunyu

2024

Overview

Background Due to the ability of circRNA to bind with corresponding RBPs and play a critical role in gene regulation and disease prevention, numerous identification algorithms have been developed. Nevertheless, most of the current mainstream methods primarily capture one-dimensional sequence features through various descriptors, while neglecting the effective extraction of secondary structure features. Moreover, as the number of introduced descriptors increases, the issues of sparsity and ineffective representation also rise, causing a significant burden on computational models and leaving room for improvement in predictive performance. Results Based on this, we focused on capturing the features of secondary structure in sequences and developed a new architecture called CRBPSA, which is based on a sequence-structure attention mechanism. Firstly, a base-pairing matrix is generated by calculating the matching probability between each base, with a Gaussian function introduced as a weight to construct the secondary structure. Then, a Structure_Transformer is employed to extract base-pairing information and spatial positional dependencies, enabling the identification of binding sites through deeper feature extraction. Experimental results using the same set of hyperparameters on 37 circRNA datasets, totaling 671,952 samples, show that the CRBPSA algorithm achieves an average AUC of 99.93%, surpassing all existing prediction methods. Conclusions CRBPSA is a lightweight and efficient prediction tool for circRNA-RBP, which can capture structural features of sequences with minimal computational resources and accurately predict protein-binding sites. This tool facilitates a deeper understanding of the biological processes and mechanisms underlying circRNA and protein interactions.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Apoptosis / Attention mechanism / Binding sites / Biological activity / Biomedical and Life Sciences / Cancer / Cardiomyocytes / Cell cycle / Circular RNA / Computational inference of protein conformations and interactions / Computer applications / Computer simulation / Computer-generated environments / Datasets / Feature extraction / Gene regulation / Genetic research / Information processing / Life Sciences / Mathematical models / Metastasis / Molecular structure / Neural networks / Protein binding / Protein interaction / Protein structure / Proteins / Research methodology / RNA processing / RNA-binding proteins / Secondary structure / Sequences / Software / Sparsity / Structural information / Structure-function relationships