DeepPWM-BindingNet: Unleashing Binding Predictionwith Combined Sequence and PWM Features

Abstract

A crucial challenge in molecular biology is the prediction of DNA-protein binding interactions, which has applications in the study of gene regulation and genome functionality. In this paper, we present a novel deep-learning framework to predict DNA-protein binding interactions with increased precision and interoperability. Our proposed framework DeepPWM-BindingNet leverages the rich information encoded in Position Weight Matrices (PWMs), which capture the sequence-specific binding preferences of proteins. These PWM-derived features are seamlessly integrated into a hybrid model of convolutional recurrent neural networks (CRNNs) that extracts hierarchical features from DNA sequences and protein structures. The sequential dependencies within the sequences are captured by recurrent layers. By incorporating PWM-derived features, the models interpretability is improved, enabling researchers to learn more about the underlying binding mechanisms. The models capacity to locate crucial binding sites is improved by the incorporation of an attention mechanism that highlights crucial regions. Experiments on diverse DNA-protein interaction datasets demonstrate the proposed approach improves the predictive performance. The proposed model holds significant potential in deciphering intricate DNA-protein interactions, ultimately advancing our comprehension of gene regulation mechanisms.