CoSEF-DBP: Convolution scope expanding fusion network for identifying DNA-binding proteins through bilingual representations

Abstract

Precisely recognizing DNA-binding proteins (DBPs) from sequences is crucial for a profound comprehension of the mechanisms governing protein-DNA interactions in various cellular processes. However, traditional in-silico methods for DBP identification encounter several challenges, such as time-consuming evolutionary modeling based on multiple sequence alignments, and intricate feature engineering associated with machine or deep learning approaches. In this paper, we introduce a novel end-to-end predictor for identifying DNA-binding proteins without intricate feature engineering, which innovatively enriches the semantics of amino acid sequences through the fusion of bilingual representations derived from distinct language models. We further design a convolution scope expanding (CoSE) module to widen the receptive fields of convolution kernels, thereby forming protein-level CoSE representation sequences. These representations are subsequently integrated via BiLSTM in conjunction with a simplified capsule network, enhancing the hierarchical feature extraction capability. Extensive experiments confirm that our model surpasses existing baselines across diverse benchmark datasets, notably achieving at least a 5.1% improvement in MCC value on the UniSwiss dataset.