Feature Extraction Approach for Predicting Protein-DNA Binding Residues Using Transformer Encoder-Decoder Architecture

In the realm of biology, the effects of protein binding with other molecules are of paramount importance, especially in the context of DNA binding. Precisely identifying the residues implicated in protein-DNA binding is crucial for gaining a more profound insight into the mechanisms governing protein-DNA interactions. The majority of existing methods presently utilize a two-step approach, which is plagued by drawbacks including low prediction efficiency and poor usability, thereby constraining their practical applicability. In the present study, we propose a novel method grounded in sequence-to-sequence (seq2seq) models. This model has the capability to accept variable-length complete protein sequences as input and employs Transformer encoder blocks along with feature extraction blocks for hierarchical feature extraction. Through this approach, our objective is to augment the identification capability of protein-DNA binding residues. We conducted comparative experiments on the benchmark datasets, with the results demonstrating the remarkable effectiveness of our proposed method in identifying protein-DNA binding residues. This approach presents a promising new avenue for tackling research on protein-DNA interactions.