PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.

Introduction: Protein-DNA interactions are central to gene regulation, genome stability, and disease mechanisms. Identifying DNA-binding residues (DBRs) is critical for structural modeling, protein engineering, and therapeutic design. Although experimental approaches provide valuable insights, they remain low-throughput and resource-intensive. Computational methods offer scalable alternatives by leveraging protein sequential and structural information to predict DBRs. Methods: We present PRIMED (Protein Residue Inference using Multilayer perceptron for Enhanced DNA-binding predictions), a machine learning framework that integrates protein representations of distinct biochemical and structural properties from three protein language models: ESM-2, ESM-3, and ESM-C. These representations are concatenated and processed by a multilayer perceptron to perform DBR predictions. Results: PRIMED demonstrated strong performance across three benchmark datasets: Test-46 and Test-129 from a previous study, CLAPE-DB, and Test-10 K, which we curated from UniProtKB/Swiss-Prot. The model achieves an area under the Receiver Operating Characteristic curve (AUC) of 0.92 and a Matthews Correlation Coefficient (MCC) of 0.64 on Test-46, as well as an AUC of 0.93 and MCC of 0.45 on Test-129. On Test-10 K, PRIMED demonstrates generalizability across proteins with varying DBR percentages, maintaining competitive performance relative to the runner-up method, CLAPE-DB. Discussion: These results highlight the effectiveness of integrating diverse protein language model representations for accurate, transferable DBR predictions.