Hanjin Kim, Sung-Gwon Lee, Jooseong Oh, Kee K Kim, Eun-Mi Kim, Chungoo Park
This study aimed to introduce transformer-based classifiers for human DBPs and RBPs that rely solely on protein sequence information without engineered features or domain constraints. The models were implemented using ESM-2 with low-rank adaptation (LoRA) fine-tuning and trained on experimentally validated datasets, including chromatin immunoprecipitation sequencing (ChIP-seq) annotations for DBPs and eCLIP annotations for RBPs. Next, to evaluate biological relevance, we computed value-aware attention (VAT) scores aggregated across transformer layers to interpret model focus. In 20-fold cross-validation, the DBP model achieved an area under the receiver operating characteristic curve (AUROC) of 0.84 with a Matthews correlation coefficient (MCC) of 0.40, while the RBP model achieved an AUROC of 0.92 with an MCC of 0.46. Proteins predicted as nucleic acid-binding were enriched for known binding domains, and inspection of attention distributions revealed preferential focus on annotated functional regions rather than non-binding segments. These results demonstrate that attention-based protein language models can accurately identify nucleic acid-binding proteins directly from sequence data. Moreover, these models reveal biologically meaningful sequence determinants of binding, establishing an interpretable and scalable framework for proteome-wide characterization of protein-nucleic acid interactions.
MOTIVATION: Accurate identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) is critical for elucidating transcriptional and post-transcriptional regulatory mechanisms. However, existing computational approaches often rely on inferred labels or domain-specific annotations, which limit the subsequent generalizability.
RESULTS: This study aimed to introduce transformer-based classifiers for human DBPs and RBPs that rely solely on protein sequence information without engineered features or domain constraints. The models were implemented using ESM-2 with low-rank adaptation (LoRA) fine-tuning and trained on experimentally validated datasets, including chromatin immunoprecipitation sequencing (ChIP-seq) annotations for DBPs and eCLIP annotations for RBPs. Next, to evaluate biological relevance, we computed value-aware attention (VAT) scores aggregated across transformer layers to interpret model focus. In 20-fold cross-validation, the DBP model achieved an area under the receiver operating characteristic curve (AUROC) of 0.84 with a Matthews correlation coefficient (MCC) of 0.40, while the RBP model achieved an AUROC of 0.92 with an MCC of 0.46. Proteins predicted as nucleic acid-binding were enriched for known binding domains, and inspection of attention distributions revealed preferential focus on annotated functional regions rather than non-binding segments. These results demonstrate that attention-based protein language models can accurately identify nucleic acid-binding proteins directly from sequence data. Moreover, these models reveal biologically meaningful sequence determinants of binding, establishing an interpretable and scalable framework for proteome-wide characterization of protein-nucleic acid interactions.
AVAILABILITY AND IMPLEMENTATION: Code is available on GitHub (https://github.com/CSB-hub/DRBP).