AstraROLE2 & AstraSUIT2: Multi-Task Annotation Models for Functional Profiling of Proteins

Most in-silico protein characterisation tools focus on only one aspect of protein function, forcing researchers to use multiple models or to bypass computational checks. Here we introduce AstraROLE2 and AstraSUIT2, two transformer-based, multi-task annotators that deliver an integrated functional profile in a single pass. A 1,351-dimensional input (ESM-2 CLS embeddings plus physicochemical Orbion enrichments) is mapped by a 512-unit encoder and task-specific linear heads: four in AstraROLE2 (EC class, GO term, molecular pathway, protein category) and nine in AstraSUIT2 (cofactor group, specific cofactor, domain, host, membrane association type, transmembrane helix number, subcellular localization, quaternary category, quaternary stoichiometry). Models were trained on 730k UniProt proteins with stratified 70/15/15 splits; class-weighted BCE and Optuna hyper-parameter search countered imbalance. On hold-out sets the heads reached macro F1=0.84–0.98 and MCC=0.85–0.98. Highest scores were seen for cofactor binding (0.98), membrane association type (F1=0.97) and top-level EC number (0.96); GO term classification was hardest (0.85). Against recent comparators (incl. DeepGOPlus and TargetP 2.0), the Astra models matched or exceeded performance, especially on metal-ion binding and cofactor binding. Additional tests on three novel proteins not included in initial dataset showed good predictions for most labels, underscoring the potential for hypothesis generation. Overall, AstraROLE2 and AstraSUIT2 supplied fast, state-of-the-art multi-label protein annotation within one unified model network.