Machine learning guided structural dynamics identifies translation elongation factor 1 (EEF1A1) as an immunological biomarker and marine natural products as therapeutic leads for rheumatoid arthritis with major depressive disorder.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that predominantly affects synovial joints, especially those of the hands, elbows, wrists, knees, and shoulders. RA frequently co-occurs with major depressive disorder (MDD), amplifying disease burden and complicating clinical outcomes. This study employed a multi-step integrative bioinformatics and structural biology framework to identify candidate molecular biomarkers for RA and MDD. Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the epitranscriptomic dataset. These analyses identified immune-regulatory gene modules that were significantly associated with both phenotypes. Least absolute shrinkage and selection operator (LASSO) regression was applied to select strong, statistically significant biomarkers. The methylated biomarker EEF1A1 was identified, and its structure predicted via AlphaFold, was subjected to in silico structure-based virtual screening (SBVS) against the Comprehensive Marine Natural Product Database (CMNPD). Four marine natural products (CMNPD17984, CMNPD27318, CMNPD26200, and CMNPD26011) showed significant binding affinity for EEF1A1. Furthermore, EEF1A1-MNP complexes were simulated for 150 ns using GROMACS, and PCA-based free energy landscape (FEL) analyses were performed to characterize the dynamic behavior and identify energy minima. This integrated computational approach provides a comprehensive platform for biomarker discovery and validation in RA and MDD, with potential applications in early diagnosis, therapeutic targeting, and precision medicine.