In Silico Meta-Analysis of Orthologous Gene Networks in Drought-Tolerant Millets: A Comparative Computational Proteomics Evaluation for Functional Genomic Adaptations

Abstract

This study presents a non-linear in silico meta-analysis evaluating the structural configurations and evolution of orthologous gene networks across premium drought-tolerant millet species, specifically targeting Pennisetum glaucum (Pearl Millet) and Eleusine coracana (Finger Millet). Utilizing deep computational proteomics mining from verified NCBI, UniProt, and Ensembl Plants databases, we structuralized a functional mapping of Late Embryogenesis Abundant (LEA) protein families and Dehydrin clusters. By resolving orthologous networks via alignment-free sequence validation metrics, this paper identifies a highly conserved amino-acid sequence vector directly correlated with osmotic regulation under hyper-arid thresholds. The structural modeling displays a significant structural resilience in finger millet orthologs compared to traditional cereal frameworks. These metadata discoveries eliminate the necessity of wet-lab validation by introducing a predictive computational model for genomic adaptations, securing an uncompensated priority credit for distributed plant molecular biology workflows in 2026.