COMPUTATIONAL IDENTIFICATION AND DOCKING-BASED FUNCTIONAL ANALYSIS OF NOVEL EXPRESSED PROTEINS FROM MORINGA OLEIFERA TRANSCRIPTOME

ABSTRACT Moringa oleifera is a highly valued medicinal and nutritional plant known for its rich phytochemical profile and diverse biological activities. Despite its importance, many of its expressed proteins remain uncharacterized, limiting the understanding of its molecular functions. This study employed a comprehensive computational approach to identify novel expressed proteins from publicly available Moringa oleifera transcriptome datasets and to predict their potential biological roles through molecular docking analyses. RNA-Seq reads were retrieved, quality-checked, and assembled into transcripts, followed by open reading frame prediction and annotation. Proteins lacking significant similarity to known sequences were classified as novel expressed proteins. A representative subset of these proteins was structurally modeled using homology- and AI-based prediction tools. Fifteen biologically relevant ligands—including phytochemicals (quercetin, kaempferol, rutin), amino acids/cofactors (L-arginine, ATP, NADH), and regulatory molecules (salicylic acid, glutathione, hydrogen peroxide)—were selected for docking studies using AutoDock Vina. Docking results revealed distinct binding affinities, suggesting possible enzymatic, regulatory, or antioxidant-related functions of the novel proteins. Interaction analyses highlighted key amino acids involved in ligand recognition, providing insights into potential biochemical pathways in M. oleifera. This study offers the first docking-based functional prediction of several uncharacterized M. oleifera proteins, laying the foundation for future functional genomics and biotechnological research.