MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085365 A) filed by Sri Eshwar College Of Engineering on July 11, 2026, for Computational Platform For Precision Discovery Of Milk-Derived Metabolites Stabilizing Disease- Associated Bdnf Variants.
Inventors include P. Santhiya; and J. Manikandan.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The current invention relates to a computational tool that allows the precision discovery and prioritization of bioactive metabolites found in milk that may stabilize mutated forms of brain-derived neurotrophic factor (BDNF) associated with neurodegenerative and neuropsychiatric disorders. The BDNF is an essential neurotrophin whose primary physiological functions include neuron survival, neuronal differentiation, synaptic plasticity, memory, and cognition processes such as learning and memory formation. Pathogenic mutations may lead to structural changes within BDNF and thereby affect its physiological functions, ultimately leading to diseases such as Alzheimer's disease (AD) and major depressive disorder (MDD). This invention integrates computational methodologies involving PTM-guided mutation identification, structural modeling, virtual screening, molecular docking, molecular dynamics simulations, PCA, and FEL evaluation for identifying potential metabolites that could restore the structural stability of BDNF variants associated with diseases. PTM-associated mutations in BDNF were identified using dbPTM database, and disease association was verified by querying the Human Gene Mutation Database (HGMD). In accordance with PTM and associated diseases, rs1048218 (Gln75His; Q75H) and rs1048220 (Arg125Met; R125M) variants of BDNF were chosen for structural and computational analyses. This invention employs libraries of milk metabolites that contain metabolites derived from human breast milk acquired from the HMDB and bovine milk metabolites acquired from MCDB. The selected BDNF variants were virtually screened using a molecular docking technique in order to identify metabolites possessing good binding properties. Subsequently, these metabolites were validated through molecular dynamics simulation studies to analyze protein-ligand stability, conformational behavior, intermolecular interaction, and structural restoration due to mutations. In order to improve the accuracy of candidate identification, the present invention takes into consideration various structural and dynamical factors, such as Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA), as well as hydrogen bond formation capabilities. Additionally, the present invention makes use of PCA in order to study collective protein movements and conformational dynamics, and FEL for the analysis of energetically favorable conformations of the protein during metabolite binding. These analyses can help identify how these metabolites affect the protein stability. Based on the developed computational platform, it was possible to screen metabolites that originate from milk, such as Thymidine, Biochanin A, and Ala-Phe-Ala, as potential stabilizers of the target proteins. Additionally, the present invention proposes a method of ranking the candidates based on their docking performance, structural stability, conformational dynamics, and free energy properties.
Disclaimer: Curated by HT Syndication.
