Der Pharma Chemica
Journal for Medicinal Chemistry, Pharmaceutical Chemistry, Pharmaceutical Sciences and Computational Chemistry
ISSN: 0975-413X
Abstract
Target identification and validation for diabetic nephropathy using molecular docking studies
Author(s): Ramanjaneyulu M.*, K. Ananth Kumar, M. Shubash Kumar, Shivakanth Reddy, Divyadhatri Kara, Pravallika Mukthala, Rohith Raj and Chandaka MadhuDiabetic nephropathy is one of the chronic complications of diabetes mellitus. Inflammatory mediators are believed to play a vital role as predictors of low grade systemic inflammation in diabetic nephropathy. Drug designing, one of the hottest topics have found its new pathway to create a history in the field of medical science. The lead compound analysis starts with CADD, assisting to identify and to optimize the right compound. The technique helps in generating a suitable compound specific to the disease; thereby an effective treatment is achieved. Molecular modeling method has been used for modeling a new molecule for Diabetic nephropathy using Lisinopril , a drug that’s already designed. This drug is drawn using hyperchem, and its R group is modified by replacing different functional groups like OH, Br, CH2CH3, CH3, Cl, F, H, and NH2 , etc in its place and docked by using gold software.. The molecules designed as such are optimized using different algorithms and their affinity is checked with protein. The binding free energy of the protein is calculated by performing docking process. The molecule with minimum binding energy will have the maximum binding affinity. The binding free energy is calculated by the formula Z = Sum of the energy of optimized ligand devoid of solvation parameters and the energy of the protein - ligand optimization. The binding free energy of the designed molecules is obtained by eliminating the energy of the main molecule i.e. Lisinopril .From the results obtained it’s clear that ligand 1 & 5 ( -3.65 & -2.73 ) for Diabetic nephropathy have the maximum binding affinity. So these molecules are determined as the best lead molecules targeting computationally.
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