Maestro 9.4 as a Tool in the Structure Based Screening of Glycoalkaloids and Related Compounds, Targeting Aldose Reductase

Abstract

Diabetes mellitus is one of the alarming common diseases of this century. In India, according to the statistics of the International Diabetes Federation, 87 million of people are affected by Diabetes mellitus and this number is expected to cross 100 million by 2030. This has created a thrust for the development of new medicines. Recently, ban of pioglitazone, an oral anti-diabetic drug by Drugs Technical Advisory Board (DTAB) on account of its side effects, portrays the need for developing new drugs with less or no side effects. Cheminformatics tools assist in screening several millions of compounds and providing lead compounds in drug designing. This paper focuses on screening of lead compounds in arriving at newer drugs for Diabetes mellitus. Aldose reductase a cytosolic enzyme is the receptor to which selected lead compounds are docked. Glycoalkaloids (present in bitter melon) and related compounds were docked onto aldose reductase and based on the GLIDE score, structural modifications were carried out to arrive at the highest GLIDE score. A commercially available molecule recommended for Type 2 Diabetes mellitus was also taken for reference. Glycoalkaloids were found to possess high GLIDE score compared to standard. In order to analyze the competence of the Schrodinger software a comparison was made with an internet freeware Hex 6.3 version. The flexible receptor docking of Schrodinger was found to be more advantageous than the Hex 6.3. Key words: Diabetes mellitus, aldose reductase, glycoalkaloids, GLIDE score INTRODUCTIONHuman life span is on an increase and aging is also postponed in recent days. Innovative medicines play a profound role along with nutrition, sanitation and other public health measures in increasing the average life span of man. Still there are many of the most common human diseases that are not effectively treated by existing therapies. With the improved technologies, researchers are focusing on genes and proteins responsible for genetic disorders and common polygenic diseases such as diabetes, heart disease etc. The increased pressure from the cost of clinical investigations and insufficient sources of financial support in research promotes cheminformatics (Hughes et al., 2011;Van de Waterbeemd and Gifford, 2003;Guttula et al., 2011;Umamaheswari et al., 2012).In order to facilitate the speed and cost involved in the drug discovery, a number of computational methods are used. The successfulness of the methods depend on number of factors like ligand structure, target receptor structure etc. In molecular modeling, the crystal structure,

Mechanistic Insights into the Binding of Boar Salivary Pheromones and Putative Molecule with Receptor Proteins: A Comparative Computational Approach

“…The protein−ligand complex structures were generated using PyMOL and twodimensional (2-D) ligand interactions and visualized using Maestro−Schrodinger Suite. 35 Molecular Dynamics Simulation. The binding modes and stabilities of the protein−ligand complexes listed in Table 1 were investigated in a dynamic environment using the GROMACS version 2018.2 36 and CHARMM27 force field over a simulation period of 50 ns.…”

“…The selection of the best-docked conformation was based on several criteria, including binding affinity, low root-mean-square deviation (RMSD) value (<1 Å), and hydrogen bond (H-bond) interactions between the ligand and the protein within the complex. The protein–ligand complex structures were generated using PyMOL and two-dimensional (2-D) ligand interactions and visualized using Maestro–Schrödinger Suite …”

In silico studies: Physicochemical properties, drug score, toxicity predictions and molecular docking of organosulphur compounds against Diabetes mellitus

“…The docking helps predict the most potent binding energy score functions when the ligand binds to a receptor protein, inhibiting its function from acting as a drug. The specific parameters used for optimising the docking process, which includes, 23 • Correlation type-shape only • Grid dimension-0.6…”

“…The GI absorption, P-glycoprotein substrate, predicts the level of compound absorption. The results of GIA exhibit good oral absorption except compounds numbers2,5,7,8,12,14,18,19,21,22,23,28,30,31, 32, 33, 35, 36, and 37. For BBB permeability, except 2, 5, 18, and 30, all other compounds possess a good permeability level, and one of the major roles of P-gp substrate or inhibitor is to protect the central nervous system.…”

In silico vetting of vinyl quinoline derivatives as potent Anti- Diabetic leads

“…Computer aided software is used to determine the drug likeliness of numerous compounds, including natural products and synthetic compounds. The commercial software Schrödinger and computer freeware Hex were used and reported to screen thousands of lead compounds for its in silico biological activity [13,14] Thus the present study aims to screen vinyl quinoline lead compounds for its anti-diabetic potential as potent DPP IV inhibitor using the commercial Schrödinger software and Hex freeware. The toxicity of these class of vinyl quinolines are determined by Toxtree software.…”