Evaluation of Inhibitory Affinity Potential of the Alkaloids Against Crystal Structure of Human Angiotensin-Converting Enzyme Using Lamarckian Genetic Algorithm

Abstract

Angiotensin-converting enzyme is known to be principally responsible for hypertension. Three dimensional quantitative structure-activity relationship models have been constructed using the comparative molecular field analysis. Alkaloids are the active constituents that may play a crucial role in preventing the hypertension. In this perspective, alkaloids like codeine, colchinine, hyoscyamine, physostigmine, and reserpine were selected. Captopril, a known angiotensin converting enzyme inhibitor was used as the standard. In an attempt to identify inhibitory affinity potential of the alkaloids, against crystal structure of human angiotensin-converting enzyme (4APH) using AutoDock 4.2. In the docking studies, three important parameters like binding energy, inhibition constant, intermolecular energy and internal energy were determined. In the results, selected compounds and the standard showed the amino acid residues responsible for the inhibitory affinity potential with the target along with hydrogen bonding interactions was found to be ASN 85, MET 86, GLN 87, ILE 88, ALA 89, ASN 90, HIS 91, LEU 132. The selected compounds exhibited the binding energy ranging between –9.14 to –7.17 kcal/mol when compared with that of the standard (–5.38 kcal/mol). Inhibition constant (201.31 nM to 5.59 µM), intermolecular energy (–12.12 to –7.76 kcal/mol) and internal energy (–1.71 to –0.22 kcal/mol) of the alkaloids also coincide with the binding energy. Hence, these selected compounds can be further investigated to develop potential chemical entities for the prevention and treatment of hypertension.

Keywords: Docking studies, binding energy, inhibition constant, intermolecular energy, hypertension

Cite this Article:

Madeswaran A, Asokkumar K. Evaluation of Inhibitory Affinity Potential of the Alkaloids against Crystal Structure of Human Angiotensin-Converting Enzyme Using Lamarckian Genetic Algorithm. Research and Reviews: Journal of Computational Biology (RRJoCB). 2015; 4(1): 1–6p.