ABSTRACT
Malaria is the world's most fatal disease - causing up to 2.7 million deaths annually all over the world. The ability of organisms to develop resistance against existing antimalarial drugs exacerbates the problem. There is a clear cut need for more effective, affordable and accessible drugs that act by novel modes of action. Glutathione synthetase (GS) from Plasmodium falciparum represents an important potential drug target due to its defensive role; hence ceasing the respective metabolic step will destroy the parasite. A three dimensional model of Plasmodium GS was constructed by de novo modelling method and potential GS inhibitors were identified from a library of glutathione (GSH) analogues retrieved from Ligand-info database and filtered using Lipinski and ADME rules. Two common feature pharmacophore models were generated from the individual inhibitor clusters to provide insight into the key pharmacophore features that are crucial for the GS inhibition. Molecular docking of selective compounds into the predicted GS binding site revealed that the compound CMBMB was the best GS inhibitor when compared to the standard reference Chloroquine (CQ). This was taken as indicating that CMBMB was the best effective and safest drug against P. falciparum.
Subject(s)
Antimalarials/pharmacology , Enzyme Inhibitors/pharmacology , Glutathione Synthase/antagonists & inhibitors , Glutathione/pharmacology , Plasmodium falciparum/drug effects , Amino Acid Sequence , Antimalarials/chemistry , Binding Sites/drug effects , Drug Evaluation, Preclinical , Enzyme Inhibitors/chemistry , Glutathione/chemistry , Glutathione Synthase/chemistry , Glutathione Synthase/metabolism , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Molecular Sequence Data , Molecular Structure , Parasitic Sensitivity Tests , Plasmodium falciparum/enzymology , Sequence Alignment , Structure-Activity RelationshipABSTRACT
OBJECTIVES: To predict the structure of protein, which dictates the function it performs, a newly designed algorithm is developed which blends the concept of self-organization and the genetic algorithm. METHODS: Among many other approaches, genetic algorithm is found to be a promising cooperative computational method to solve protein structure prediction in a reasonable time. To automate the right choice of parameter values the influence of self-organization is adopted to design a new genetic operator to optimize the process of prediction. Torsion angles, the local structural parameters which define the backbone of protein are considered to encode the chromosome that enhances the quality of the confirmation. Newly designed self-configured genetic operators are used to develop self-organizing genetic algorithm to facilitate the accurate structure prediction. RESULTS: Peptides are used to gauge the validity of the proposed algorithm. As a result, the structure predicted shows clear improvements in the root mean square deviation on overlapping the native indicates the overall performance of the algorithm. In addition, the Ramachandran plot results implies that the conformations of phi-psi angles in the predicted structure are better as compared to native and also free from steric hindrances. CONCLUSIONS: The proposed algorithm is promising which contributes to the prediction of a native-like structure by eliminating the time constraint and effort demand. In addition, the energy of the predicted structure is minimized to a greater extent, which proves the stability of protein.
