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Updated: Feb 17, 2026

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
Published on: October 25, 2013
Identification of Novel Ligands for Therapeutic Lipopeptides: Daptomycin, Surfactin and Polymyxin
Satya Eswari Jujjavarapu1, Swasti Dhagat1, Vinay Kurrey1
1Department of Biotechnology, National Institute of Technology Raipur, Raipur, India.
Background:
Due to increase in antibiotic-resistance among pathogens, there is a need for potent and safe drugs. An alternative to antibiotics is lipopeptides which are produced as a secondary metabolite by many microorganisms. They exhibit broad-spectrum activities against pathogens along with anticancer properties. Among various lipopeptides produced by microorganisms, daptomycin, surfactin, and polymyxin have gained popularity as medicines but their mechanism of action is not described properly. In silico drug design of these lipopeptides becomes a challenge due to their complex structures. In order to initiate a physiological response, specific agonists (ligands) of their receptor (lipopeptide) must be identified.
Objective:
The objective of this study was molecular docking of three lipopeptides, daptomycin, surfactin and polymyxin, with their ligands as a means of drug design.
Method:
Schrödinger software was used for molecular docking of lipopeptides with their corresponding ligands whereas the ligand search was done using RCSB. Once the ligands were identified, they were docked with their corresponding lipopeptide. Docking score and glide energy were used as the parameters to test docking.
Results:
All four of the identified ligands were found to dock with daptomycin, whereas for both surfactin and polymyxin one out of two ligands docked with the lipopeptides.
Conclusion:
The knowledge of the docking sites and docking characteristics of the lipopeptide mentioned in the paper with the ligands can provide advantages of high speed and reliability, reduced costs on chemicals and experiments and the ethical issues concerned with the use of animal models for screening of drug toxicity.
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