Pharmacophore modeling, docking and the integrated use of a ligand- and structure-based virtual screening approach for novel DNA gyrase inhibitors: synthetic and biological evaluation studies

药效团建模、对接以及基于配体和结构的虚拟筛选方法的综合应用,用于新型 DNA 旋转酶抑制剂的合成和生物学评价研究

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作者:Deepti Mathpal, Mukesh Masand, Anisha Thomas, Irfan Ahmad, Mohd Saeed, Gaffar Sarwar Zaman, Mehnaz Kamal, Talha Jawaid, Pramod K Sharma, Madan M Gupta, Santosh Kumar, Swayam Prakash Srivastava, Vishal M Balaramnavar

Abstract

Fluoroquinolones, a class of compound, act via inhibiting DNA gyrase and topoisomerase IV enzymes. This is an important class of drugs with high success rates for the treatment of tuberculosis and other bacterial infections. An indirect drug design approach was used to develop a meaningful pharmacophore model using the HypoGen module of Discovery Studio 2.0 on a set of 27 structurally diverse compounds with a wide range of biological activity (5 log units). The best hypothesis had three hydrogen bond acceptors (HBA) and one hydrophobic (Hy) moiety, showing r = 0.95, and it predicts the test set of 44 compounds well, with r 2 = 0.823. The same features (acceptor and hydrophobic functionality) were validated at the binding site of the DNA gyrase active site using GOLD version 3.0.1 and Molegro Virtual Docker, which showed corresponding hydrogen bond interactions and also π-π stacking interactions that correlated well with the PIC50 values (r 2 = 0.6142). The thoroughly validated model was used to screen an extensive database of 0.25 million compounds to identify potential leads. The validated model was implemented for the identification, design, synthesis, and biological evaluation of leads. Ten new chemical entities were synthesized based on our scaffold hopping techniques from the identified virtual screening and tested against the tuberculosis bacterium to obtain preliminary MIC values. The results showed that 3 out of 10 synthesized compounds exhibited good MICs, from 1.25 to 50 μM. This proves the robustness and applicability of the developed model, which is a promising tool for identifying new topoisomerase II inhibitors for the treatment of tuberculosis.

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