Multiple e-pharmacophore modelling pooled with high-throughput virtual screening, docking and molecular dynamics simulations to discover potential inhibitors of Plasmodium falciparum lactate dehydrogenase (PfLDH)

Abstract

Development of new antimalarial drugs continues to be of huge importance because of the resistance of malarial parasite towards currently used drugs. Due to the reliance of parasite on glycolysis for energy generation, glycolytic enzymes have played important role as potential targets for the development of new drugs. Plasmodium falciparum lactate dehydrogenase (PfLDH) is a key enzyme for energy generation of malarial parasites and is considered to be a potential antimalarial target. Presently, there are nearly 15 crystal structures bound with inhibitors and substrate that are available in the protein data bank (PDB). In the present work, we attempted to consider multiple crystal structures with bound inhibitors showing affinity in the range of 1.4 × 10²–1.3 × 10⁶ nM efficacy and optimized the pharmacophore based on the energy involved in binding termed as e-pharmacophore mapping. A high throughput virtual screening (HTVS) combined with molecular docking, ADME predictions and molecular dynamics simulation led to the identification of 20 potential compounds which could be further developed as novel inhibitors for PfLDH.

Keywords:

• ADME
• e-pharmacophore
• molecular docking
• molecular dynamics simulation
• Plasmodium falciparum lactate dehydrogenase

Abbreviations:

• A, hydrogen bond acceptor
• ADME, absorption, distribution, metabolism and excretion
• % A, percentage of active
• CG, conjugate gradient
• D, hydrogen bond donor
• e-pharmacophore, energy-optimized pharmacophore
• EF, enrichment factor
• Glide XP, extra precision
• GH, goodness of hit
• H, hydrophobic
• HTVS, high throughput virtual screening
• MD, Molecular dynamics
• N, negative ionizable
• OPLS, optimized potential for liquid simulations
• PfLDH, Plasmodium falciparum lactate dehydrogenase
• PDB, protein data bank
• R, ring aromatic
• ROC, receiver operating characteristic
• RMSD, root mean square deviation
• RMSF, root mean square fluctuation
• SD, steepest descent
• SP, standard precision
• VdW, Van der Waals
• % Y, percentage of yield

Acknowledgement

The authors are thankful to the Prof. D Sriram, BITS-Pilani Hyderabad Campus for proving the assess of Schrödinger software. S.S. acknowledges the UGC-D.S. Kothari postdoctoral fellowship.