An integrated computational approach to identify GC minor groove binders using various molecular docking scoring functions, dynamics simulations and binding free energy calculations

Abstract

Understanding the DNA-ligand interaction mechanism is of utmost importance to design selective inhibitors targeting the GC- and AT-rich DNA. This forms a primary strategy to block the association of transcription factors to promoters and subsequently, reduce the expression of genes. We present here an integrated approach combining various docking scoring functions, selective ligand-based pharmacophore models, molecular dynamics simulations and binding free energy calculations to prioritize natural compounds specific to GC minor groove binding. The approach initially applies a selective ligand-based pharmacophore model built upon known GC minor groove binders to identify potential GC minor groove binders from natural compound repositories. These GC minor groove binders were then cross-examined with selective pharmacophore models (controls) based on AT-rich binders and GC intercalators to assess its unfitness. This approach involves the calculation of binding energies of known GC- and AT minor groove binders using three scoring functions without any constraint on groove specificity of GC- and AT-rich DNA. The evaluation of empirical scoring functions led to enumeration of a new parameter, the energy difference computed using Glide (sensitivity = 80%) to recognize GC-rich binders effectively. Molecular dynamics simulations and binding free energy calculations (MM/GBSA) constituted the final phase of this approach to analyze the interactions of natural molecules (hits) with GC-rich DNA comprehensively. Seven natural molecules were selected which exhibited fewer fluctuations in RMSD and RMSF profiles and better GC-rich DNA binding with low free energies of binding. These natural hits prioritized by this integrated approach can be tested in DNA binding assay.

Communicated by Ramaswamy H. Sarma

Keywords:

• GC minor groove binders
• computational approach
• dynamic simulations
• structure-based drug design
• MM/GBSA

Acknowledgments

Mehul Soni (IF150289) would like to acknowledge financial support from DST, New Delhi as Innovation in Science Pursuit for Inspired Research (INSPIRE) Fellowship. The authors thank the financial support of Gujarat State Biotechnology Mission (GSBTM), Department of Science and Technology (DST, Govt. of Gujarat)and Gujarat Council onScience and Technology (GUGCOST). The authors thank Gujarat University for providing the academic license of Schrödinger v2018.

Disclosure statement

The authors declare that no conflict of interest exists.

Additional information

Funding

This work was financially supported by the INSPIRE fellowship, Financial Assistance Programme- Department of Science and Technology [Grant Number GSBTM/MD/JDR/1409/2017-18] and Gujarat Council on Science and Technology [GUJCOST/Supercomputer/2019-20/1359].