Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα.

Matej Janežič,Simona Golič Grdadolnik,Kaja Bergant Loboda,Iza Ogris,Andrej Perdih,Gerhard Wolber,Marjetka Podobnik,Mirijam Kozorog,Barbara Herlah,Katja Valjavec

doi:10.3390/ijms222413474

Abstract

In this study, we utilized human DNA topoisomerase IIα as a model target to outline a dynophore-based approach to catalytic inhibitor design. Based on MD simulations of a known catalytic inhibitor and the native ATP ligand analog, AMP-PNP, we derived a joint dynophore model that supplements the static structure-based-pharmacophore information with a dynamic component. Subsequently, derived pharmacophore models were employed in a virtual screening campaign of a library of natural compounds. Experimental evaluation identified flavonoid compounds with promising topoisomerase IIα catalytic inhibition and binding studies confirmed interaction with the ATPase domain. We constructed a binding model through docking and extensively investigated it with molecular dynamics MD simulations, essential dynamics, and MM-GBSA free energy calculations, thus reconnecting the new results to the initial dynophore-based screening model. We not only demonstrate a new design strategy that incorporates a dynamic component of molecular recognition, but also highlight new derivates in the established flavonoid class of topoisomerase II inhibitors.

Highlights

Virtual screening of molecular libraries is a routine process for which an increasing number of tools are available [1]
With the proposed design strategy, we aimed to incorporate a dynamicsite component of molecular recognition of known ligands that bind to the ATP-binding of topo IIα ofinto molecular recognition of known ligands that bind to the ATP-binding site of achieve topo IIαthis a structure-based pharmacophore that can be used in virtual screening
To into a structure-based pharmacophore can be dynamics used in virtual screening

Summary

Introduction

Virtual screening of molecular libraries is a routine process for which an increasing number of tools are available [1]. Due to the high computational and time costs, the flexibility of proteins is often neglected, even though pioneering reports on their importance were published decades ago [2,3,4]. With the increasing affordability of computing power and the development of efficient algorithms, dynamic molecular design methods are becoming more common [5,6]. We have explored the dynamic nature of proteins in the past, from the rudimentary use of multiple structures [7], to analyzing binding pockets and studying the role of water molecules via simulations [8,9]. In silico methods of molecular design can be divided into ligand-based and structure-based methods, the latter requiring the structural data of the target molecule.

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Conclusion