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Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach

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Abstract

In this work, we analyze the structure–activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape analysis rapidly led to the identification of activity cliffs, scaffolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity cliffs, scaffold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identification of interactions with key residues involved in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this work is expected to further advance the development of G9a inhibitors.

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Funding

This research was funded by the School of Chemistry of the Universidad Nacional Autónoma de México (UNAM), the Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT) grant number IA203718, UNAM and the Consejo Nacional de Ciencia y Tecnología (CONACyT) grant number 282785. We also thank the program Nuevas Alternativas para el Tratamiento de Enfermedades Infecciosas NUATEI-UNAM for funding. We thank the Foundation for Applied Medical Research, University of Navarra (Pamplona, Spain), as well as Fundación Fuentes Dutor, for financial support.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by EL-L and OR. The first draft of the manuscript was written by EL-L and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to José L. Medina-Franco.

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López-López, E., Rabal, O., Oyarzabal, J. et al. Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach. J Comput Aided Mol Des 34, 659–669 (2020). https://doi.org/10.1007/s10822-020-00298-x

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