Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics

Author(s)
Doris A. Schuetz, Mattia Bernetti, Martina Bertazzo, Djordje Musil, Hans Michael Eggenweiler, Maurizio Recanatini, Matteo Masetti, Gerhard F. Ecker, Andrea Cavalli
Abstract

Computational approaches currently assist medicinal chemistry through the entire drug discovery pipeline. However, while several computational tools and strategies are available to predict binding affinity, predicting the drug-target binding kinetics is still a matter of ongoing research. Here, we challenge scaled molecular dynamics simulations to assess the off-rates for a series of structurally diverse inhibitors of the heat shock protein 90 (Hsp90) covering 3 orders of magnitude in their experimental residence times. The derived computational predictions are in overall good agreement with experimental data. Aside from the estimation of exit times, unbinding pathways were assessed through dimensionality reduction techniques. The data analysis framework proposed in this work could lead to better understanding of the mechanistic aspects related to the observed kinetic behavior.

Organisation(s)
Department of Pharmaceutical Chemistry
External organisation(s)
Università degli Studi di Bologna, Istituto Italiano di Tecnologia, Merck KGaA
Journal
Journal of Chemical Information and Modeling
Volume
59
Pages
535-549
No. of pages
15
ISSN
1549-9596
DOI
https://doi.org/10.1021/acs.jcim.8b00614
Publication date
01-2019
Peer reviewed
Yes
Austrian Fields of Science 2012
Pharmaceutical chemistry
ASJC Scopus subject areas
, , ,
Portal url
https://ucris.univie.ac.at/portal/en/publications/predicting-residence-time-and-drug-unbinding-pathway-through-scaled-molecular-dynamics(1a10f3b5-2e00-4419-bce6-d8522ef725ea).html