Estimation of drug-target residence times by t-random acceleration molecular dynamics simulations
Drug-target residence time (t), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, t-random acceleration molecular dynam...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
16 May 2018
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| In: |
Journal of chemical theory and computation
Year: 2018, Volume: 14, Issue: 7, Pages: 3859-3869 |
| ISSN: | 1549-9626 |
| DOI: | 10.1021/acs.jctc.8b00230 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1021/acs.jctc.8b00230 Verlag, Volltext: https://doi.org/10.1021/acs.jctc.8b00230 
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| Author Notes: | Daria B. Kokh, Marta Amaral, Joerg Bomke, Ulrich Grädler, Djordje Musil, Hans-Peter Buchstaller, Matthias K. Dreyer, Matthias Frech, Maryse Lowinski, Francois Vallee, Marc Bianciotto, Alexey Rak, Rebecca C. Wade |
| Summary: | Drug-target residence time (t), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, t-random acceleration molecular dynamics (tRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed tRAMD on a data set of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that affect ligand unbinding rates, including transient polar interactions and steric hindrance. These results suggest that tRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization. |
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| Item Description: | Published online 16 May 2018 Gesehen am 17.0ß7.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1549-9626 |
| DOI: | 10.1021/acs.jctc.8b00230 |