- Marc Bianciotto Sanofi Aventis Recherche & Développement ,
- Paraskevi Gkeka Sanofi Aventis Recherche & Développement ,
- Daria B. Kokh Heidelberg Institute for Theoretical Studies ,
- Rebecca Wade Heidelberg Institute for Theoretical Studies & Heidelberg University ,
- Minoux Hervé Sanofi Aventis Recherche & Développement
<div>The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-demanding experimental counterparts, even at an early drug discovery stage.</div><div>Herein, we perform Scaled Molecular Dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than 7 chemical series in order to estimate their relative residence time. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state and the second one, based on a new contact map fingerprint, allows the description and the comparison of the paths that lead to unbinding.</div><div>Using these analyses, we find that ScaledMD’s relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.</div>
Updated after review.
Bianciotto et al 2021 contact map FP SI