Machine learning analysis of [tau]RAMD trajectories to decipher molecular determinants of drug-target residence times

• Verfasst von: Kokh, Daria B. [VerfasserIn]
• Verfasst von: Kaufmann, Tom [VerfasserIn]
• Verfasst von: Kister, Bastian [VerfasserIn]
• Verfasst von: Wade, Rebecca C. [VerfasserIn]
• Titel: Machine learning analysis of [tau]RAMD trajectories to decipher molecular determinants of drug-target residence times
• Verf.angabe: Daria B. Kokh, Tom Kaufmann, Bastian Kister, Rebecca C. Wade
• E-Jahr: 2019
• Jahr: 24 May 2019
• Umfang: 17 S.
• Fussnoten: Gesehen am 11.07.2019 ; Im Titel ist "tau" als griechischer Buchstabe dargestellt
• Titel Quelle: Enthalten in: Frontiers in molecular biosciences
• Ort Quelle: Lausanne : Frontiers, 2014
• Jahr Quelle: 2019
• Band/Heft Quelle: 6(2019) Artikel-Nummer 36, 17 Seiten
• ISSN Quelle: 2296-889X
• DOI: doi:10.3389/fmolb.2019.00036
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: drug-protein residence time
• Sach-SW: drug-target binding kinetics
• Sach-SW: Heat shock protein 90 (hsp90)
• Sach-SW: machine learning
• Sach-SW: Molecular Dynamics Simulation
• Sach-SW: structure-kinetic relationships (SKRs)
• Sach-SW: tauRAMD
• K10plus-PPN: 1668998327
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, tau, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the tauRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce tau. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating tau and to decipher the molecular features leading to longer tau values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of tau for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of tau. Thus, we find that machine learning helps to overcome inaccuracies in the modelling of the bound protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the tauRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.