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The serine/threonine kinase SGK1 is an activator of the β-catenin pathway and a powerful stimulator of cartilage degradation that is found to be upregulated under genomic control in diseased osteoarthritic cartilage. Today, no oral disease-modifying treatments are available and chronic treatment in this indication sets high requirements for the drug selectivity, pharmacokinetic, and safety profile. We describe the identification of a highly selective druglike 1

We here report on non-equilibrium targeted Molecular Dynamics simulations as tool for the estimation of protein-ligand unbinding kinetics. Correlating simulations with experimental data from SPR kinetics measurements and X-ray crystallography on two small molecule compound libraries bound to the N-terminal domain of the chaperone Hsp90, we show that the mean non-equilibrium work computed in an ensemble of trajectories of enforced ligand unbinding is a promising predictor for ligand unbinding rates. We furthermore investigate the molecular basis determining unbinding rates within the compound libraries. We propose ligand conformational changes and protein-ligand nonbonded interactions to impact on unbinding rates. Ligands may remain longer at the protein if they exhibit strong electrostatic and/or van der Waals interactions with the target. In the case of ligands with rigid chemical scaffold that exhibit longer residence times however, transient electrostatic interactions with the protein appear to facilitate unbinding. Our results imply that understanding the unbinding pathway and the protein-ligand interactions along this path is crucial for the prediction of small molecule ligands with defined unbinding, This unedited version of the article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Chemical Society. This article appeared in J. Chem. Inf. Model. (2019), 10.1021/acs.jcim.9b00592 and may be found at https://pubs.acs.org/doi/10.1021/acs.jcim.9b00592

Drug-target residence time (τ), 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, τ-random acceleration molecular dynamics (τRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD 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 τRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization.

A key component to success in structure-based drug design is reliable information on protein-ligand interactions. Recent development in NMR techniques has accelerated this process by overcoming some of the limitations of X-ray crystallography and computational protein-ligand docking. In this work we present a new scoring protocol based on NMR-derived interligand INPHARMA NOEs to guide the selection of computationally generated docking modes. We demonstrate the performance in a range of scenarios, encompassing traditionally difficult cases such as docking to homology models and ligand dependent domain rearrangements. Ambiguities associated with sparse experimental information are lifted by searching a consensus solution based on simultaneously fitting multiple ligand pairs. This study provides a previously unexplored integration between molecular modeling and experimental data, in which interligand NOEs represent the key element in the rescoring algorithm. The presented protocol should be widely applicable for protein-ligand docking also in a different context from drug design and highlights the important role of NMR-based approaches to describe intermolecular ligand-receptor interactions.