Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations

Abstract

In silico virtual screening (VS) is a powerful hit identification technique used in drug discovery projects that aims to effectively distinguish true actives from inactive or decoy molecules. To better capture the dynamic behavior of protein drug targets, compound databases may be screened against an ensemble of protein conformations, which may be experimentally determined or generated computationally, i.e. via molecular dynamics (MD) simulations. Several studies have shown that conformations generated by MD are useful in identifying novel hit compounds, in part because structural rearrangements sampled during MD can provide novel targetable areas. However, it remains difficult to predict a priori when an MD conformation will outperform a VS against the crystal structure alone. Here, we assess whether MD conformations result in improved VS performance for six protein kinases. MD conformations are selected using three different methods, and their VS performances are compared to the corresponding crystal structures. Additionally, these conformations are used to train ensembles, and their VS performance is compared to the individual MD conformations and the corresponding crystal structures using receiver-operating characteristic curve (ROC) metrics. We show that performing MD results in at least one conformation that offers better VS performance than the crystal structure, and that, while it is possible to train ensembles to outperform the crystal structure alone, the extent of this enhancement is target dependent. Lastly, we show that the optimal structural selection method is also target dependent and recommend optimizing virtual screens on a kinase-by-kinase basis to improve the likelihood of success.

In vitro and in silico identification of the mechanism of interaction of antimalarial drug – artemisinin with human serum albumin and genomic DNA

“…Particle-Mesh Ewald (PME) was used for electrostatics with a cut-off of 1.0 Å. The simulation was run 4 times (400 ns each) which resulted a total of 1.6 μ s. To obtain the most common HSA conformation, cluster analysis was performed using the Gromos RMSD-based method [38] and the criteria were based on the work λ of Offutt et al [39]. The molecular dynamics simulations were performed on the HPC of Lomonosov Moscow State University [40].…”

“…The Gromos method developed by Daura et al [38] based on the calculation for each point the number of other points for which the RMSD is less than a given cutoff. In our case, the cutoff value was chosen 0.26, according to the 3 criteria described in [39]. As a result, we obtained 36 clusters, where the first cluster was 43.25% of the total number of conformations (Fig.…”

Benchmarking ensemble docking methods as a scientific outreach project

“…Here, we perform MD simulations of the receptor protein, CatS, to obtain unique conformational states and introduce structural variation in the binding site. MD simulations allow the exploration of multiple conformations of the protein while in a solute-based, native environment (25, 26). This concept of selecting naturally-occurring conformations through MD for ensemble docking is known as the Relaxed Complex Scheme (26–31).…”

“…MD simulations allow the exploration of multiple conformations of the protein while in a solute-based, native environment (25, 26). This concept of selecting naturally-occurring conformations through MD for ensemble docking is known as the Relaxed Complex Scheme (26–31). MD-generated ensembles of flexible binding sites have been used successfully in a number of studies to identify lead compounds (13, 32–35).…”

“…Many studies have successfully used clustering methods in ensemble docking to extract relevant conformations, such as those based on RMSD (26, 34), QR factorization (13, 33), and active pocket volume (26). However, choosing the most appropriate clustering method for a system is still challenging and often dependent on human intuition.…”