SMD-Based Interaction-Energy Fingerprints Can Predict Accurately the Dissociation Rate Constants of HIV-1 Protease Inhibitors

Abstract

Recent research has increasingly suggested that the crucial factors affecting drug potencies are related not only to the thermodynamic properties but also to the kinetic properties. Therefore, in silico prediction of ligand-binding kinetic properties, especially the dissociation rate constant (k off ), has aroused more and more attention. However, there are still a lot of challenges that need to be addressed. In this paper, steered molecular dynamics (SMD) combined with residue-based energy decomposition was employed to predict the dissociation rate constants of 37 HIV-1 protease inhibitors (HIV-1 PIs). For the first time, a predictive model of the dissociation rate constant was established by using the interaction-energy fingerprints sampled along the ligand dissociation pathway. On the basis of the key fingerprints extracted it can be inferred that the dissociation rates of 37 HIV-1 PIs are basically determined in the first half of the dissociation processes and that the H-bond interactions with active-site Asp25 and van der Waals interactions with flap-region Ile47 and Ile50 have important influences on the dissociation processes. In general, the strategy established in this paper can provide an efficient way for the prediction of dissociation rate constants as well as the unbinding mechanism research.

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

“…Comparison of the three data-sets, with different definitions of the transition part of the trajectory, shows that the residence time strongly depends on the interaction of the ligand with residues of the binding cavity, when most of the bound state protein-ligand contacts are still preserved. This is in accord with the recent calculations of relative residence times for HIV-1 protease inhibitors (Huang et al, 2019) and HIV-1 protease and HSP90 inhibitors (Ganotra and Wade, 2018), which demonstrated that protein-ligand contacts in the complex could be used to deduce ligand residence times. From the linear regression model, as well as from clustering analysis, we found out that the interaction of the ligand with F138 is very important.…”

“…While compounds with a common scaffold are required for good prediction of the equilibrium dissociation constant, K D , a good prediction of the off-rate could be obtained for a dataset of diverse compounds from analysis of the bound protein-ligand complexes (Ganotra and Wade, 2018) suggesting that differences in the unbound state are less important for off-rate than for binding affinity. Huang et al (2019) applied PLS analysis to interaction-energy fingerprints extracted from snapshots of steered MD ligand dissociation trajectories to obtain a predictive model for residence time for a set of HIV-1 protease inhibitors and found that important interactions for determining τ were in the first half of the dissociation processes. This is consistent with a previous steered MD study of HIV-1 protease inhibitor dissociation in which the strength of the ligand-protein hydrogen bond network of the bound state was found to be crucial for the dissociation process (Li et al, 2011), as well as with the above-mentioned models based solely on analysis of the bound state.…”

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

“…While applying biasing force restraints on explicit-solvent models allows for faster approximate calculations in SMD simulations, this leads to a non-equilibrium trajectory and could change the underlying physical processes too drastically [294]. A recent work [295] used interaction-energy fingerprints obtained from SMD to predict k off values for 37 HIV protease inhibitors (R 2 of 0.75), showing that interactions with residues Asp25, Ile47, and Ile50, located in the active site or in the flap region, are important to modulate k off values. SMD was also used in combination with RAMD to study dissociation from B-RAF kinase [296].…”

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

“…Huang et al [14] extracted ligand-receptor interaction energy fingerprints from the steered MD trajectories of 37 HIV-1 protease inhibitors, which were further used for estimating the ligand dissociation rate constants by partial least squares (PLS) regression successfully. By employing position-restrained molecular dynamics simulations, Zhang et al [15] decomposed the protein-ligand interaction fingerprints alone the ligand-unbinding pathway and constructed PLS models to predict k off value of 20 p38 mitogen-activated protein kinase (p38 MAPK) Type II inhibitors.…”