SMD-based Interaction-energy Fingerprints Can Predict Accurately the

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Computational Chemistry

SMD-based Interaction-energy Fingerprints Can Predict Accurately the Dissociation Rate Constants of HIV-1 Protease Inhibitors Shuheng Huang, Duo Zhang, Hu Mei, MuliadiYeremia Kevin, Sujun Qu, Xianchao Pan, and Laichun Lu J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00567 • Publication Date (Web): 13 Nov 2018 Downloaded from http://pubs.acs.org on November 14, 2018

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

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Shuheng Huang†,‡,§, Duo Zhang‡,§, Hu Mei*,†,‡, MuliadiYeremia Kevin‡, Sujun Qu‡, Xianchao Pan‡,¶, Laichun Lu*,†,‡

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† Key

Laboratory of Biorheological Science and Technology (Ministry of Education),

Chongqing University, Chongqing 400044, China

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‡ College

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University, Luzhou, Sichuan, 646000, China

of Bioengineering, Chongqing University, Chongqing 400044, China

Department of Medicinal Chemistry, College of Pharmacy, Southwest Medical

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ABSTRACT

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Recent researches have increasingly suggested that the crucial factors affecting drug

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potencies are related not only to the thermodynamic properties but also to the kinetic

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properties. Therefore, in silico prediction of ligand-binding kinetic properties,

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especially the dissociation rate constant (koff), has aroused more and more attentions.

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However, there are still a lot of challenges that need to be addressed. In this paper,

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steered

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decomposition was employed to predict the dissociation rate constants of 37 HIV-1

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protease inhibitors (HIV-1 PIs). For the first time, a predictive model of the

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dissociation rate constant was established by using the interaction-energy fingerprints

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sampled along the ligand dissociation pathway. Based on the key fingerprints

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extracted, it can be inferred that the dissociation rates of 37 HIV-1 PIs are basically

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determined in the first half of the dissociation processes, and that the H-bond

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interactions with active-site Asp25 and van der Waals interactions with flap-region

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Ile47 and Ile50 have important influences on the dissociation processes. In general,

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the strategy established in this paper can provide an efficient way for the prediction of

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dissociation rate constants as well as the unbinding mechanism researches.

molecular

dynamics

(SMD)

combined

with

residue-based

energy

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Keywords: Steered molecular dynamics, HIV-1 protease, Inhibitors, Dissociation rate

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constant, Ligand-receptor interaction fingerprints

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1. INTRODUCTION

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Human immunodeficiency virus type 1 protease (HIV-1 PR) is one of the most crucial

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enzymes in the life cycle of HIV-1, which produces mature enzymes and structural

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proteins during virus replication process by cleaving polypeptide precursors.1 HIV-1

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PR is a C2-symmetric, homodimeric protein composed of two chemically matching

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monomers of 99 residues. Each monomer contains an α-helix near the C-terminus and

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two antiparallel β-sheets.2 The C2-symmetric active site is composed of two tripeptide

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sequences, i.e. Asp25/Asp25*-Thr26/Thr26*-Gly27/Gly27*, and is gated by two

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extended β hairpin loops (residues 46-56), known as flap regions (Fig.1).3, 4 The main

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function of the flap regions is to control the entrance of substrates and other small

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molecules into the active site. The flap regions are generally believed to be partially

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closed (semi-open form) in unbinding state (Fig 1a). However, when the substrates or

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other small molecules enter into the active site, the flap regions will be closed to

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prevent molecules out of the active site (Fig 1b) until protease hydrolysis prompts the

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flap regions opening again.5, 6

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There is a growing interest in the discovery of HIV-1 protease inhibitors (HIV-1

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PIs) in the last three decades. HIV-1 PIs can competitively occupy the binding site of

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HIV-1 PR substrates, and prevent the synthesis of functional proteins required by

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virus assemble.7 US Food and Drug Administration has approved 9 HIV-1 PIs as

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clinical medicines for AIDS treatments, namely Saquinavir (Saq), Amprenavir (Amp),

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Atazanavir (Ataz), Darunavir (Dar), Indinavir (Ind), Lopinavir (Lop), Nelfinavir

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(Nelf), Ritonavir (Rit), and Tipranavir (Tip).8

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For a long time, the affinity has been regarded as a key indicator of drug potency,

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which is often measured by thermodynamic properties, e.g., equilibrium dissociation

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constant (KD). However, Recent researches have increasingly showed that the kinetic

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properties, i.e. dissociation rate constant or drug-target residence time, are more

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important for the potencies of HIV-1 PIs.9 Maschera et al.10 investigated the

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association (kon) and dissociation rate constants (koff) of Saq for wild and mutant

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HIV-1 PRs. The results showed that the kon of Saq were similar between the wild and

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mutant HIV-1 PRs, while a significant difference in koff was observed. That is to say,

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the reduced affinities for the mutant HIV-1 PRs are mainly due to the relatively fast

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dissociation rate constants. By using biosensor technology, Shuman et al.11 measured

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the kinetic properties of five clinical inhibitors (Amp, Ind, Nelf, Rit, and Saq) by

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using drug-resistant variants of HIV-1 PRs. The results showed that single-amino-acid

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substitution in the drug-resistant variants can increase the dissociation rates of the

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HIV-1 PIs. In general, the researches revealed that the dissociation rate constant is the

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most crucial factor determining the potencies of HIV-1 PIs.

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Up to date, the kinetic properties are mainly determined by fluorescence

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measurement,12 capillary electrophoresis,13 affinity chromatography,14 high pressure

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spectroscopy,15 and surface plasmon resonance methods,16,

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methods are still confronted with great difficulties (i.e., time-consumed, high cost, and

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large measurement errors), which limits drug R&D in a large degree. With the

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development of molecular simulation techniques, more and more in silico approaches

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have been used to predict qualitatively or quantitatively the kinetic properties of small

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molecules. Schaal et al.18 utilized comparative molecular field analysis (CoMFA) to

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predict kinetic rate constants of 34 HIV-1 PIs. After molecular alignments and partial

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least squares (PLS) modeling, the cross-validated determination coefficients (R2) were

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etc.. However, these

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0.72 and 0.48 for the optimal koff and kon models, respectively. By using grid-based

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VolSurf method, Qu et al.19 established PLS models successfully for predicting kon,

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koff, and KD of 37 cyclic and linear HIV-1 PIs. In particular, the predictive power of

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the koff model was quite satisfied with the R2, cross-validated R2, and predictive R2 of

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0.70, 0.70, and 0.77, respectively.

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Many attempts have proven to be successful in the estimation of dissociation rates

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or drug residence time by using the methods based on molecular dynamics

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simulations.20 Li et al.21 investigated the dissociation processes of three HIV-1 PIs,

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i.e., AHA001, XK263 and ABT538, by steered molecular dynamics (SMD) and

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umbrella sampling techniques. The results showed that the strength of intermolecular

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H-bonds plays a crucial role in the dissociation processes, and that the predicted

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kinetic parameters were closely consistent with the experimental results. Buch et al.22

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constructed a Markov state model (MSM) to predict the kinetic properties of

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benzamidine, and the predicted kon and koff were in good agreement with the

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experimental values. By using metadynamics method, Sun et al.23 predicted drug

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residence time by using the optimized structures derived from holo-state proteins. In

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comparison with the MD methods based on Poisson processes, this strategy gave a

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comparable or even better prediction accuracies.

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Kokh et al.24 applied τ-random acceleration molecular dynamics (τRAMD) for

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predicting the residence time of 70 inhibitors of human N-terminal domain of heat

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shock protein 90α. A strong correlation (R=0.81) was observed between the predicted

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and measured residence time. Mollica et al.25 performed multiple-replica scaled-MD

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simulations to predict the ligand residence time of Heat Shock Protein 90,

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Glucose-Regulated Protein, and Adenosine A2A receptor, respectively. The

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correlation coefficients between the predicted and observed residence time were 0.95,

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0.85, and 0.95, respectively. This protocol was further applied to predict the residence

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time of 7 glucokinase activators, and achieved satisfied results.26

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In spite of these exploratory studies, accurate prediction of the unbinding kinetics

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remains a challenging task. As a simple and efficient biased molecular simulation

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method, steered molecular dynamics has been proved to be a powerful tool in the drug

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design and molecular simulation domains,27 e.g. ligand unbinding mechanism,28

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membrane translocation29 and prediction of drug binding affinity.30, 31 In this study,

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the ligand-receptor interaction fingerprints extracted from SMD trajectories were, for

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the first time, employed to predict the dissociation rate constants of 37 HIV-1 PIs with

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excellent prediction results. The framework presented in this paper can provide an

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efficient way for quantitative prediction of the dissociation rate constants as well as

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the dissociation mechanism researches.

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2. METHODS

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2.1. Molecular docking

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A dataset of 37 HIV-1 PIs with different structural skeletons was derived from

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Shumanet et al.32 (Table 1). There are five categories of the 37 HIV-1 PIs: non-B268

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analogues, cyclic sulfamide analogues, P1/P1’ analogues of B268, P2/P2’ and central

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hydroxy analogues of B268, and 7 clinical medicines. It can be seen that the kon vary

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by more than 4 orders of magnitude and the koff by 3 orders of magnitude.

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Based on the crystal structure of HIV-1 PR with a co-crystallized ligand

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AHA001 (PDB: 1AJX, resolution 2.0 Å), Surflex-dock was firstly used to derive the

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conformations of HIV-1 PR-inhibitor complexes. Before molecular docking, all the

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37 HIV-1 PIs were charged by MMFF94 method and then optimized by Tripos force

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field with conjugate gradient minimizer (Sybyl 8.1). The maximum iteration steps and

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energy gradient were set to 5000 times and 0.05 kcal/mol·Å, respectively. In the

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structural preparation of HIV-1 PR, Asp25/Asp25* were firstly protonated according

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to previous experimental and theoretical researches.33,

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minimization of HIV-1 PR was performed by using AMBER FF99 force field with

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conjugate gradient algorithm. Surflex-dock employs an idealized ensemble of CH4,

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NH, and CO probes, namely protomol, as a guide to generate putative poses of

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ligands. In this paper, the protomol was generated based on the co-crystalized ligand

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AHA001 with a threshold of 0.5 and bloat of 0.0 Å.35,

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conformations were set to 5 and ring flexibility was considered in the docking

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processes.

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2.2. Molecular dynamics

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Then, a quick global

The additional starting

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Molecular dynamics is one of the most widely used methods in computer-aided

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drug design and computational biology.37 MD can simulate the movement process of

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molecular system at atomic level, and can in principle characterize the relevant

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thermodynamics and kinetics properties.38, 39

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Based on the optimal docking conformation for each HIV-1 PR complex, MD

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simulation was performed to obtain an equilibrium conformation in solvent

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environment. Each complex was firstly solvated by TIP3P water box with the

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dimensions of 90Å×90Å×90Å , and added counter ion Cl- to neutralize the whole

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system. Before MD simulations, 2500 steps of steepest descent followed by 2500

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steps of conjugate gradient energy minimizations were performed. Herein, MD

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simulations with periodic boundary conditions40, 41 were performed by NAMD 2.10

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with CHARMM22 force field. At first, each system was gradually heated from 0 to

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310 K within 5000 ps in the NVT ensemble, where the HIV-1 PR complex was

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harmonically constrained by a force of 50 kcal/mol·Å2. Then, a 10-ns MD simulation

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was performed in the NPT ensemble (310 K, 1 atm) without any constraint. The

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integration time step was set to 2 fs, and the cutoff of van der Waals and electrostatic

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interactions was set to 10 Å. The Particle Mesh Ewald (PME) method was used to

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calculate the long-range electrostatic interactions,42 and the SHAKE algorithm43 was

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used to constrain the covalent bonds with H-atoms.

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2.3. Steered molecular dynamics

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Steered molecular dynamics, firstly proposed by Schulten et al.44, 45 in 1998, is a

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well-established method for non-equilibrium MD simulations. In SMD simulations, a

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hypothetical force (or harmonic potential) was exerted to a given ligand, which can

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accelerate dissociation process along a predefined reaction coordinate in constant

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velocity or constant force modes.

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Before SMD simulation, a series of computational experiments were performed on

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a reference molecule A016 for exploring the optimal force constant k by using eight

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gradual values (i.e. 20, 25, 30, 35, 40, 50, 60 and 70 kcal/mol·Å2). Each force rate

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was simulated more than twice to calculate the unbinding speed. Based on the results

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of SMD simulations of a reference molecule A016, a 6-ns SMD simulation with a

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constant velocity of 2.5 Å/ns was performed. At this moderate pulling velocity, the

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ligands and the nearby residues could be relaxed efficiently while reducing the

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computation time significantly. Meanwhile, all the HIV-1 PIs can be completely

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pulled out of the binding site of HIV-1 PR after 6-ns SMD simulations.

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In this paper, a 6-ns SMD simulation was performed for each HIV-1 PR complex,

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of which the starting conformation was derived from the lowest-energy conformation

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in the last 2-ns MD trajectory. Previous studies proved that the HIV-1 PIs tended to

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laterally slide out from the binding site in the dissociation process.46,

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lateral direction depicted by the vector from the center of mass (COM) of Arg8 to the

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COM of Arg8* was defined as pulling direction (Fig.2). Herein, the COM of the

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ligand was harmonically constrained to move at a constant velocity (2.5 Å/ns) in the

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Thus, the

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specified direction. To avoid translation and rotation movements of the receptor, the

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Ca atoms of Thr74/Thr74* and Leu90/Leu90* were restrained by a harmonic

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potential with a constant force of 5 kcal/mol·Å2. The SMD trajectories were sampled

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at a time interval of 2 ps.

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2.4. Extraction of interaction-energy fingerprints

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Fig.3 shows the flowchart of interaction-energy fingerprint extraction and PLS

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modeling. In this paper, the interaction-energy fingerprints were calculated between

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the ligand and residues within 5 Å distance away from the surface of dissociation

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channel. A total of 48 residues were involved in the extraction of interaction-energy

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fingerprints (Table S1). The fingerprints were then sampled from the 6-ns SMD

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trajectory at a time interval of 300 ps, which resulted in a total of 2880 (20×48×3)

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interaction fingerprints, i.e., 960 electrostatic interaction energies, 960 van der Waals

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interaction energies, and 960 total interaction energies for each HIV-1 PR complex.

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2.5 Partial least-squares modeling

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Partial least-squares (PLS) regression48,

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is a statistical method that combines

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principal component analysis (PCA) and multiple regression (MLR). This method is

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especially suited for treating the variables with strongly collinear, noisy, and

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numerous X variables. In PLS modeling, both X and Y variables are bilinearly

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decomposed and projected into new principal component spaces (Eq.(1-2)).

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X=TPT+E

Eq.(1)

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Y=UQT+F

Eq.(2)

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Where, T and U are scores of the X and Y matrices, respectively; P is loading

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matrix of X and Q is weight matrix of Y; E and F are residual matrices of X and Y.

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The aim of PLS method is to find an inner linear relationship between T and U

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matrices (Eq.(3)):

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U=CT+G

Eq.(3)

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where C is the regression coefficient matrix and G is the residual matrix. For details,

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please refers to the references.48, 49

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Before PLS modeling, the 37 HIV-1 PR complexes were randomly divided into 29

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training samples and 8 test samples. Herein, 2880 interaction fingerprints were used

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as independent variables and the -log(koff) was used as a target variable. Then, forward

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selection (FS) was used for variable selection, of which the entry probability was set

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to 0.05. Based on the variables screened from FS, PLS modeling was performed. The

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determination coefficients (R2), three-fold cross-validated R2 (Q2), and R2 for the test

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set (R2 pred) were used for model evaluations.

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3. RESULTS AND DISCUSSION

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3.1. Molecular docking and MD simulations

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To validate the protocol of Surflex-dock, the co-crystallized AHA001 was

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re-docked into the binding site of HIV-1 PR (PDB ID: 1AJX). The total score of the

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optimal docking conformation of AHA001 was 13.57, and the root mean squared

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deviation (RMSD) of the heavy atoms was 0.77 Å (Fig.4a). As shown in Fig.4b, the

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optimal docking conformation of AHA001 forms strong H-bond interactions with

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Asp25/Asp25*, Gly27/Gly27*, and Ile50/Ile50*, which are consistent with the

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experimental observations. Thus, it can be concluded that Surflex-dock can reproduce

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the native conformation of AHA001 very well. Besides, no significant correlation was

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observed between the docking scores (Table S2) and -log(KD) of the 37 HIV-1 PIs.

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The reasons may be that the empirical scoring function of Surflex-dock cannot

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estimate accurately the binding free energies of the 37 HIV-1 PIs, or the Surflex-dock

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is unable to take fully consideration of the ligand/receptor induced fit effects.

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Fig.5 shows the total energy and RMSD evolutions in the MD simulations of 5

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representative HIV-1 PR complexes. The results indicate that all the 5 complexes

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reached equilibrium states after 4 ns simulations. The detailed MD results of 37

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HIV-1 PR complexes are shown in Fig.S1-S3. For each complex, the lowest-energy

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conformation in the last 2-ns was used for the following SMD simulations.

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3.2. Steered molecular dynamics

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In SMD simulations, small spring force constant k can reduce simulation errors and

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enhance simulation accuracy.44,

50

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optimized by using a reference molecule A016. From the plot of distance vs. time

243

(Fig.6), it can be seen that A016 tends to move at a constant speed of 2.5 Å/ns when

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SMDk increased to 35 kcal/mol·Å2. When applying this optimal SMDk to the rest 36

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HIV-1 PR complexes, similar results were obtained.

Herein, the force constant k (SMDk) was firstly

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Fig.7 shows the SMD results of Saq and Ind. As shown in Fig.7a, both of the

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inhibitors moved approximately at the constant velocity of 2.5 Å/ns by using the

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SMDk of 35 kcal/mol·Å2. Fig.7b shows the monitored pulling forces exerted on Saq

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and Ind in the SMD processes. It can be observed that Saq has a force peak of about

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650 pN at 1500 ps, which is much larger than that of Ind (180 pN at 2500 ps). In

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comparison with Ind, Saq tends to overcome relatively higher energy barriers during

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the dissociation process, which is in consistent with the longer residence time of Saq

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(Table 1).

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3.3. Interaction-energy fingerprint extraction and PLS modeling

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Based on the SMD trajectories, a total of 2880 fingerprint descriptors were

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extracted for each HIV-1 PR complex, which was comprised of 960 electrostatic

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energies, 960 van der Waals energies, and 960 total energies. By using all of the 2880

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fingerprint descriptors, a PLS model was obtained with the R2, Q2, and R2 pred were

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0.789, 0.76, and 0.357, respectively. It is obvious that the external predictive power is

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very poor.

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To eliminate the irrelevant variables and enhance predictive power, PLS combined

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with forward selection was performed. A total of 11 interaction-energy fingerprints

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were obtained (Table 2), which were mainly related to the active-site residues (Asp25,

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Asp30) and flap residues (Ile47, Gly49, Ile50, Phe54). From Table 2, it can be

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observed that the predictive performances are increased significantly with the

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increased numbers of variables. In consideration of the predictive power and model

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explainability, Model 5 with 5 variables was chosen as the optimal PLS model, of

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which the R2, Q2 and R2 pred were 0.749, 0.742, and 0.834, respectively. It is should

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be noted that the predictive power of Model 5 is quite satisfactory in comparison with

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the available models.18, 19

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The variables included in the optimal model are T1500-Ile47*, E900-Ile50,

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V3000-Asp25, T3000-Ile47 and V3000-Ile50*. Herein, T, E, and V represent the total,

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electrostatic, and van der Waals interaction energies, respectively, and the subscript

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indicates the SMD time (ps). As mentioned above, Asp25, Ile47, Ile47*, Ile50, and

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Ile50* were determined as the key residues effecting dissociation rates, of which

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hydrophilic Asp25 is located in the active site and hydrophobic Ile47/Ile47* and

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Ile50/Ile50* are located in the flap region. Thus, it can be inferred that the

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dissociation rate constants of the 37 HIV-1 PIs are mainly determined by the

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interactions with the active-site and flap-region residues in the first half (SMD time