Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by supervised molecular dynamics simulation

Farzin Sohraby; Hassan Aryapour

doi:10.1371/journal.pone.0257916

. 2021 Sep 27;16(9):e0257916. doi: 10.1371/journal.pone.0257916

Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by supervised molecular dynamics simulation

Farzin Sohraby ^1,^#, Hassan Aryapour ^1,^*,^#

Editor: Israel Silman²

PMCID: PMC8476009 PMID: 34570822

Abstract

Determining the unbinding pathways of potential small molecule compounds from their target proteins is of great significance for designing efficacious treatment solutions. One of these potential compounds is the approved HIV-1 protease inhibitor, Indinavir, which has a weak effect on the HTLV-1 protease. In this work, by employing the SuMD method, we reconstructed the unbinding pathways of Indinavir from HIV and HTLV-1 proteases to compare and understand the mechanism of the unbinding and to discover the reasons for the lack of inhibitory activity of Indinavir against the HTLV-1 protease. We achieved multiple unbinding events from both HIV and HTLV-1 proteases in which the RMSD values of Indinavir reached over 40 Å. Also, we found that the mobility and fluctuations of the flap region are higher in the HTLV-1 protease, making the drug less stable. We realized that critically positioned aromatic residues such as Trp98/Trp98′ and Phe67/Phe67′ in the HTLV-1 protease could make strong π-Stacking interactions with Indinavir in the unbinding pathway, which are unfavorable for the stability of Indinavir in the active site. The details found in this study can make a reasonable explanation for the lack of inhibitory activity of this drug against HTLV-1 protease. We believe the details discovered in this work can help design more effective and selective inhibitors for the HTLV-1 protease.

Introduction

In the last decades, retroviruses have always been a significant threat to human beings′ lives, and since then, countless people have lost their lives as a result. Retroviruses are the oldest family of viruses on planet earth. Recent bioinformatics advances have estimated that retroviruses have originated about 500 million years ago [1] and were inserted into the human genome about a million years ago [2]. Over these years, the genome of retroviruses, like most families of viruses, has been added to our genomes, and as a result, about 8 to 10% of the human genome has a viral origin which is the sign of ancient germ line infections. They are known as Endogenous Retroviruses (ERVs) [1,3], which may have probably evolved from transposable elements. However, RNA-enveloped Viruses such as Human Immunodeficiency Virus (HIV) or Human T-cell Leukemia Virus 1 (HTLV-1) have to be transmitted horizontally among hosts and belong to the subfamily of Exogenous Retroviruses (XRVs).

HIV has been one of the dangerous viruses on planet earth. Since its occurrence in the 1980s, this lethal virus has taken millions of lives. Since the start of the epidemic, 32 million people have died from AIDS-related illnesses [4]. This pandemic virus forced health organizations to speed up the research to find treatment options. Fortunately, the extensive research and development to eradicate HIV led to treatment solutions with good efficacy. In 1987, the FDA approved the first drug to treat HIV, called Zidovudine (AZT). AZT belongs to a class of drugs known as Nucleoside Reverse Transcriptase Inhibitors (NRTIs), which cause premature determination of the proviral DNA. Many more drugs and solutions were achieved afterward, and now HIV-infected individuals are almost able to live everyday life with a usual life expectancy.

The viral proteases are one of the critical targets for the treatment of virus infection. Over the last 20 years, HIV protease inhibitors have shown that inhibiting viral protease can be an excellent strategy to fight the virus. Nowadays, protease inhibitors are one of the main parts of the combination therapies of HIV treatments. Approved drugs such as Indinavir, Saquinavir, etc., have good efficacy for inhibiting the HIV protease and have been proven to have a significant impact on the condition of the patients [5,6]. The first Anti-Retroviral Therapy (ART) was based on NRTIs, which were not very effective and caused many complications, but the development and the emergence of protease inhibitors introduced a new ART milestone. This milestone gave hope to both the developers and also the patients that this infection may be conquerable. Combination therapies provide excellent efficacy and show that protease inhibitors are the main parts of successful treatments [7–12].

The HTLV-1 virus is associated with adult T-cell leukemia (ATL) and an inflammatory disease syndrome called HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) [13–15]. About 90 percent of infected individuals remain asymptomatic, but due to the virus′s permanent entanglement with the immune system, the infected person will suffer from immune system deficiencies throughout his/her life, leading to other problematic diseases [16–18]. HTLV-1 has infected about 20 million people worldwide [15], and treatment solutions are urgently needed. Anti-ATL vaccines have been developed recently and are in clinical trials, but they are designed to fight the cancerous cells, not the virus itself [19]. In contrast to HIV, which has first-line therapies and treatments are available for it, HTLV-1 does not have any treatments to this date. A solid strategy to fight this virus is the direct inhibition of its essential work machines, such as the protease enzyme, the same as the effective HIV treatments [20]. As mentioned before, protease inhibitors have proved their efficacy, and in the case of HTLV-1, protease inhibitors may be a potential breakthrough in HTLV-1 treatments [21].

In the last few years, all of the inhibitors of the HIV protease have been tested against the HTLV-1 protease. However, none of the inhibitors exhibited sufficient inhibition. Although the two enzymes are very similar in structure, the anti-HIV drug with an inhibitory constant (Ki) of 540 pM [22], Indinavir, could only achieve an inhibitory constant of 3.5 μM against the HTLV-1 protease [23,24]. The only consolation is that the cause of this can only be the difference in their structural behavior and the difference in the sequences of these two enzymes. The residues of the active site and the flap regions can especially dictate this behavior [25–28].

Computational methods such as Molecular Dynamics (MD) simulation enables researchers to thoroughly study the structural details of complex mechanisms of biomacromolecules [29]. Studying the unbinding pathway of small molecule compounds in complex with their target proteins is a great feature of the MD simulation method that has been made possible in the last decade. Many research groups developed great approaches over these years that empower us to understand the fine details of these mechanisms [30–44]. Unraveling the drug′s unbinding pathway from its target protein is an excellent task that can be very useful for understanding the mechanism of the unbinding process, as well as the essential residues involved [45]. The details achieved can be used to make more effective and more selective drugs. One of the appropriate approaches for this task is the Supervised MD (SuMD) simulations [41]. In this approach, the simulation of the protein-ligand complex is performed in replicas with fixed duration times, and at the end of each replica, a specific parameter such as the distance of the ligand from the binding site is checked, and the frame with the highest distance is extracted and extended as the next replica. This cycle will be continued until the ligand is entirely unbound from the target protein. This atomistic approach is entirely unbiased, and there are no artificial attractive or repulsive forces involved. Also, similar out-of-equilibrium approaches such as weighted ensemble milestoning (WEM) and forward flux sampling (FFS) have been developed so far that have been used for ligand unbinding processes [46,47]. Herein, by utilizing the SuMD approach under equilibration conditions, we tried to achieve the anti-HIV drug′s unbinding pathway, Indinavir, from both the HIV protease and the HTLV-1 protease as a comparison test to understand the details and the behavior of these critical protein targets. This strategy can be effective in finding the reasons for the lack of efficacy of the HIV protease inhibitors against the HTLV-1 protease.

Methods

The X-ray crystallography structures of the HTLV-1 (PDB ID: 3WSJ) [24] protease and the HIV protease (PDB ID: 1K6C) [48] in complex with Indinavir were obtained from the Protein Data Bank [49]. The structures were prepared by UCSF Chimera software [50]. All of the unnecessary molecules, such as water molecules, were deleted from the structures, and the protein-ligand complexes′ structures were ready for the next step, the MD simulations. There are two catalytic Asp residues in both proteases′ active sites, and one of them must be protonated during the simulation. The extra hydrogen atom was added to the Asp residue of chain A in both cases. All of the MD simulations were done by GROMACS 2018 package [51] and OPLS-AA force field [52]. The 3D structures of the co-crystallized Indinavir were parameterized using ACEPYPE [53] with the default setting for assigning the partial charges and atom types. For the construction of the simulation systems, first, the related protein-ligand complex was placed in the center of a triclinic box with a distance of 1 nm from all edges and then solvated with the TIP3P water model [54]. About 9400 and 7300 water molecules were added to the HTLV-1 and HIV protease systems, respectively. Then, sodium and chloride ions were added to produce a neutral physiological salt concentration of 150 mM. Each system was energy minimized, using the steepest descent algorithm, until the Fmax was smaller than 10 kJ.mol^-1.nm^-1. All of the covalent bonds were constrained using the Linear Constraint Solver (LINCS) algorithm [55] to maintain constant bond lengths. The long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method [56], and the cut-off radii for Coulomb and Van der Waals short-range interactions were set to 0.9 nm for the interaction of the protein-ligand complex. 100 ps of NVT and 300 of NPT were performed using the modified Berendsen (V-rescale) thermostat [57] and Parrinello–Rahman barostat [58], respectively, for the equilibrations and to keep the system in stable environmental conditions (310 K, 1 Bar) during the production runs. Finally, SuMD simulations [59] were carried out with a time step of 2 fs. The periodic boundary condition (PBC) was set at XYZ coordinates to ensure that the atoms had stayed inside the simulation box during the equilibration and production runs. The subsequent analyses were then performed using GROMACS utilities, VMD [60] and USCF Chimera, and also the plots were created using Daniel′s XL Toolbox (v 7.3.2) add-in [61]. The free energy landscapes were rendered using Matplotlib [62]. In addition, to estimate the interaction energies, we used the g_mmpbsa package [63]. The Free Energy Landscape (FEL) analysis was done by “gmx sham” module of GROMACS software.

The difference between the conventional MD (cMD) and the SuMD simulations is the fact that SuMD is an out-of-equilibrium simulation and, also in SuMD, the entire simulation is divided into a series of replicas, and a specific parameter is monitored throughout them as the guideline to choose the starting point of the next replica. The original idea was proposed by Giuseppe Deganutti et al. [41], which is an excellent methodology for achieving unbinding events. In the original method, “A series of short unbiased MD simulations are performed, and after each simulation, the distances (collected at regular time intervals) are fitted to a linear function. If the resulting slope is negative (showing progress toward the target), the next simulation step starts from the last set of coordinates and velocities produced; otherwise, the simulation is restarted by randomly assigning the atomic velocities”, whereas, in our series of replicas, the procedure is much simpler. We considered the distance between the Center Of Mass (COM) of all of the atoms of the drug and the COM of the entire atoms of the Asp32 (chain A) and Asp32′ (chain B) in the HTLV-1 MD system and the Asp25 (chain A) and the Asp25′ (chain B) in the HIV proteases MD system as the guideline for selecting the best frame to be the starting point of the next replica. The distance between the COM of Indinavir and the mentioned residues in each system was monitored in the entire simulation. The duration time of all of the replicas in this study was set to 500 ps. At the end of every 500 ps simulation, the frame with the highest distance was selected as the next 500 ps simulation starting point. This procedure was done automatically by an external python script. We also performed 50 ps of NVT and NPT equilibration runs after the frame selection before the 500 ps production runs. This methodology addressed the out-of-equilibration problem of SuMD simulation.

Results and discussion

In this study, by utilizing SuMD simulations, we tried to reveal the unbinding pathway of the anti-HIV drug, Indinavir, from the HIV and the HTLV-1 proteases to find the details of the unbinding mechanism and also to compare the results to understand the lack of inhibitory activity of this drug against the HTLV-1 protease.

The 3D structure of the HTLV-1 and the HIV proteases are very similar. When superimposed, the backbone RMSD value is as low as 0.95 Å (Fig 1A). Although the positions of the backbone atoms and the protein fold are roughly the same, the sequence identity falls to 28 percent [64]. The HTLV-1 protease has 125 residues, whereas the HIV protease has 99 residues. However, the conserved residues sequence in the active sites has an identity value of 45 percent [64]. The various regions of these two enzymes have high structural similarities since their function are the same. However, the details of the two enzymes’ active sites differ in some points to recognize and accommodate their specific substrates. As a result, none of the HIV protease inhibitors, except Indinavir, can inhibit this enzyme with relatively low concentrations. Kuhnert et al. determined and compared the structures of HIV and HTLV-1 proteases in complex with Indinavir and characterized the role of residues inside the active sites, and discovered significant deviations for the interaction networks of each moiety (Fig 1B) with the important sections of the binding pocket [24].

We achieved three unbinding events from each of the complexes in three series of replicas. The replicas were continued until the distance between the two centroids (COMs) reached roughly over 4 nm, and the ligand was in the unbound conformation. In this work, the unbinding events happened in 173, 183.5, and 338 ns in the HIV protease-Indinavir complex (Fig 2A and S4–S6 Files) and the HTLV-1 protease-Indinavir complex; the unbinding events happened in 243.5, 552.5, and 671 ns (Fig 2B and S1–S3 Files). Although Indinavir inhibits HIV protease much more selectively than the HTLV-1 protease, the overall time needed for the unbinding events of the HTLV-1 protease case was considerably more than that of the HIV protease case. However, many factors govern an inhibitor’s activity, and only comparing the overall duration of the unbinding events in three series of replicas is not a correct way of comparing these two cases. This may be due to the inherent limitations of the SuMD method. Because the time window is set to 500 ps, therefore more conformational sampling by ligand is limited.

Fig 2 — a, Indinavir-HIV protease complex and b, Indinavir-HTLV-1 protease complex. Values were calculated using the crystallographic binding pose as the reference.

There are no biasing forces involved in the simulations, and they are entirely unbiased. The main difference between this method and conventional MD simulations is the automatic supervision at choosing the most appropriate frame in a replica for extending the simulation and also the fact that SuMD is an out-of-equilibrium simulation. As explained in the methods section, the most appropriate frame in a 500 ps simulation was the frame with the highest distance between the ligand and the catalytic Asp residues (S1 Fig). The SuMD approach is speedy and a remarkable tool for reconstructing the unbinding pathways of small molecule drugs and unraveling their details which are of great significance in drug discovery and design.

The unbinding pathway of Indinavir from the HTLV-1 protease and the HIV protease was very similar. In total, three stable states were observed during the unbinding pathways; (i) the Native state (N), (ii) the Intermediate states (I₁, I₂) and, (iii) the Solvated state (S) (Fig 3A–3F). The native state is where the ligand is in the crystallographic conformation. Indinavir can stay in this state for a long time in the unbinding pathway. In this state, the contact surfaces between the ligand and the protein are at their maximum, and the RMSD values are at their minimum. The next state in the unbinding pathway is the intermediate state I₁ and I_2, where the ligand has a tight interaction with the flap regions’ residues. The two flaps in both proteases have high mobility, and their strong interaction with the ligand can make it get out of the crystallographic conformation in which it has fewer contact surfaces with the protein and higher RMSD values. This is the I₁ state. These intermediate states of Indinavir in the unbinding pathway are stable, and the ligand can stay in them for long times. After the flap region was opened, the ligand can get attached to one of the flaps and get further from the active site, and this is the I₂ state.

Fig 3 — **a, b, c**, from the HIV protease and, **d, e, f,** from the HTLV-1 protease. The stable states of Indinavir during the unbinding pathway are indicated by capital letters; Native (N), Intermediate (I₁, I₂), and Solvated (S).

The total interaction energies of Indinavir in the unbinding pathways of both proteases, calculated by the MM/PBSA approach (Fig 4A and 4B), showed roughly the same amounts. This means that although the residues of these two enzymes’ active sites only have 45 percent identity, the interaction profiles are almost the same [23]. The values gradually increased and eventually reached zero when the ligand left the binding pocket and got solvated. Moreover, this analysis showed that the dominant interactions are the short-range VdW interactions between the aromatic and aliphatic residues and the ligand (S2 Fig). This finding indicates that the binding pockets of these two enzymes are somewhat hydrophobic. Except for the two Asp residues in the active site’s deepest part, almost none of the residues can make direct hydrogen bonds with the ligand using their side chains. The hydrogen bonds are mainly formed with the residues’ backbone by incorporating water molecules [24]. There is also a conserved water molecule in the Indinavir-HTLV-1 protease complex known as the "flap water," which mediates interactions between the ligand and Ala59 on the tip of the flaps (see Ref 24 for more information) [24].

As mentioned above, the interactions between Indinavir and the residues of both enzymes’ binding pockets have almost the same amount and nature. However, each enzyme’s active site residues’ contribution to the total interaction energies differs in some points (Figs 5A, 5B and S3). In the Indinavir-HIV protease complex, Ile50, Gly49, Gly48 from the flap region, and Asp25 and Thr80 had the most contribution in the interaction energies (Fig 1C). In the Indinavir-HTLV-1 protease complex, Leu57, Gly58, and Ala59 from the flap region and the Trp98 and Asp32 in the active site had the most contribution in the interaction energies (Fig 1D). These emphasize the vital role of the residues of the flap region. It is also clear that in the HIV protease, almost all of the critical residues are aliphatic. However, in the HTLV-1, aromatic residues such as Trp98 and Phe67 conceal the role of aliphatic residues in the unbinding pathway. The existence of these aromatic residues in the active site of the HTLV-1 protease is one of the main reasons for the lack of proper inhibitory activity of Indinavir against this enzyme, which will be discussed later [24].

Fig 5 — a, Indinavir-HIV protease complex, 1^st replica. b, Indinavir-HTLV-1 protease complex, 2^nd replica. A and B stands for "Chain A" and "Chain B", respectively. This data is the average interaction energy of each residue during the entire SuMD simulation.

In this study, we also found that one of the most important structural differences between the two proteases is the level of mobility of the flap region. In both enzymes, this region has high mobility, but in the HTLV-1 protease, the flexibility and the fluctuations of flaps are slightly more than that of the HIV protease due to the nature of residues and the larger length of the flaps (Fig 6A and 6B) [65–68]. The higher mobility of this region is unfavorable for Indinavir to stay stable in the active site. In the presence of a ligand inside of aspartic proteases, the fluctuation of the flap region is significantly decreased, which makes the ligand very stable in the active site [69,70]. However, in the Indinavir-HTLV-1 protease complex, the higher mobility and fluctuation of this region might play a big part in the lack of sufficient inhibitory activity of Indinavir. The handedness feature of the flaps was also visible in the RMSF values in which one of the flaps has more fluctuation than the other [67,71].

Fig 6 — a, RMSF values of HIV protease and, b, RMSF values of HTLV-1 protease. c, RMSD values of the backbone atoms of HIV protease and, d, RMSD values of the backbone atoms of HTLV-1 protease.

Furthermore, in almost all three replicas of the Indinavir-HTLV-1 protease complex, the RMSD values of the flap region are noticeably higher than that of the HIV protease (Fig 6C and 6D). Ala59 in the HTLV-1 protease and Ile50 in HIV protease is present on the tip of the flap region. The Ile50 on the tip of the flap region in HIV protease may affect keeping the flaps in the closed state due to its strong interaction with Indinavir (Figs 6A and 7A). On the other hand, in HTLV-1 protease, Ala59 is present on the tip of the flaps, making weaker interactions with Indinavir and may explain another reason for the higher mobility of the flap region in HTLV-1 protease (Fig 6B) [72–74].

Fig 7 — a, The “out-of-equilibrium” free energy landscape and the 3D structures of HTLV-1 protease with different flap states. b, The “out-of-equilibrium” free energy landscape and the 3D structures of HIV protease with different flap states.

The flap region of the two proteases can acquire three states: closed, semi-open, and open [67,75]. In the 6 SuMD simulations performed in this study, three replicas for each complex, all of these states were observed in the unbinding pathways of Indinavir (Fig 7A and 7B). The natural function of proteases is to recognize the polypeptide chain, trap and place it in the active site where the catalytic residues cut the peptide bond at a specific position. This function of the flaps is highly crucial for the whole function of the enzyme [68,76]. The weak interaction between the ligand and the flap regions’ residues coupled with the high mobility of this region causes the ligand to unbind and leave the enzymes. In almost all of the replicas, Indinavir left the active site from the flap region.

Indinavir interacts with these two enzymes at two separate states, the native and the intermediate states. In the native state, Asp32/Asp32′ in the HTLV-1 protease and Asp25/Asp25′ in HIV protease have the role of interacting with the ligand and make it stable. Although many other residues interact with the ligand in the native state through VdW and electrostatic interactions, the two Asp residues in the deep parts of both enzymes keep Indinavir anchored to the native state. One of the two catalytic Asp residues is always protonated, which is essential for the enzyme function [77–79]. This protonation enables one of the Asp residues to make a strong hydrogen bond with the central hydroxyl group of Indinavir. The first step of the unbinding pathway is the hydrogen bond breakage with the catalytic Asp residues in all replicas. The rotation of the hydroxyl group of Indinavir is the main cause of this breakage (Fig 8A and 8B). The rotation around C₁₀-C₁₁ bond and change of its dihedral angle at the start of the unbinding process (Fig 8D), and simultaneously the increasing distance between the OD₂ atom of Asp32 and the H₂₁ atom of Indinavir (Fig 8C) prove the breakage of this hydrogen bond and are the first events of the unbinding pathway (more examples in S4 Fig).

Fig 8 — a, Indinavir in the native state where a hydrogen bond with the Asp32 in the HTLV-1 protease keeps it stable in this state. b, the breakage of the hydrogen bond, which triggers the unbinding pathway. Both figures are frames 1153 and 1154, showing that this event needs very little time to happen. c, The distance between the OD₂ atom of Asp32 and the H₂₁ atom of Indinavir in the first 50 ns of the unbinding pathway of Indinavir-HTLV-1 complex in the 1^st replica. d, The dihedral angles of the rotatable bond responsible for the rotation of the hydroxyl group of Indinavir in the first 50 ns of the unbinding pathway of Indinavir-HTLV-1 complex in the 1^st replica.

After the first step, the unbinding pathway of Indinavir varies in some points between the HIV and HTLV-1 cases. After breaking the above-mentioned hydrogen bond, Indinavir started to get further from the active site’s deep parts and interact with higher positioned residues and the flap region residues. One of the active site’s critical points is the Trp98/Trp98′ residue in HTLV-1 protease, which Val82/Val82′ occupies in the HIV protease at the same position. The Trp residues in the HTLV-1 protease are one of the main contributors to the interaction energies. Phe67/Phe67′ was also an aromatic residue in the active site of HTLV-1 protease, which interacts with Indinavir in the native and intermediate states. These aromatic residues’ interactions with the ligand make it easier for the ligand to get out of the native state (Fig 9C–9E). They mainly interact with the benzyl and the pyridyl moieties of Indinavir, but interactions with the indanol and tert-butyl- moieties were also spotted in the unbinding pathway (Figs 1B, 9A and 9B). It was observed that they could acquire different orientations and make edge-to-face and face-to-face π-stacking and π-alkyl interactions. After the intermediate states, the ligand in both cases interacted solely with the flap region’s residues for a very short time and then left the enzymes ultimately and got solvated in the simulation box.

Fig 9 — a, indanol, pyridyl, and, b, tert-butyl and benzyl moieties of Indinavir interacting with the Indinavir molecule in different states. c, The first replica of the unbinding pathway where only Trp98 from chain A showed elevated RMSD values (nm) in comparison to Indinavir RMSD values (Å). d, The second replica where the two Phe67 from both chains presented their role in comparing the Indinavir RMSD values (Å) and the residues’ RMSD values (nm). e, The third replica where Phe67 from both chains and Trp98 from chain A exhibited their role in the unbinding pathway.

In 2003, Sa-Filho et al. provided very interesting mutagenesis data about the mutant forms of HIV protease in which V82F was one of the main contributors to drug resistance during treatment by Indinavir [80]. This mutation made Indinavir and other protease inhibitors act considerably less selectively. An even larger aromatic residue like Trp in this position in HTLV-1 protease can make a massive difference in the activity of the drug. This incredible experimental data guided us to explore the role of the aromatic residues inside the active site of HTLV-1 protease. The aromatic residues in the active site, Trp98, and Phe67 in both chains of this enzyme can make strong interactions with Indinavir in the intermediate state. These interactions are entropically unfavorable for the ligand since they make it easier for Indinavir to get out of the native binding mode and interact with higher positioned residues. By comparing the RMSD values of the ligand and the RMSD values of these essential residues of the HTLV-1 protease binding site during the unbinding pathway (Fig 9C–9E), the role of these aromatic residues, Phe67 and Trp98 were evident. In all three replicas of the Indinavir-HTLV-1 protease complex, the elevation of the RMSD values of these aromatic residues directly correlates to the elevated values of ligand RMSD. The transition of the ligand from the native state to the intermediate states directly corresponds to the conformational changes of these aromatic residues.

The fluctuation of every atom of the Indinavir molecule during the unbinding pathways in both enzymes (Fig 10A and 10B) shows that the fluctuation of the Indinavir atoms in the active site of HTLV-1 protease is different and slightly higher than that Indinavir in the active site of the HIV protease. Consequently, the motions of Indinavir moieties in the active site of HTLV-1 protease are greater, and thus its interactions with the residues are less stable. This extra freedom in the active site of HTLV-1 protease makes it easier and more probable for Indinavir to interact with unfavorable regions of the active site, such as the flap region and the aromatic residues, discussed above, which facilitate the unbinding event. This data may explain another reason for the lack of inhibitory activity of this approved drug against the HTLV-1 protease.

As described in previous sections, the mobility and fluctuation of the flap region in the HTLV-1 protease is higher than HIV protease (Fig 6A and 6B). The flap region’s residues in HIV protease have primarily organized secondary structures, two antiparallel β-strands (Fig 7B), whereas this region in the HTLV-1 protease is a mixture of short β-strands with random coils (Fig 7A). We believe that the higher mobility of the flap region coupled with the unfavorable interactions with aromatic residues in the active site of HTLV-1 protease can be the main reason for the significantly reduced inhibitory activity of Indinavir. A more effective inhibitor of HTLV-1 protease must make more interactions with the residues in the deep parts of the active site and have more stable interactions to reduce movements inside the active site. It must make more direct or water-mediated hydrogen bonds with the more inner positioned residues to avoid any interaction with the flap region since this region’s high fluctuation makes it easy for the ligand to unbind. The aromatic residues are also a big part of the active site. For an inhibitor to stay in the active site, it must reduce the fluctuations of the aromatic residues and the fluctuations of the flap region residues. The results found in this work represent the dynamic details of the unbinding pathways of Indinavir from HTLV-1 and HIV proteases, and they are supporting the static results achieved by other research groups such as Kuhnert et al. [24]. We believe that all of the details achieved so far over the years can explain the lack of inhibitory activity of Indinavir or even any other approved protease inhibitor against HTLV-1 protease.

Conclusion

Understanding the details of the unbinding pathway of potential small molecule inhibitors from their target proteins is of significant importance. One of the most promising tools for accomplishing this task is utilizing the SuMD simulation method. It is capable of reconstructing the binding and unbinding pathways of small molecule compounds. In this work, by taking advantage of this method, we reconstructed the unbinding pathway of Indinavir from the HTLV-1 protease and the HIV protease in order to understand the details of the unbinding pathways from both enzymes and also to compare the two unbinding events to find the reasons for the lack of inhibitory activity of Indinavir against the HTLV-1 protease. We achieved multiple unbinding pathways from both complexes and found that although the structure and the interaction profiles are broadly similar, superficial differences in the details cause massive effects on the inhibitor’s performance. It was discovered that in the unbinding pathway of Indinavir form HTLV-1 protease, the fluctuation of the flap region is higher than that of HIV protease. We also found that aromatic residues in the active site of HTLV-1 protease, Phe67/Phe67′, and Trp98/Trp98′, are among the essential residues in the unbinding pathways and had the most contribution to the interaction energies. However, these interactions are unfavorable and cause the ligand to get out of the native binding mode. The unfavorable interactions coupled with the higher fluctuations of the flap region were the reasons for the poor inhibitory activity of Indinavir against HTLV-1 protease. We believe that the details found in this study can be an excellent assist in designing more effective compounds for inhibiting the HTLV-1 protease as a treatment solution for fighting the HTLV-1 virus.

Supporting information

S1 Fig. The distance between the COM of Indinavir and the COM of the Asp residues in the active site of HIV and HTLV-1 protease during the unbinding pathway.

a, b, c, The HIV protease, d, e, f, The HTLV-1 protease.

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Click here for additional data file.^{(2.2MB, tif)}

S2 Fig. The VdW, electrostatic, and total interaction energies between Indinavir and the two proteases during the unbinding pathways.

a, b, c, The Indinavir-HIV complex and, d, e, f, The Indinavir- HTLV-1 protease complex.

(TIF)

Click here for additional data file.^{(5MB, tif)}

S3 Fig. The contribution of the active site residues to the total interaction energies in the unbinding pathways.

a, b, 1^st_, and 3^rd replica of the Indinavir-HIV protease case. c, b, 1^st_, and 2^nd replica of the Indinavir-HTLV-1 protease case.

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Click here for additional data file.^{(3.6MB, tif)}

S4 Fig. The first step of the unbinding pathway of Indinavir is illustrated by (i) the distance of the hydrogen atom of the central hydroxyl group of Indinavir and the OD₂ atom of Asp32 in HTLV-1 protease and the Asp25 in HIV protease, and (ii) the dihedral angle of the C₁₀-C₁₁ bond in the Indinavir molecules.

a, The distance between the OD₂ atom of Asp25 and the H₂₁ atom of Indinavir in the first 50 ns of the unbinding pathway of Indinavir-HIV complex in the 2nd replica. b, The dihedral angles of the rotatable bond responsible for the rotation of the hydroxyl group of Indinavir in the first 50 ns of the unbinding pathway of the Indinavir-HIV complex in the 2nd replica. c, The distance between the OD₂ atom of Asp32 and the H₂₁ atom of Indinavir in the first 200 ns of the unbinding pathway of Indinavir-HTLV-1 complex in the 3rd replica. d, The dihedral angles of the rotatable bond responsible for the rotation of the hydroxyl group of Indinavir in the first 200 ns of the unbinding pathway of Indinavir-HTLV-1 complex in the 3rd replica.

(TIF)

Click here for additional data file.^{(4.1MB, tif)}

S1 File. HIV-rep1.mp4, the 1^st unbinding pathway of Indinavir from the HIV protease enzyme.

(MP4)

Click here for additional data file.^{(16.2MB, mp4)}

S2 File. HIV-rep2.mp4, the 2^nd unbinding pathway of Indinavir from the HIV protease enzyme.

(MP4)

Click here for additional data file.^{(23.3MB, mp4)}

S3 File. HIV-rep3.mp4, the 3^rd unbinding pathway of Indinavir from the HIV protease enzyme.

(MP4)

Click here for additional data file.^{(30.6MB, mp4)}

S4 File. HTLV-1-rep1.mp4, the 1^st unbinding pathway of Indinavir from the HTLV-1 protease enzyme.

(MP4)

Click here for additional data file.^{(50.5MB, mp4)}

S5 File. HTLV-1-rep2.mp4, the 2^nd unbinding pathway of Indinavir from the HTLV-1 protease enzyme.

(MP4)

Click here for additional data file.^{(30MB, mp4)}

S6 File. HTLV-1-rep3.mp4, the 3^rd unbinding pathway of Indinavir from the HTLV-1 protease enzyme.

(MP4)

Click here for additional data file.^{(42MB, mp4)}

Data Availability

The data underlying this study are available on Zenodo (https://zenodo.org/record/5121095#.YPpq470zbIU).

Funding Statement

This study was supported by Golestan University, Gorgan, Iran.

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PLoS One. doi: 10.1371/journal.pone.0257916.r001

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14 Jul 2021

PONE-D-21-18336

Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by Supervised Molecular Dynamics simulation

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: No

Reviewer #2: N/A

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: This article is a theoretical study of the ligand unbinding of HIV and HTLV-1 proteases. Through the supervised molecular dynamics (SuMD) simulations on the two proteases in complex with the indinavir inhibitor, the authors observed multiple unbinding processes. Subsequently, the detailed analyses of these unbinding pathways suggested that the unfavorable interactions from Phe67/Phe67′, and Trp98/Trp98′ to the inhibitor, coupled with the higher fluctuations of the flap region of HTLV-1 protease are the reasons for the poor inhibitory activity of indinavir against HTLV-1 as compared to that against HIV protease. HIV and HTLV-1 proteases (particularly the former) are two extremely popular biomolecule systems for MD simulations to study the structural dynamics, protease-inhibitor interactions, et al. The observations in the present study are, to be honest, not novel as compared to the previous various simulation studies. The main issues to be addressed include:

1) All the conclusions are made based on three observed unbinding events per protease. The simulation data seems not solid enough.

2) SuMD yielded the time of 173, 183.5, and 338 ns required for the unbinding of the indinavir from HIV protease. Although no biased force is added in the simulation, SuMD is certainly different to the conventional MD and the simulated unbinding times are not the times in the reality. Then the direct comparison of the unbinding times between HIV and HTLV-1 proteases should be meaningless. The authors need to discuss it in the manuscript.

3) I couldn’t understand how the conclusions about the more flexible and fluctuated flaps of HTLV-1 than that of HIV were made from Fig. 6 and higher fluctuation of the indinavir atoms in the active site of HTLV-1 than in HIV were made from Fig. 10. The authors need to somehow redraw these figures to let people clearly know how these conclusions are drawn.

4) The authors mentioned that the simulation results in this work can help design more effective and more selective inhibitors for the HTLV-1 protease. Unfortunately, detailed discussion is absent in the article.

Reviewer #2: Obtaining and understanding the ligand unbinding mechanism and pathways is an important task in computational aided drug discovery. In this paper, the author use an unbiased, but out-of-equilibrium, MD simulation method, called supervised unbiased MD (SuMD), and apply it to a challenging systems. The topic of ligand unbinding is of general interested and should be of interest to the readership of PLoS One.

However, in the correct form, I am of the opinion that the manuscript is not of sufficient quality to be accepted for publication in PloS One (or other journals). The manuscript has to be significantly improved to be accepted for publication. Once the authors have made substantial changes to revise the manuscript, I am ready to reconsider my assessment.

I note that I reviewed a different manuscript of the same authors for Scientific Reports a few months ago. In that manuscript, the author also used SuMD and applied to a different systems. Some of my comments below regarding the SuMD method and the general presentation of results are nearly the same as I had for that manuscript. Therefore, the authors should have been aware of these issues.

In following, I list my comments and suggestions to improve the manuscript.

* A major issue that I have with the manuscript is that the authors heavily emphasize that SuMD is an unbiased method, while not fully acknowledging that the method is out-of-equilibrium due to the iterative selection of starting points for the subsequent simulations. Thus, the methods cannot give reliable kinetics (this is acknowledged for SuMD in Ref 41 of the manuscript). Furthermore, this makes SuMD rather similar to other out-of-equilibrium methods that use unbiased simulations like weighted ensemble, forward flux sampling, milestoning, and more (some of which allow for obtaining kinetics under certain conditions). This point should be discussed in manuscript and appropriate citations added if they not already included.

* Lines 85-87: "Unraveling the drug′s unbinding pathway from its target protein is an excellent task that can be very useful to understand the process′s mechanism and the residues involved" This sentence does not make sense, I ask the authors to re-phrase it.

* Lines 88-89: "One of the most accurate approaches for this task is the Supervised MD (SuMD) simulations". To describe SuMD as "One of the most accurate approaches" is a very strong statement by the authors. Do they have results that can be used to back-up this statement? For example, work comparing SuMD to other methods. If not, I would ask them to tone down this statement.

* Line 93-94: "This atomistic approach [SuMD] is entirely unbiased and is very accurate." Following the previous comment. To say that SuMD is "very accurate" is a rather strong statement by the authors. Do they have results that can be used to back-up this statement? For example, work comparing SuMD with other methods. If not, I would ask them to tone down this statement. Furthermore, as SuMD cannot give the correct kinetics or binding energies (e.g., discussed in Ref 41), it would perhaps not correct to call it "very accurate".

* Line 93-94: "This atomistic approach [SuMD] is entirely unbiased and is very accurate." While it is true that SuMD use unbiased simulations, it is not an equilibrium method. Rather I would characterize it as an out-of-equilibrium method. For this reason, while the unbinding pathways obtained from SuMD might be correct, SuMD cannot give the correct kinetics or binding energies. This is for example discussed in Ref 41. I would ask the authors to acknowledge that SuMD is not an equilibrium method.

* Line 93-94: Following my previous comment, I find a similarity of SuMD (and HSuMD) to other out-of-equilbrium methods like weighted ensemble, forward flux sampling, milestoning, and others. Many of these methods have been applied to ligand unbinding. Some of these methods can, under certain conditions, also obtain the correct kinetic parameters. I ask that the author to add citations to other similar out-of-equilibrium methods.

* Line 40: "HIV has been the most advertised virus on planet earth." I don't think that this is right phrase to use here.

* I assume that there have been many other papers that have investigated the HTLV-1 and HIV protease using different molecular simulation techniques. However, I find missing in the introduction proper discussion about these other paper. This needs to be added.

* Line 108-109: The MD simulations time step is not listed as it should be. This should be added.

* Line 112: The number of water molecules included in the two systems is missing. This needs to be added.

* Line 119-121: "The modified Berendsen (V-rescale) thermostat [55] and Parrinello–Rahman barostat [56] respectively were applied for 100 and 300 ps to keep the system in the stable environmental conditions (310 K, 1 Bar)." This is a bit weird statement, it sounds like a thermostat or a barostat was not used for the production runs. I assume what the authors mean that they did 100 ps of NVT equilibration and 300 ps of NPT equilibration. This should be clarified.

* Line 121-122: Given the previous comment, were the production runs done in NVT or NPT conditions? If so, what were the thermo- and barostats used (I assume the same as for the equilibration?) This should be clarified. Furthermore, parameters for the thermo- and barostat should be listed, like relaxation times, etc.

* Line 121-122: "Finally, SuMD simulations [57] were carried out under the periodic boundary conditions (PBC), set at XYZ coordinates to ensure that the atoms had stayed inside the simulation box," This is also a strange statement, it sound like PBC were not used for the previous energy minimization and the equilibration runs (given that the authors used Gromacs, PBC should have been used there). Therefore, this should be clarified. Furthermore, the phrase "set at XYZ coordinates to ensure that the atoms had stayed inside the simulation box," is very unclear, what do the authors mean here? That they removed center of mass motion? This needs to be clarified.

* Line 125-126: "The free energy landscapes were rendered using Matplotlib [60]." As I mention below, it would be more correct to call these "out-of-equilibrium free energy landscapes"

* Line 126-127: "In addition, to estimate the binding free energy we used the g_mmpbsa package [61]." In the manuscript, the authors never mention binding free energies again, so I assume this text should not be there, maybe it was not removed by mistake. The authors need to fix this.

* Line 129-130: "the entire simulation is divided into a series of replicas, and a specific parameter is monitored throughout them as the guideline to choose the starting point of the next replica." How was the SuMD procedure implemented? Is it done in an external script and using some other codes? (For example in Ref 41, PLUMED was used to calculate distances.) This should be detailed in the manuscript.

* Lines 132-135: "For example, in our series of replicas, we considered the distance between the Center Of Mass (COM) of the drug and the COM of the Asp32 and Asp32′ in the HTLV-1 and the Asp25 and the Asp25′ in the HIV proteases as the guideline for selecting the best frame to be the starting point of the next replica." For full reproducibility, more details should be listed for how the distance in the SuMD simulations is defined. For the ligand, are all atoms taking into account or only heavy atoms? The same for the amino acid residues, it should be listed in the what atoms on the residues are considered (perhaps in the SI).

* Lines 132-135: "For example, in our series of replicas," I don't see why "For example" is needed in this sentence.

* Lines 132-135: "the COM of the Asp32 and Asp32′ in the HTLV-1 and the Asp25 and the Asp25′ in the HIV proteases" This is unclear to me. Does this mean that the author calculated two distances for each system? If so, how was this used in the SuMD procedure? Did they use the maximum of the two distances? This should be clarified.

* Lines 133: "Asp32 and Asp32'" I am not an expert on the specific system so it is unclear to me what is the difference between "Asp32" and "Asp32'". From Figure 5, I would assume that "Asp32" means amino acid from chain A and "Asp32'" means amino acid from chain B. The authors cannot expect the reader to know and understand this notion used for the amino acids. Therefore, I ask that the authors explicitly define the notation used for the amino acids and what is the difference between "Asp32" and "Asp32'".

* Lines 176-178: "There are no biasing forces involved in the simulations, and they are entirely unbiased. The only difference between this method and conventional MD simulations is the automatic supervision at choosing the most appropriate frame in a replica for extending the simulation." This discussion ignores the fact that the frame selection creates an out-of equilibrium effect. Therefore, even though SuMD is an unbiased method, it is an out-of-equilibrium method. This should be acknowledged here.

* Figure 2: How is the RMSD of the Indinavir defined? This should be detailed in the caption of Figure 2.

* Figure 2: For the six simulations considered, I would like to see in the results, or in the SI, the time series of the distances that are used in the SuMD. This is useful for reader to understand better how the SuMD methods acts for these systems.

* Line 190-191: "In total, three stable states were observed during the unbinding pathways; (i) the Native state (N), (ii) the Intermediate states (I1, I2) and, (iii) the Solvated state (S) (Fig. 3a-f)." The Figure 3 that the authors refer to includes what the authors call "Free energy landscapes (FEL) of the unbinding pathways". I agree that showing the unbinding obtained from SuMD in this manner is useful. However, these are not "free energy landscapes" in traditional meaning of the notation as SuMD is an out-of-equilibrium method. Thus, I don't think it is correct to call this a FEL, at least in equilibrium meaning of a FEL. I would ask the authors to make it clear in the text that the the surfaces in fig. 3a-f are not traditional equilibrium free energy landscapes, but rather out-of-equilibrium.

* Figure 3, line 204: "Free energy landscapes (FEL) of the unbinding pathways" same as previous comment. I ask the authors to make it clear that the FEL shown in Figure 3 are not traditional equilibrium free energy landscapes, but rather out-of-equilibrium.

* Figure 3: For the reader to be able to compare the different surfaces better, I would suggest the authors to use the same color scale in all figures. Panel (d) has a different color scale (from 0 to 23.70) than all other panels (from 0 to 20.30).

* Figure 5: How are the contribution of the different residues to the interaction energy obtained? Are they averaged over some part of the simulation? This should be detailed in the caption of Figure 5.

* Figure 5: In Figure 5, the authors talk about chain A and B and use the notation Ala59-A and Ala59-B (for example). However, elsewhere in the manuscript the authors use the notation Ala59 and Ala59' (for example) that I assume mean that Ala59 would be from chain A and Ala59' is from chain B. To avoid confusion, the authors should use the same notation throughout the manuscript. Therefore, this should be changed either in Figure 5 or everywhere else in the manuscript.

* Figure 6: For the reader to be able to compare the different systems better I would ask the authors to use the same y-scale in panels (a) and (b), so that both go from 0 to 0.45.

* Figure 6: For the reader to be able to compare the different systems better I would ask the authors to use the same y-scale in panels (c) and (d), so that both go from 0 to 0.9.

* Figure 7: lines 285 and 286: "The free energy landscape" Same as for Figure 3, I ask the authors to make it clear that the FEL shown in Figure 7 are not traditional equilibrium free energy landscapes, but rather out-of-equilibrium.

* Figure 7: Same as for Figure 3, for the reader to be able to compare the different surfaces better, I would suggest the authors to use the same color scale in panels (a) and (b).

* Regarding Data Availability Statement. The authors state "All data will be available online" without stating how it will be made available. I don't think this is line with PLoS One policy and needs to be fixed before submission. The authors could for example upload the data to Zenodo.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Sep 27;16(9):e0257916. doi: 10.1371/journal.pone.0257916.r002

Author response to Decision Letter 0

23 Jul 2021

Dear Editor

Thanks for the constructive comments on the manuscript. We are pleased to submit the revised version of our original research paper entitled “Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by Supervised Molecular Dynamics simulation” for publication in your journal. We carefully applied the reviewers’ comments in the text and the figures of the manuscript and hope that this version is appropriate for publication. We would be honored to be a part of your impressive journal.

Funding: This study was supported by Golestan University, Gorgan, Iran.

Thank you in advance for your consideration

Yours sincerely,

Hassan Aryapour

Reviewer #1:

This article is a theoretical study of the ligand unbinding of HIV and HTLV-1 proteases. Through the supervised molecular dynamics (SuMD) simulations on the two proteases in complex with the indinavir inhibitor, the authors observed multiple unbinding processes. Subsequently, the detailed analyses of these unbinding pathways suggested that the unfavorable interactions from Phe67/Phe67′, and Trp98/Trp98′ to the inhibitor, coupled with the higher fluctuations of the flap region of HTLV-1 protease are the reasons for the poor inhibitory activity of indinavir against HTLV-1 as compared to that against HIV protease. HIV and HTLV-1 proteases (particularly the former) are two extremely popular biomolecule systems for MD simulations to study the structural dynamics, protease-inhibitor interactions, et al. The observations in the present study are, to be honest, not novel as compared to the previous various simulation studies. The main issues to be addressed include:

Question #1: All the conclusions are made based on three observed unbinding events per protease. The simulation data seems not solid enough.

Answer #1: Three SuMD production runs have been performed for each enzyme and the data achieved are all consistent. We believe reconstructing three unbinding events was adequate to reach a scientific conclusion. However, each ligand may have an infinite number of unbinding paths, and it is impossible to reach all of them. Nevertheless, the more replicas there are, the results will be closer to reality.

Question #2: SuMD yielded the time of 173, 183.5, and 338 ns required for the unbinding of the indinavir from HIV protease. Although no biased force is added in the simulation, SuMD is certainly different from the conventional MD and the simulated unbinding times are not the times in reality. Then the direct comparison of the unbinding times between HIV and HTLV-1 proteases should be meaningless. The authors need to discuss it in the manuscript.

Answer #2: An explanation describing this point is present in the text: “Although Indinavir inhibits HIV protease much more selectively than the HTLV-1 protease, the overall time needed for the unbinding events of HTLV-1 protease case was considerably more than that of the HIV protease case. However, many factors govern an inhibitor's activity, and only comparing the overall duration of the unbinding events in three series of replicas is not a correct way of comparing these two cases”.

Question #3: I couldn’t understand how the conclusions about the more flexible and fluctuated flaps of HTLV-1 than that of HIV were made from Fig. 6 and higher fluctuation of the indinavir atoms in the active site of HTLV-1 than in HIV were made from Fig. 10. The authors need to somehow redraw these figures to let people clearly know how these conclusions are drawn.

Answer #3: In Fig. 6, the RMSF values of the two proteases have been shown in which the values of the flap region of the HTLV-1 protease are noticeably higher than that of HIV protease. Also, in Fig. 10, the RMSF values of the Indinavir atoms have been compared in which these values are higher as well. We think the discussion in the text of the manuscript and the caption are self-explanatory.

Question #4: The authors mentioned that the simulation results in this work can help design more effective and more selective inhibitors for the HTLV-1 protease. Unfortunately, detailed discussion is absent in the article.

Answer #4: The last paragraph of the discussion section describes some suggestions for designing a more effective inhibitor for the HTLV-1 protease.

In following, I list my comments and suggestions to improve the manuscript.

Question #1: A major issue that I have with the manuscript is that the authors heavily emphasize that SuMD is an unbiased method, while not fully acknowledging that the method is out-of-equilibrium due to the iterative selection of starting points for the subsequent simulations. Thus, the methods cannot give reliable kinetics (this is acknowledged for SuMD in Ref 41 of the manuscript). Furthermore, this makes SuMD rather similar to other out-of-equilibrium methods that use unbiased simulations like weighted ensemble, forward flux sampling, milestoning, and more (some of which allow for obtaining kinetics under certain conditions). This point should be discussed in manuscript and appropriate citations added if they not already included.

Answer #1: The SuMD method counts as an unbiased method, and it has been used by several other scientific groups which are cited in the text. However, in the methodology, we addressed the out-of-equilibration problem by running NVT, and NPT equilibrium runs after every frame selection before the 500 ps production runs. Therefore, the algorithm we have written to perform the SuMD is NOT out-of-equilibrium. An explanation about this has been added to the text.

Question #2: Lines 85-87: "Unraveling the drug′s unbinding pathway from its target protein is an excellent task that can be very useful to understand the process′s mechanism and the residues involved" This sentence does not make sense, I ask the authors to re-phrase it.

Answer #2: The statement has been Improved.

Question #3: Lines 88-89: "One of the most accurate approaches for this task is the Supervised MD (SuMD) simulations". To describe SuMD as "One of the most accurate approaches" is a very strong statement by the authors. Do they have results that can be used to back-up this statement? For example, work comparing SuMD to other methods. If not, I would ask them to tone down this statement.

Answer #3: The statement has been Improved.

Question #4: Line 93-94: "This atomistic approach [SuMD] is entirely unbiased and is very accurate." Following the previous comment. To say that SuMD is "very accurate" is a rather strong statement by the authors. Do they have results that can be used to back-up this statement? For example, work comparing SuMD with other methods. If not, I would ask them to tone down this statement. Furthermore, as SuMD cannot give the correct kinetics or binding energies (e.g., discussed in Ref 41), it would perhaps not correct to call it "very accurate".

Answer #4: The statement has been Improved.

Question #5: Line 93-94: "This atomistic approach [SuMD] is entirely unbiased and is very accurate." While it is true that SuMD use unbiased simulations, it is not an equilibrium method. Rather I would characterize it as an out-of-equilibrium method. For this reason, while the unbinding pathways obtained from SuMD might be correct, SuMD cannot give the correct kinetics or binding energies. This is for example discussed in Ref 41. I would ask the authors to acknowledge that SuMD is not an equilibrium method.

Answer #5: An explanation was added.

Question #6: Line 93-94: Following my previous comment, I find a similarity of SuMD (and HSuMD) to other out-of-equilbrium methods like , milestoning, and others. Many of these methods have been applied to ligand unbinding. Some of these methods can, under certain conditions, also obtain the correct kinetic parameters. I ask that the author to add citations to other similar out-of-equilibrium methods.

Answer #6: citations to other similar methods were added.

Question #7: Line 40: "HIV has been the most advertised virus on planet earth." I don't think that this is right phrase to use here.

Answer #7: The statement has been Improved.

Question #8: I assume that there have been many other papers that have investigated the HTLV-1 and HIV protease using different molecular simulation techniques. However, I find missing in the introduction proper discussion about these other paper. This needs to be added.

Answer #8: There are numerous papers about the MD simulation HIV protease but very few about the MD simulation of HTLV-1 protease. Worse still, nearly no paper exists that present and compare the dynamical conformational details of these two important enzymes.

Question #9: Line 108-109: The MD simulations time step is not listed as it should be. This should be added.

Answer #9: added.

Question #10: Line 112: The number of water molecules included in the two systems is missing. This needs to be added.

Answer #10: added.

Question #11: Line 119-121: "The modified Berendsen (V-rescale) thermostat [55] and Parrinello–Rahman barostat [56] respectively were applied for 100 and 300 ps to keep the system in the stable environmental conditions (310 K, 1 Bar)." This is a bit weird statement, it sounds like a thermostat or a barostat was not used for the production runs. I assume what the authors mean that they did 100 ps of NVT equilibration and 300 ps of NPT equilibration. This should be clarified.

Answer #11: The text has been Improved.

Question #12: Line 121-122: Given the previous comment, were the production runs done in NVT or NPT conditions? If so, what were the thermo- and barostats used (I assume the same as for the equilibration?) This should be clarified. Furthermore, parameters for the thermo- and barostat should be listed, like relaxation times, etc.

Answer #12: The text has been Improved and clarified.

Question #13: Line 121-122: "Finally, SuMD simulations [57] were carried out under the periodic boundary conditions (PBC), set at XYZ coordinates to ensure that the atoms had stayed inside the simulation box," This is also a strange statement, it sound like PBC were not used for the previous energy minimization and the equilibration runs (given that the authors used Gromacs, PBC should have been used there). Therefore, this should be clarified. Furthermore, the phrase "set at XYZ coordinates to ensure that the atoms had stayed inside the simulation box," is very unclear, what do the authors mean here? That they removed center of mass motion? This needs to be clarified.

Answer #13: The text has been Improved and clarified.

Question #14: Line 125-126: "The free energy landscapes were rendered using Matplotlib [60]." As I mention below, it would be more correct to call these "out-of-equilibrium free energy landscapes"

Answer #14: refer to the Answer #1.

Question #15: Line 126-127: "In addition, to estimate the binding free energy we used the g_mmpbsa package [61]." In the manuscript, the authors never mention binding free energies again, so I assume this text should not be there, maybe it was not removed by mistake. The authors need to fix this.

Answer #15: The text was fixed. We calculated the interaction energies by g-mmpbsa.

Question #16: Line 129-130: "the entire simulation is divided into a series of replicas, and a specific parameter is monitored throughout them as the guideline to choose the starting point of the next replica." How was the SuMD procedure implemented? Is it done in an external script and using some other codes? (For example in Ref 41, PLUMED was used to calculate distances.) This should be detailed in the manuscript.

Answer #16: An explanation was added.

Question #17: Lines 132-135: "For example, in our series of replicas, we considered the distance between the Center Of Mass (COM) of the drug and the COM of the Asp32 and Asp32′ in the HTLV-1 and the Asp25 and the Asp25′ in the HIV proteases as the guideline for selecting the best frame to be the starting point of the next replica." For full reproducibility, more details should be listed for how the distance in the SuMD simulations is defined. For the ligand, are all atoms taking into account or only heavy atoms? The same for the amino acid residues, it should be listed in the what atoms on the residues are considered (perhaps in the SI).

Answer #17: corrected.

Question #18: Lines 132-135: "For example, in our series of replicas," I don't see why "For example" is needed in this sentence.

Answer #18: corrected.

Question #19: Lines 132-135: "the COM of the Asp32 and Asp32′ in the HTLV-1 and the Asp25 and the Asp25′ in the HIV proteases" This is unclear to me. Does this mean that the author calculated two distances for each system? If so, how was this used in the SuMD procedure? Did they use the maximum of the two distances? This should be clarified.

Answer #19: Corrected.

Question #20: Lines 133: "Asp32 and Asp32'" I am not an expert on the specific system so it is unclear to me what is the difference between "Asp32" and "Asp32'". From Figure 5, I would assume that "Asp32" means amino acid from chain A and "Asp32'" means amino acid from chain B. The authors cannot expect the reader to know and understand this notion used for the amino acids. Therefore, I ask that the authors explicitly define the notation used for the amino acids and what is the difference between "Asp32" and "Asp32'".

Answer #20: Corrected.

Question #21: Lines 176-178: "There are no biasing forces involved in the simulations, and they are entirely unbiased. The only difference between this method and conventional MD simulations is the automatic supervision at choosing the most appropriate frame in a replica for extending the simulation." This discussion ignores the fact that the frame selection creates an out-of equilibrium effect. Therefore, even though SuMD is an unbiased method, it is an out-of-equilibrium method. This should be acknowledged here.

Answer #21: refer to the Answer #1.

Question #22: Figure 2: How is the RMSD of the Indinavir defined? This should be detailed in the caption of Figure 2.

Answer #22: Corrected.

Question #23: Figure 2: For the six simulations considered, I would like to see in the results, or in the SI, the time series of the distances that are used in the SuMD. This is useful for reader to understand better how the SuMD methods acts for these systems.

Answer #23: Added to the SI.

Question #24: Line 190-191: "In total, three stable states were observed during the unbinding pathways; (i) the Native state (N), (ii) the Intermediate states (I1, I2) and, (iii) the Solvated state (S) (Fig. 3a-f)." The Figure 3 that the authors refer to includes what the authors call "Free energy landscapes (FEL) of the unbinding pathways". I agree that showing the unbinding obtained from SuMD in this manner is useful. However, these are not "free energy landscapes" in traditional meaning of the notation as SuMD is an out-of-equilibrium method. Thus, I don't think it is correct to call this a FEL, at least in equilibrium meaning of a FEL. I would ask the authors to make it clear in the text that the the surfaces in fig. 3a-f are not traditional equilibrium free energy landscapes, but rather out-of-equilibrium.

Answer #24: refer to the Answer #1.

Question #25: Figure 3, line 204: "Free energy landscapes (FEL) of the unbinding pathways" same as previous comment. I ask the authors to make it clear that the FEL shown in Figure 3 are not traditional equilibrium free energy landscapes, but rather out-of-equilibrium.

Answer #25: refer to the Answer #1.

Question #26: Figure 3: For the reader to be able to compare the different surfaces better, I would suggest the authors to use the same color scale in all figures. Panel (d) has a different color scale (from 0 to 23.70) than all other panels (from 0 to 20.30).

Answer #26: corrected

Question #27: Figure 5: How are the contribution of the different residues to the interaction energy obtained? Are they averaged over some part of the simulation? This should be detailed in the caption of Figure 5.

Answer #27: corrected

Question #28: Figure 5: In Figure 5, the authors talk about chain A and B and use the notation Ala59-A and Ala59-B (for example). However, elsewhere in the manuscript the authors use the notation Ala59 and Ala59' (for example) that I assume mean that Ala59 would be from chain A and Ala59' is from chain B. To avoid confusion, the authors should use the same notation throughout the manuscript. Therefore, this should be changed either in Figure 5 or everywhere else in the manuscript.

Answer #28: Both forms have been used for the text and also for the figures and we have made them clear throughout the manuscript. We believe this does not make any complications for the readers.

Question #29: Figure 6: For the reader to be able to compare the different systems better I would ask the authors to use the same y-scale in panels (a) and (b), so that both go from 0 to 0.45.

Answer #29: Corrected

Question #30: Figure 6: For the reader to be able to compare the different systems better I would ask the authors to use the same y-scale in panels (c) and (d), so that both go from 0 to 0.9.

Answer #30: Corrected

Question #31: Figure 7: lines 285 and 286: "The free energy landscape" Same as for Figure 3, I ask the authors to make it clear that the FEL shown in Figure 7 are not traditional equilibrium free energy landscapes, but rather out-of-equilibrium.

Answer #31: refer to the Answer #1.

Question #32: Figure 7: Same as for Figure 3, for the reader to be able to compare the different surfaces better, I would suggest the authors to use the same color scale in panels (a) and (b).

Answer #32: Corrected

Attachment

Submitted filename: Response to Reviewers-Final.docx

Click here for additional data file.^{(30.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0257916.r003

Decision Letter 1

Israel Silman

24 Aug 2021

PONE-D-21-18336R1

Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by Supervised Molecular Dynamics simulation

PLOS ONE

Dear Dr. Aryapour,

As you can see, Reviewer 2 still has many serious concerns that you are requested to address as fully as possible in your revised manuscript and in your rebuttal.

Please submit your revised manuscript by Oct 08 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Israel Silman

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: The authors have addressed some of the my comments. However, overall I feel that the authors have not done enough improvements to their manuscript so that it can be accepted for publication in PLoS One. Therefore, I would have to recommend rejection.

In the following I will some furthermore comments regarding the manuscript.

I am still not convinced at all with their argument that by performing 50 ps of NVT and NPT equilibration runs after the

frame selection before the 500 ps production runs, they solve the "out-of-equilibration problem" of SuMD. I think this is a point that requires much more consideration and justifications than given in the manuscript

The bottom line is that SuMD can give pathways and mechanisms of ligand unbinding. However, it is clear (see https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b01094) that SuMD cannot give kinetics or dynamics of ligand unbinding due to procedure of running short simulations and selecting a frame for the next iteration. Pathways and mechanisms of ligand unbinding are certainly of interest so insight obtained with SuMD is valuable. However, I would expect user of any method to be honest of the limitations of the method that they are using. In the current version of the manuscript I feel that the authors are not totally honest with the limitations of SuMD.

Furthermore, I would like to reiterate my comment regarding talking about talking about Free Energy Landscapes in Figures 3 and 7. A free energy landscape is an equilibrium property, it is the logarithm of the probability distribution in the collective variables considered. This probability distribution would tell us that what is the (relative) population of the different states if we would run an infinitely long MD simulations. To estimate a FEL from a finite MD simulations, one would need to observe many transitions between the different metastable states (e.g., bound and unbound state).

I believe that the authors can agree with me what they present in Figures 3 and 7 are strictly not true equilibrium free energy landscapes. As I said in my previous report, this is still a useful way to present the results from SuMD simulations. It should just be acknowledged that these are not true equilibrium free energy landscapes, but rather a useful way to present the results from the SuMD in a CV space.

Another issue that I noticed while reading https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b01094 better (this is the papers from the developers of SuMD where they apply it to ligand unbinding), is that they SuMD procedure used in the current manuscript is different from the one described on page 2 (1805) in https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b01094. The procedure described there is much more intricate while the procedure used in the current manuscripts in much simpler. For example, in [DOI:10.1021/acs.jcim.9b01094] they use the idea of a "productive short MD run" that is not used here. I would have expect the authors to address and acknowledge this in the their manuscript.

Regarding the data that the authors have made available on Zenodo. The authors state that "Yes - all data are fully available without restriction" and that "all data is available online at: " ext-link-type="uri" xlink:type="simple">https://zenodo.org/record/5121095#.YPpq470zbIU". However, there the authors only include a Gromacs TPR file for each run, and a Gromcas XTC trajectory. Furthermore, there is no information given on how to use the data. In my opinion this does not fulfill the requirements of PLoS of "Authors are required to make all data underlying the findings described fully available, without restriction, and from the time of publication." There is no way of seeing how the conclusion in the manuscript are reached from the data include on Zenodo. I would ask the authors to greatly expand the data included so that the requirement of making available all data needed to reach the conclusions in the manuscript. In my opinion, this should include initial geometries (e.g., Gromcas GRO file), topology files (Gromacs TOP file), and also the script used to perform the simulations. Furthermore, at the very least there should be a readme file describing how to guide people through the data.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. 2021 Sep 27;16(9):e0257916. doi: 10.1371/journal.pone.0257916.r004

Author response to Decision Letter 1

25 Aug 2021

Reviewer #2: The authors have addressed some of my comments. However, overall, I feel that the authors have not done enough improvements to their manuscript so that it can be accepted for publication in PLoS One. Therefore, I would have to recommend rejection. In the following I will some furthermore comments regarding the manuscript.

Question #1: I am still not convinced at all with their argument that by performing 50 ps of NVT and NPT equilibration runs after the

Answer #1: This study did not focus on the justification of the SuMD method. It focuses on the applications of SuMD in uncovering the unbinding pathways of a ligand. We believe enough explanations have been added to the text.

Question #2: The bottom line is that SuMD can give pathways and mechanisms of ligand unbinding. However, it is clear (see https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b01094) that SuMD cannot give kinetics or dynamics of ligand unbinding due to procedure of running short simulations and selecting a frame for the next iteration. Pathways and mechanisms of ligand unbinding are certainly of interest so insight obtained with SuMD is valuable. However, I would expect user of any method to be honest of the limitations of the method that they are using. In the current version of the manuscript I feel that the authors are not totally honest with the limitations of SuMD.

Answer #2: Apart from the fact that SuMD is an out-of-equilibrium method, it is a great method to achieve a mechanistic and energetic insight into the unbinding pathway of a ligand, and that was the area we focused on in this study. We did not focus on the kinetics parameters of the unbinding pathway. We even mentioned that the duration times of the unbinding pathways of both ligands are in contrast with the real potentials of the ligands: "Although Indinavir inhibits HIV protease much more selectively than the HTLV-1 protease, the overall time needed for the unbinding events of the HTLV-1 protease case was considerably more than that of the HIV protease case. However, many factors govern an inhibitor's activity, and only comparing the overall duration of the unbinding events in three series of replicas is not a correct way of comparing these two cases. This may be due to the inherent limitations of the SuMD method. Because the time window is set to 500 ps, therefore more conformational sampling by ligand is limited."

Question #3: Furthermore, I would like to reiterate my comment regarding talking about talking about Free Energy Landscapes in Figures 3 and 7. A free energy landscape is an equilibrium property, it is the logarithm of the probability distribution in the collective variables considered. This probability distribution would tell us that what is the (relative) population of the different states if we would run an infinitely long MD simulations. To estimate a FEL from a finite MD simulations, one would need to observe many transitions between the different metastable states (e.g., bound and unbound state).

Answer #3: The phrase "out-of-equilibrium" was added to the FEL section to clarify this fact for the readers.

Question #4: Another issue that I noticed while reading https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b01094 better (this is the papers from the developers of SuMD where they apply it to ligand unbinding), is that they SuMD procedure used in the current manuscript is different from the one described on page 2 (1805) in https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b01094. The procedure described there is much more intricate while the procedure used in the current manuscripts in much simpler. For example, in [DOI:10.1021/acs.jcim.9b01094] they use the idea of a "productive short MD run" that is not used here. I would have expect the authors to address and acknowledge this in the their manuscript.

Answer #4: An explanation about the difference was added to the method section.

Question #5: Regarding the data that the authors have made available on Zenodo. The authors state that "Yes - all data are fully available without restriction" and that "all data is available online at: https://zenodo.org/record/5121095#.YPpq470zbIU". However, there the authors only include a Gromacs TPR file for each run, and a Gromcas XTC trajectory. Furthermore, there is no information given on how to use the data. In my opinion this does not fulfill the requirements of PLoS of "Authors are required to make all data underlying the findings described fully available, without restriction, and from the time of publication." There is no way of seeing how the conclusion in the manuscript are reached from the data include on Zenodo. I would ask the authors to greatly expand the data included so that the requirement of making available all data needed to reach the conclusions in the manuscript. In my opinion, this should include initial geometries (e.g., Gromcas GRO file), topology files (Gromacs TOP file), and also the script used to perform the simulations. Furthermore, at the very least there should be a readme file describing how to guide people through the data.

Answer #5: We provided the TPR and the XTC files of each replica in the Zenodo database. These two files are enough for all of the analysis of this study. You can extract the topology, initial geometries, and much more files only by having these two files. Therefore we believe it completely fulfills the journal requirements.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(16.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0257916.r005

Decision Letter 2

Israel Silman

14 Sep 2021

Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by Supervised Molecular Dynamics simulation

PONE-D-21-18336R2

Dear Dr. Aryapour,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Israel Silman

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: I am still not fully convinced by arguments of the authors regarding the SuMD methods. Furthermore, I do not agree with how the authors present the SuMD method in this manuscript. However, I can agree that this is more of a scientific disagreement. Therefore, I can agree that the manuscript is accepted for publication in PLoS One.

As for the data that the authors have deposited on Zenodo and their reply regarding that. I still stand by my previous comment for the revised (R1) version. I feel that the authors are trying to do the bare-minimum to fulfill the data sharing requirement of PLoS, rather than really trying following an open science policy like PLoS one is trying to foster. Unfortunately, this type of attitude is prevalent in the simulation community. Therefore, I still answer "No" to the question "Have the authors made all data underlying the findings in their manuscript fully available?" I leave it up to the editor have to address this issue.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0257916.r006

Acceptance letter

Israel Silman

16 Sep 2021

PONE-D-21-18336R2

Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by supervised molecular dynamics simulation

Dear Dr. Aryapour:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Israel Silman

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. The distance between the COM of Indinavir and the COM of the Asp residues in the active site of HIV and HTLV-1 protease during the unbinding pathway.

a, b, c, The HIV protease, d, e, f, The HTLV-1 protease.

(TIF)

Click here for additional data file.^{(2.2MB, tif)}

S2 Fig. The VdW, electrostatic, and total interaction energies between Indinavir and the two proteases during the unbinding pathways.

a, b, c, The Indinavir-HIV complex and, d, e, f, The Indinavir- HTLV-1 protease complex.

(TIF)

Click here for additional data file.^{(5MB, tif)}

S3 Fig. The contribution of the active site residues to the total interaction energies in the unbinding pathways.

a, b, 1^st_, and 3^rd replica of the Indinavir-HIV protease case. c, b, 1^st_, and 2^nd replica of the Indinavir-HTLV-1 protease case.

(TIF)

Click here for additional data file.^{(3.6MB, tif)}

(TIF)

Click here for additional data file.^{(4.1MB, tif)}

S1 File. HIV-rep1.mp4, the 1^st unbinding pathway of Indinavir from the HIV protease enzyme.

(MP4)

Click here for additional data file.^{(16.2MB, mp4)}

S2 File. HIV-rep2.mp4, the 2^nd unbinding pathway of Indinavir from the HIV protease enzyme.

(MP4)

Click here for additional data file.^{(23.3MB, mp4)}

S3 File. HIV-rep3.mp4, the 3^rd unbinding pathway of Indinavir from the HIV protease enzyme.

(MP4)

Click here for additional data file.^{(30.6MB, mp4)}

S4 File. HTLV-1-rep1.mp4, the 1^st unbinding pathway of Indinavir from the HTLV-1 protease enzyme.

(MP4)

Click here for additional data file.^{(50.5MB, mp4)}

S5 File. HTLV-1-rep2.mp4, the 2^nd unbinding pathway of Indinavir from the HTLV-1 protease enzyme.

(MP4)

Click here for additional data file.^{(30MB, mp4)}

S6 File. HTLV-1-rep3.mp4, the 3^rd unbinding pathway of Indinavir from the HTLV-1 protease enzyme.

(MP4)

Click here for additional data file.^{(42MB, mp4)}

Attachment

Submitted filename: Response to Reviewers-Final.docx

Click here for additional data file.^{(30.7KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(16.5KB, docx)}

Data Availability Statement

The data underlying this study are available on Zenodo (https://zenodo.org/record/5121095#.YPpq470zbIU).

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PERMALINK

Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by supervised molecular dynamics simulation

Farzin Sohraby

Hassan Aryapour

Roles

Abstract

Introduction

Methods

Results and discussion

Fig 1. Structure of HTLV-1 and HIV protease.

Fig 2. The RMSD values of the Indinavir in the unbinding pathways in the three series of replicas for each protein-ligand complex.

Fig 3. The “out-of-equilibrium” Free Energy Landscapes (FEL) of the unbinding pathways of Indinavir.

Fig 4. The total interaction energies of Indinavir in the unbinding pathways in the three series of replicas for each protein-ligand complex.

Fig 5. The contribution of each residue from both A and B chains of the proteases in the total interaction energies between Indinavir and the enzyme in the unbinding pathways.

Fig 6. The average RMSF values of residues and the RMSD values of the two proteases’ backbone atoms in three replicas.

Fig 7. The main conformational states of the flap region were acquired during the unbinding pathways of Indinavir.

Fig 8. The first step of the unbinding pathway of Indinavir.

Fig 10. A comparison between the RMSF values of the Indinavir atoms during the three replicas of the unbinding pathways.

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Israel Silman

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Israel Silman

Roles

Author response to Decision Letter 1

Decision Letter 2

Israel Silman

Roles

Acceptance letter

Israel Silman

Roles

Associated Data

Supplementary Materials

Data Availability Statement

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