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Benchmarking the Ability of Common Docking Programs to
Correctly Reproduce and Score Binding Modes in SARS-CoV‑2
Protease Mpro
Shani Zev, Keren Raz, Renana Schwartz, Reem Tarabeh, Prashant Kumar Gupta, and Dan T. Major*
Cite This: J. Chem. Inf. Model. 2021, 61, 2957−2966
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sıSupporting Information
ABSTRACT: The coronavirus SARS-CoV-2 main protease, Mpro,
is conserved among coronaviruses with no human homolog and
has therefore attracted significant attention as an enzyme drug
target for COVID-19. The number of studies targeting Mpro for in
silico screening has grown rapidly, and it would be of great interest
to know in advance how well docking methods can reproduce the
correct ligand binding modes and rank these correctly. Clearly,
current attempts at designing drugs targeting Mpro with the aid of
computational docking would benefit from a priori knowledge of
the ability of docking programs to predict correct binding modes
and score these correctly. In the current work, we tested the ability
of several leading docking programs, namely, Glide, DOCK,
AutoDock, AutoDock Vina, FRED, and EnzyDock, to correctly identify and score the binding mode of Mpro ligands in 193 crystal
structures. None of the codes were able to correctly identify the crystal structure binding mode (lowest energy pose with root-mean-
square deviation < 2 Å) in more than 26% of the cases for noncovalently bound ligands (Glide: top performer), whereas for
covalently bound ligands the top score was 45% (EnzyDock). These results suggest that one should perform in silico campaigns of
Mpro with care and that more comprehensive strategies including ligand free energy perturbation might be necessary in conjunction
with virtual screening and docking.
■INTRODUCTION
Coronaviruses are positive-stranded RNA viruses that infect
humans and animals and cause common and severe respiratory
diseases, including severe acute respiratory syndrome (SARS)
and Middle East respiratory syndrome (MERS).
1,2
These
viruses rely heavily on functional polypeptides that are
generated by proteolytic cleavage of polyproteins that are
translated from viral RNA. The principal coronavirus proteases
responsible for this polypeptide formation are mainly protease
and papain protease. The coronavirus SARS-CoV-2 main
protease, Mpro (henceforth denoted simply as Mpro), has
garnered significant attention in the past year as an enzyme
drug target due to the COVID-19 pandemic outbreak.
3
Mpro is
a druggable target
4,5
as it is conserved among coronaviruses
and has no human homolog. The first Mpro structures were
published in early 2020.
3,6,7
The first crystal structures revealed
that the active form of Mpro is a homodimer containing two
protomers, each composed of three domains (Figure 1A). The
active site in Mpro is located in a cleft between domains I and II
(Figure 1B) and features a noncanonical catalytic Cys−His
dyad. The active site is composed of four regions: S1′, S1, S2,
and S4, with the catalytic Cys145 located in S1′and His41
located in S1′and S2 (Figure 1C). To date, well over 200
three-dimensional holostructures have been resolved at a
resolution of 3 Å or better.
3,6−9
In these structures, ligands
bind to a variety of binding sites, including covalent and
noncovalent binding in the main catalytic site, noncovalent
binding in pockets in between the two proteomers, and weakly
bound at the protein surface (i.e., in between crystallographic
homodimer units). This wealth of structural information makes
Mpro an attractive benchmarking system for testing the ability
of docking programs to correctly identify and rank the correct
poses. This becomes particularly important in light of the
severity of the COVID-19 pandemic
2
and the significant
number of mutant forms of the virus that are rapidly appearing
and might render current and future vaccines ineffective. To
date, Mpro has been studied extensively using ligand docking
and screening tools
10−19
and computational enzymology tools,
such as hybrid quantum mechanics−molecular mechanics
(QM/MM).
20−29
Received: March 6, 2021
Published: May 28, 2021
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The number of studies targeting Mpro for in silico screening
has grown,
30
and it would be of great interest to know in
advance how well docking methods can reproduce the correct
ligand binding modes and rank these correctly. Clearly, current
attempts at designing drugs targeting Mpro with the aid of
computational docking are problematic if programs struggle to
predict correct binding modes and score these correctly. This
is true even if common docking programs have undergone
extensive testing since each protein target comes with its own
challenges due to the complexity of binding pocket crevices,
nature of interactions, and solvent exposure. Therefore, critical
evaluation of how well common docking programs perform for
Mpro is important.
Since the development of the first automated docking
program DOCK,
31−35
a multitude of docking software
packages have been developed, with different physicochemical
approximations and algorithmic details. Popular docking
programs in addition to DOCK, include Autodock,
36−38
Autodock Vina,
39
Glide,
40−42
Rosettaligand,
43,44
Gold,
45−47
and CDocker.
48−51
Widely used search algorithms include
molecular dynamics (MD), Monte Carlo (MC), and genetic
algorithms (GA). Common scoring functions include force
field-based energy functions, such as CHARMM,
52−56
AMBER,
57
and OPLS
58,59
(i.e., molecular mechanics, MM),
and knowledge-based scoring functions, such as DRUG-
SCORE,
60
IT-SCORE,
61
DSX,
62
CHEMSCORE,
63
and
SMoG.
64
Specialized docking programs addressing en-
zymes,
65,66
such as EnzyDock, have also been developed.
67
Current challenges for docking methods include protein
flexibility,
68
ligand solvation, and binding-site hydration.
47,69
Thus far, several docking approaches have been employed to
screen Mpro for potential drugs in virtual screening and drug
repurposing campaigns, including Glide,
7,10,13,17,18,70−72
Auto-
dock,
11,13,73,74
Autodock Vina,
11,13,14,19,71,73,75,76
Surflex,
77
PLANT,
78
DockThor,
76
fast pulling of ligands,
14
deep
docking,
70
algebraic topology and deep learning,
79
and virtual
reality-based docking.
16
However, to the best of our knowl-
edge, no rigorous benchmark study addressing the ability of
such docking tools to reproduce and correctly rank known
ligand binding modes has been published, in spite of the
known inherent challenges in docking.
80−83
In the current work, we tested the ability of several leading
docking programs to correctly identify and score the binding
mode of Mpro ligands in 193 crystal structures (Figure S1). We
tested the following docking programs: Glide,
40−42
DOCK,
31−35
Autodock,
36−38
Autodock Vina,
39
FRED,
84−87
and EnzyDock.
67
The current results suggest that care should
be taken in applying docking programs to a challenging protein
target such as Mpro.
■METHODS
Preparing Mpro Structure Database. The available
crystal structures of ligand-containing SARS-CoV-2 Mpro were
downloaded from the RCSB PDB website (March-December
2020).
88
In total, we collected 193 different structures,
including covalent and noncovalent ligands (Table S1 and
Figure S1). All structures were aligned relative to one reference
structure (PDB ID: 5R84) for easy comparison. To perform
docking, we separated each protein and ligand into separate
files, removing crystal waters, ions, and cosolvents. Missing
residues were added using the Modeller homology program.
89
Hydrogens were added using the CHARMM simulation
platform (using HBUILD) for the protein structure and
using Openbabel for the ligands.
90,91
Visual inspection was also
performed. For systems including only one monomer, the
complementary unit was generated using the crystallographic
information included in the PDB file using CHARMM.
Protonation states of His residues were determined based on
hydrogen bonding patterns and knowledge of the chemistry
catalyzed by Mpro (Table S2), and they match the protonation
states of key His residues recently published.
23
All docking
simulations described below commenced with the CHARMM
prepared systems.
Clustering of Ligands and Water Molecules. Chemical
descriptors were calculated for all ligands from the 193 PDB
files using RDKit libraries in Python. Features thought to be
important for ligand binding were chosen. Specifically, we
computed the number of rotatable torsions, molecular weight,
number of H-bond donors and acceptors, number of aromatic
rings, the fraction of carbon atoms in sp3hybridization
(relative to all carbon atoms in the molecule), and log Pvalues.
We applied principal component analysis (PCA) using these
ligands descriptors, followed by k-means clustering to cluster
the ligands into groups. We selected the number of clusters by
silhouette analysis of the k-means clustering results. Ligand
clustering was performed using Python 3.7. Density-Based
Spatial Clustering of Applications with Noise (DBSCAN)
clustering was performed to analyze the water molecules from
all crystal structures. These water molecules were not included
in the docking studies.
Subsite-Binding Pocket Binding. To classify the binding
patterns of all the 193 protein−ligand complexes, we
categorized the ligands as bound on the surface, at the dimer
interface, or in the active site. The latter was characterized
according to the subsites S1, S1′, S2, and S4 (Figure 1C, Table
S3).
7,77
A ligand is considered to occupy a binding pocket if
any ligand atom is within 4.0 Å of any pocket atom and also
within 3.0 Å of the geometric center of the pocket (defined as
geometric center of all pocket atoms). Moreover, a ligand is
considered to be in the proximity of a binding pocket if any
ligand atom is within 4.0 Å of any pocket atom and also within
Figure 1. (A) SARS-CoV-2 Mpro dimer with bound ligand in each
active site (PDB ID: 7BQY). The domains in chain A are colored as
follows: N′-finger in bright orange, domain I in pale green, domain II
in light blue, loop L3 in light teal, and domain III in light pink. The
ligand bound to chain A is colored yellow and appears in stick
representation. Both chain B and the ligand bound to it appear in
gray. The same color code applies to (B), where the active site is
shown in greater detail. (C) Four conserved subsites are presented,
along with the Cys 145−His 41 dyad (deep teal stick representation).
The covalently bound ligand appears in limon-shade stick
representation. The protein appears as a white surface.
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J. Chem. Inf. Model. 2021, 61, 2957−2966
2958
5.0 Å of the geometric center of the pocket. The criteria were
designed to account for both small and bulky ligands and to
distinguish between binding poses where ligand groups are
well docked inside a subpocket and poses where ligand groups
are located at the periphery of a subpocket. Cutoffvalues are
suitable for various nonbonded interactions (e.g., π−π
stacking, hydrogen bond, ionic, and hydrophobic interactions).
The final values were obtained by trial and error and validated
by means of visual inspection. The subsite-binding pocket
occupancy analysis was implemented as a CHARMM
90,91
script.
Docking Protocols. To compare the performance of
selected docking programs for use with Mpro (search algorithm
and scoring function), we performed noncovalent docking
using DOCK,
31−35
Autodock Vina,
39
and FRED
84−87
and
noncovalent and covalent docking using Glide,
40−42
Auto-
dock,
36−38
and EnzyDock
67
to the systems described above. In
all docking simulations described below, the ligand was fully
flexible, while the protein was fixed (except for the covalently
connected complexes, where the appropriate Cys residue was
flexible).
Ligand Docking with Glide. Proteins and ligands were
prepared using Schrödinger’s Maestro (version 11.4, 2017-4
release) Prep Wiz and LigPrep modules, respectively, with
default settings for docking with Glide. All covalent docking
simulations were performed using the CovDock module
available in Glide.
92
For the noncovalent simulations, the
grid was generated using XGlide, which enables creation of
different grids in parallel. The grids were centered around the
ligand’s centroid. The dimensions of the enclosing box and the
bounding box were set to 12 ×12 ×12 Å3and 26 ×26 ×26
Å3, respectively, for all cases. The ligand stereochemistry was
kept during all docking simulations. The number of poses
written per ligand was set to 10,000. The scaling factors of the
vdW radii and the partial atomic charge cutoffwere set to the
default values 0.80 and 0.15, respectively. Standard precision
(SP) mode was chosen for all ligand docking runs. The
selection of the best-docked ligand structure among the
proposed poses is made based on several model energies
implemented with Glide (docking score, Prime energy and E-
model energy, and cdock affinity). Solvent effects were
incorporated using MMGBSA. All reported energies herein
used the docking score function for noncovalent docking and
the cdock affinity scoring function for covalent docking as
these performed best [i.e., produced the highest number of
top-ranking structures with root-mean-square deviation (rmsd)
< 2 Å]. In all Glide docking simulations (ligand preparation,
protein preparation, grid generation, covalent, and noncovalent
docking), the OPLS3 force field
59,93
was used.
Ligand Docking with DOCK (Version 6.9). Proteins and
ligands were prepared for docking simulations using the
DockPrep option of Chimera v.14.
94
The grid was generated
according to the center of mass of the crystal structure ligand
with a grid spacing of 0.4 Å. The maximum and minimum
radius of the sphere generated was set to 4 and 1.4 Å. All the
spheres within 10 Å of the ligand were selected for docking.
The box length surrounding the ligand was set to 6 Å, that is,
the edge of the box from any atom of the ligand was at least 6
Å away, which easily accommodates all the selected spheres.
Ligand Docking with Autodock (Version 4.2). Proteins
and ligand pdbqt files were prepared using standard AutoDock
tools (prepare_flexreceptor4.py and prepare_ligand4.py).
These files include Cartesian coordinates and Gasteiger atomic
charges
95
for each atom. AutoDock employs a united atom
method, and thus, no nonpolar hydrogens are present. The
center of mass of the crystallographic ligand was used to
determine the center of the grid. AutoDock uses one grid box
to perform the docking calculations, and the dimensions of this
box were set to 37.5 ×37.5 ×37.5 Å3and the spacing was set
to 0.375 Å for all systems. We performed flexible ligand
docking into a rigid protein environment using GA, with
default settings. For covalent docking,
96
each ligand was
prepared with the active Cys residue already present in the
input file using AutoDock tools (prepare_receptor4.py and
prepare_flexreceptor4.py). For covalent docking, the ligand
flexible torsional angles were presampled using MC simu-
lations with CHARMM prior to docking.
Ligand Docking with Autodock Vina. Protein and
ligand input pdbqt files were prepared in the same way as for
Autodock4.2 (see above). The size of the grid was set to 30.0
Figure 2. Normalized radar plot showing various features of each cluster centroid for SARS-CoV-2 Mpro ligands. Inset: pie plot with the relative
fraction of each cluster among all the ligands studied in this work. The first number is the cluster name, and the second is the number of ligands in
the cluster.
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×30.0 ×30.0 Å3, and remaining parameters were set to default
values.
Ligand Docking with FRED.
85−87
FRED is one of the
docking programs available within the OpenEye scientific
library. For the docking process, proteins and ligands were
prepared using the graphical user interface “Make Receptor”
provided with OpenEye. FRED creates a potential field around
the binding site by producing a negative image, which
complements the shape of the protein site. This potential
field is represented on a contour, which completely surrounds
the ligand. OMEGA, an internal program within OpenEye is
used to generate an ensemble of conformers for each ligand. A
total of 200 different conformers were generated for each
ligand for docking, and the 50 lowest energy docked structures
were used to select the best pose in terms of lowest rmsd or
Chemgauss energy scoring relative to the crystal ligand
structure. The proteins and ligands were held fixed during
the docking process.
Ligand Docking with EnzyDock. Protein and ligand files
were prepared as described at the beginning of Methods.
CHARMM topology (RTF) and parameters (PRM) files for
the ligands were generated using the CHARMM General Force
Field (CGenFF) program.
53,97
For the proteins, CHARMM 36
was used.
52,54,55
The grid was generated according to the
center of mass of the crystal structure ligand. The grid was
generated with a mesh spacing of 0.25 Å and dimensions of
30.0 Å along each axis. The docking entailed 25 cycles of
simulated MC and MD annealing for 25 differently rotated and
MC torsion-sampled ligand configurations. Settings for non-
covalent and covalent docking were identical.
■RESULTS AND DISCUSSION
Ligand Clustering and the Subsite-Binding Pocket.
To better understand the nature of the 193 Mpro complexes
prior to discussing the docking results, we present analyses of
the ligands and their binding modes. The ligands were
clustered into seven main groups based on their chemical
features by PCA and k-means clustering. The features of each
cluster were normalized, and the average value for each cluster
was calculated (Figure 2). The relative amount of the 193
ligands composing each cluster can be seen in the inserted pie
chart (Figure 2).
For instance, cluster 6 is characterized by 17 ligands of low
molecular weight and high fraction of carbon atoms with sp3
hybridization, while cluster 3 is composed of 78 low-molecular-
weight ligands that are rather rigid and slightly hydrophobic.
Cluster 4 has 15 ligands of high molecular weight, flexible
chains with sp3carbons, and several hydrogen bond donors
and acceptors, while cluster 7 has medium-molecular-weight
ligands that are highly hydrophobic with aromatic rings, yet has
some hydrogen bond donors and acceptors.
Next, we analyzed the binding modes of the ligand clusters
in Mpro (Tables 1 and S1). In Table 1 we present the fraction
of each cluster that is bound in a specific subpocket of the
active site, at the surface, or at the interface between the two
monomers. Note that ligands can bind in more than one
pocket, and hence, the fractions do not add up to unity for
each cluster. Inspection of the binding data clearly shows that
ligands of low molecular weight (clusters 1, 3, 5, and 6) tend to
bind at the surface of the protein (i.e., clusters 3, 5, and 6 are
caught in between crystal units) or at the interface between the
homodimer units (cluster 1). Still many low-molecular-weight
ligands occupy pockets S1 and S1′as these are covalently
attached to C145. Ligands rich in aromatic rings and
correspondingly high log Pvalues (cluster 7) tend to occupy
all pockets more than average, specifically sites S1 and S2. This
is due to favorable π−πinteractions with F140, H163, and
H172 located in S1 or H41 and Y54 in S2. Another important
observation is that ligands more likely to bind to S4 (which is
rarely occupied) belong to clusters 2 and 4, which tend to
include large, flexible molecules that are rich in H-bond donors
and acceptors.
We also clustered the water molecules in all crystal
structures using DBSCAN clustering. Following clustering,
we removed all waters that overlap any bound ligand (Figure
S2). These waters form a set of active site features that can be
included in docking studies (these waters were not included in
the current docking study).
Docking of Ligands in Mpro.We docked all ligands from
193 crystal Mpro structures into their respective crystal protein
structure (Table S4). These crystal structures include ligands
bound noncovalently to the main binding pocket, surface and
dimer interface, as well as covalently attached ligands. In all
results below, we present the success rate of different docking
programs in reproducing the crystal bound poses. For DOCK,
AutoDock Vina, and FRED, the results reflect the noncovalent
complexes only.
In Figure 3A we show the overall success of all programs.
Glide and EnzyDock reproduce the correct crystal structure
pose (rmsd < 2 Å) for over 50% of the structures, with success
rates of 64 and 70%, respectively, while for AutoDock, this rate
falls to 40%. However, in many cases, even if the correct pose
is identified, it is not scored as lowest in energy, and the
success rate reduces to 33% (Glide), AutoDock (30%), and
EnzyDock (35%). The overall success rates of Glide,
AutoDock, and EnzyDock are in part due to the covalent
complexes, whose poses are easier to reproduce than the
noncovalent ones. If we analyze the success rates of identifying
only the covalently bound complexes, Glide, AutoDock, and
EnzyDock identify the correct poses 70, 42, and 71% of the
cases, while the correct pose is also scored as the best one in
38, 36, and 45% of the cases (Figure 3B). For the noncovalent
complexes, Glide, DOCK, AutoDock, AutoDock Vina, FRED,
and EnzyDock identify the correct poses in 55, 61, 37, 29, 46,
and 68% of the cases, respectively, while these are ranked as
the lowest energy poses in 26, 20, 24, 14, 14, and 22% of the
cases, respectively (Figure 3C). Finally, if we remove the
complexes with surface-bound ligands (i.e., ligands bound in
between crystal units), all programs perform significantly better
(Figure 3D). The correct poses are identified (and scored as
best) as follows (%): Glide 74 (39), DOCK 77 (29),
Table 1. Fraction of Ligands in Each Cluster that are
Located in a Specific Active-Site Pocket, on the Surface, or
at the Interface between Monomers in the SARS-CoV-2
Mpro Crystal Structures
S1 S1′S2 S4 surface dimer interface
1 0.74 0.32 0.62 0.29 0.06 0.18
2 0.84 0.44 0.74 0.50 0.04 0.00
3 0.42 0.35 0.16 0.05 0.22 0.06
4 0.87 0.67 0.80 0.87 0.00 0.00
5 0.57 0.18 0.25 0.10 0.27 0.00
6 0.24 0.44 0.12 0.00 0.29 0.06
7 0.82 0.50 0.77 0.36 0.09 0.09
Total 0.57 0.38 0.37 0.22 0.17 0.05
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AutoDock 48 (35), AutoDock Vina 40 (23), FRED 61 (18),
and EnzyDock 80 (35), respectively.
Next, we analyze how the different programs perform as a
function of binding site locations on Mpro.InFigures S3 and 4,
we present box plots of the best rmsd values and the rmsd
values for the lowest scoring pose for noncovalently bound
ligands, respectively. All methods struggle with ligands bound
at the interface between monomers and on the protein surface,
Figure 3. Docking pose prediction success (%) for selected methods for 193 crystal structures of SARS-CoV-2 Mpro. For each column, the upper
part represents the ability of methods to identify poses with rmsd < 2 Å, while the lower part represents ability of methods to identify a pose with
rmsd < 2 Å as the lowest energy pose. (A) All 193 crystal structures, (B) 108 crystal structures with covalently bound ligands, (C) 85 crystal
structures with noncovalently bound ligands, (D) 51 crystal structures with noncovalently bound ligands excluding surface bound ligands.
Figure 4. Distributions of rmsd values (Å) for the lowest-scoring pose at different sites in SARS-CoV-2 Mpro for selected noncovalent docking
methods. The ligands were clustered into groups occupying similar regions, and only clusters with more than seven members are shown.
Figure 5. Distributions of rmsd values (Å) for the lowest-scoring pose at different sites in SARS-CoV-2 Mpro for selected covalent docking methods.
The ligands were clustered into groups occupying similar regions, and only clusters with more than 22 members are shown.
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with Glide and FRED producing the best results. Additionally,
most methods perform better for ligands bound to more than a
single pocket (i.e., S1 + S2), and this trend is particularly clear
for Glide and EnzyDock.
Similarly, we analyze how the different covalent docking
programs perform as a function of binding site locations on
Mpro.InFigures S4 and 5, we present box plots of the best
rmsd values and the rmsd values for the lowest scoring pose for
covalently bound ligands, respectively. Also here, we observe a
general trend, where the docking methods perform better for
ligands occupying more pockets.
■CONCLUSIONS
In wake of the growing threat emerging from the SARS-CoV-2
pandemic, the modeling community has rushed to study a
variety of potential pharmaceutical targets. One of these
targets, Mpro, is particularly attractive as this enzyme has no
human analogue and is conserved among coronaviruses. A
large number of studies have addressed ligand docking and
virtual screening of ligand libraries against Mpro in search of
promising leads. A prerequisite for such studies is the ability of
the docking programs to correctly identify and score ligand
poses. Due to the intense efforts by the scientific community,
there is already a wealth of structural biology information on
Mpro, hence enabling comparative studies of different docking
approaches against this target. To date, the available crystal
structures of Mpro include ligands bound covalently and
noncovalently to the main catalytic site, surface, and in
between the two monomers. Here, we studied several leading
docking codes, namely, Glide, DOCK, AutoDock, AutoDock
Vina, FRED, and EnzyDock, and evaluate their ability to
correctly reproduce and score the crystal structure ligand
configuration for 193 Mpro crystal structures. None of the
codes are able to correctly identify and score the crystal
structure in more than 26% of the cases for noncovalently
bound ligands (Glide top performer), whereas for covalently
bound ligands, the top score was 45% (EnzyDock best
performer). Additionally, a general trend, where several of the
docking methods (e.g., Glide and EnzyDock) perform better
for larger, bulkier ligands occupying more than a single pocket,
is observed. All docking methods struggle with prediction of
small ligands. In the original crystal structures, many of the
smaller ligands are surrounded by numerous explicit water
molecules, dimethyl sulfoxide molecules, and Cl−ions that
were removed prior to docking. Thus, these redocking trends
may be ascribed to difficulty in accurately scoring docking
poses, where a delicate balance between intra- and
intermolecular terms and solvation terms must be stricken.
In conclusion, the current results suggest that one should
perform docking studies and virtual screening campaigns of
Mpro with care and that more comprehensive strategies might
be necessary. Such strategies might include initial virtual
screening (e.g., using FRED or AutoDock Vina) or docking
(e.g., Glide or EnzyDock), followed by more rigorous ligand
free energy binding calculations
98,99
and in-depth QM/MM
studies.
20,24,26,28,29
Inclusion of conserved water molecules, as
identified in this study, may also be of help in guiding the
docking process. Indeed, MD simulations have pointed to
several water molecules, as important for Mpro.
11,18,20,24,26,29,77
■ASSOCIATED CONTENT
*
sıSupporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acs.jcim.1c00263.
Chemical structures of all docked ligands, conserved
water molecules, additional docking results, all rmsd
data, and active-site pocket occupation (PDF)
■AUTHOR INFORMATION
Corresponding Author
Dan T. Major −Department of Chemistry and Institute for
Nanotechnology &Advanced Materials, Bar-Ilan University,
Ramat-Gan 52900, Israel; orcid.org/0000-0002-9231-
0676; Email: majort@biu.ac.il
Authors
Shani Zev −Department of Chemistry and Institute for
Nanotechnology &Advanced Materials, Bar-Ilan University,
Ramat-Gan 52900, Israel
Keren Raz −Department of Chemistry and Institute for
Nanotechnology &Advanced Materials, Bar-Ilan University,
Ramat-Gan 52900, Israel
Renana Schwartz −Department of Chemistry and Institute for
Nanotechnology &Advanced Materials, Bar-Ilan University,
Ramat-Gan 52900, Israel
Reem Tarabeh −Department of Chemistry and Institute for
Nanotechnology &Advanced Materials, Bar-Ilan University,
Ramat-Gan 52900, Israel
Prashant Kumar Gupta −Department of Chemistry and
Institute for Nanotechnology &Advanced Materials, Bar-
Ilan University, Ramat-Gan 52900, Israel; orcid.org/
0000-0002-4792-7538
Complete contact information is available at:
https://pubs.acs.org/10.1021/acs.jcim.1c00263
Author Contributions
S.Z., K.R., and P.K.G. contributed equally. The docking
simulations and system preparations were performed by all
authors. The manuscript was written through contributions of
all authors. All authors have given approval to the final version
of the manuscript.
Notes
The authors declare no competing financial interest.
All preprepared 193 Mpro systems and accompanying Python
and CHARMM scripts are available at https://github.com/
shanizev/Benchmarking-SARS-CoV-2.EnzyDockisfreely
available for noncommercial use on https://github.com/
majordt/EnzyDock.
■ACKNOWLEDGMENTS
This work was supported by the Israel Ministry of Science,
Technology and Space (grant 3-16310) and Israeli Science
Foundation (grant #1683/18).
■ABBREVIATIONS
SARS, severe acute respiratory syndrome; SARS-CoV-2, SARS
coronavirus 2; MERS, Middle East respiratory syndrome; MD,
molecular dynamics; MC, Monte Carlo
Journal of Chemical Information and Modeling pubs.acs.org/jcim Article
https://doi.org/10.1021/acs.jcim.1c00263
J. Chem. Inf. Model. 2021, 61, 2957−2966
2962
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