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Cite this: Mol. Syst. Des. Eng., 2022,
7, 123
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Thermodynamic and structural insights into the
repurposing of drugs that bind to SARS-CoV-2
main protease
Shunzhou Wan, a Agastya P. Bhati,
Dario Alfè bc and Peter V. Coveney
a
Alexander D. Wade,
*ad
a
Although researchers have been working tirelessly since the COVID-19 outbreak, so far only three drugs –
remdesivir, ronapreve and molnupiravir – have been approved for use in some countries which directly
target the SARS-CoV-2 virus. Given the slow pace and substantial costs of new drug discovery and
development, together with the urgency of the matter, repurposing of existing drugs for the ongoing
disease is an attractive proposition. In a recent study, a high-throughput X-ray crystallographic screen was
performed for a selection of drugs which have been approved or are in clinical trials. Thirty-seven
compounds have been identified from drug libraries all of which bind to the SARS-CoV-2 main protease
(3CLpro). In the current study, we use molecular dynamics simulation and an ensemble-based free energy
approach, namely, enhanced sampling of molecular dynamics with approximation of continuum solvent
Received 28th August 2021,
Accepted 15th November 2021
DOI: 10.1039/d1me00124h
rsc.li/molecular-engineering
(ESMACS), to investigate a subset of the aforementioned compounds. The drugs studied here are highly
diverse, interacting with different binding sites and/or subsites of 3CLpro. The predicted free energies are
compared with experimental results wherever they are available and they are found to be in excellent
agreement. Our study also provides detailed energetic insights into the nature of the associated drug–
protein binding, in turn shedding light on the design and discovery of potential drugs.
Design, System, Application
The COVID-19 pandemic has led to a rush to repurpose existing drugs which can treat the disease or arrest the spread of the virus. Drug repurposing can
speed up the traditional process of drug discovery because the drugs have already been proven to be safe in humans. In the current study, we use molecular
dynamics simulation and an ensemble-based free energy approach to investigate the interactions of a set of existing drugs with the main protease of the
SARS-CoV-2 virus. The drug–residue interaction profile elucidates the amino acids crucial to the drug binding while the detailed energetic insights into the
nature of binding shed light on possible new routes to future rational drug design.
Introduction
Centre for Computational Science, Department of Chemistry, University College
London, UK. E-mail: p.v.coveney@ucl.ac.uk
b
Department of Earth Sciences, London Centre for Nanotechnology and Thomas
Young Centre at University College London, University College London, UK
c
Dipartimento di Fisica Ettore Pancini, Università di Napoli Federico II, Italy
d
Institute for Informatics, Faculty of Science, University of Amsterdam, The
Netherlands
and molnupiravir are broad-spectrum antiviral drugs
targeting the RNA-dependent RNA polymerase (RdRp) of
viruses. Ronapreve is an antibody cocktail containing two
virus-neutralising antibodies, designed to target the spike
protein of the coronavirus and to stop it attaching to the
human angiotensin-converting enzyme 2 (ACE2). Studies have
shown that remdesivir may only provide a modest benefit to
patients with little to no effect on hospitalized patients with
COVID-19.1 As a combination of two antibodies, ronapreve
needs to be administered either by injection or infusion as
quickly as possible after the first symptoms of illness.
Molnupiravir has been approved by the UK medicines
regulator for use in Covid-19 patients, although concerns
remain on its mutagenic potential in human cells.
In general, small-molecule drugs have some obvious
advantages over biologics, including their oral bioavailability,
pharmacological activity, stability, permeability, etc. Relentless
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Mol. Syst. Des. Eng., 2022, 7, 123–131 | 123
From the start of the COVID-19 fight, scientists have been
scrambling to find drugs that can treat the disease and
perhaps even arrest the spread of the virus. Although many
medications have been clinically tested for COVID-19
treatment, there are only three drugs – remdesivir, ronapreve
and molnupiravir – which directly target the SARS-CoV-2
virus and have been approved or authorized for emergency
use. Although working in entirely different ways, remdesivir
a
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research efforts2,3 have led to some progress to find novel smallmolecule drugs, or to repurpose existing small-molecule drugs
for the treatment of the ongoing COVID-19 disease. An exciting
global collaboration, called the COVID Moonshot, has come
together for the discovery of new, urgent drug treatment for
COVID-19.4 Despite this encouraging development, the
discovery and development of new drugs are still associated
with a slow pace and substantial costs. Repurposing of existing
drugs for the ongoing disease therefore represents an attractive
proposition as the safety of these drugs have been demonstrated
in clinical trials and clinical applications.
The COVID-19 drug repurposing research can be grouped
into four categories: clinical trials, in vivo cell experiments,
in vitro protein-binding experiments, and in silico computational
studies. As of 23 August 2021, there are 1822 registered COVID19 clinical studies listed at https://clinicaltrials.gov/ct2/covid_
view which have at least one drug intervention. There is a total
of 615 drugs in these trials, many of which were initially
developed for other diseases. In vivo and in vitro studies have
uncovered some interesting drugs but caution must be
exercised as the apparent antiviral activities are frequently
caused by the drugs interrupting fundamental cellular processes
rather than killing the virus or preventing its entry and
duplication.5 In silico computational research efforts are
productive for the structures and interactions of the key
proteins, especially at the earliest stages when no structures
have yet been reported from experimental studies. Despite an
unprecedented number of studies having been published in the
last one and half years on computer-aided drug discovery,6 only
one drug has arisen from computational studies7 – baricitinib –
which has been approved for emergency use to treat COVID-19,
in combination with remdesivir. Baricitinib is a repurposed
medication: it is a kinase inhibitor originally designed to treat
rheumatoid arthritis. As a broad-spectrum antiviral medication,
remdesivir was originally developed to treat hepatitis C, and
subsequently investigated for Ebola. Clinical trial has shown
that the combination of baricitinib and remdesivir reduces the
recovery time for hospitalized COVID-19 patients, especially for
those requiring oxygen or ventilation.8 But neither alone, nor in
combination, has proved to be a curative treatment for the
disease.8 The four groups of repurposing studies are not
independent. Computational studies, for example, require
routine validation from experiments and clinics.
The main protease of SARS-CoV-2, 3CLpro or Mpro, is a key
enzyme of the coronaviruses. It plays a pivotal role in processing
the polyproteins that are translated from the viral RNA.9 The
enzyme has become an attractive drug target because inhibiting
its activity would block viral replication. X-ray structures have
identified 22 different sites to which small molecules or
molecular fragments can bind.10 However, most of the fragments
do not show any antiviral activity. The most interesting small
molecules in these x-ray structures are those screened from
drug libraries in a recent study.11 A high-throughput X-ray
crystallographic screening has been performed for two
repurposing drug libraries against 3CLpro. The libraries contain
5953 drugs which have been approved or are in clinical trials,
from which thirty-seven drugs have been identified which bind
to 3CLpro. The effective concentrations of the drugs have been
measured in a cell-based assay, at which SARS-CoV-2 infectious
particles are reduced by 50% (EC50) (Table 1). Some of these
drugs are considered to have antiviral activities, which show
≥100-fold reduction in infectious particles and have a much
higher cytotoxic concentration than EC50 values.11 The X-ray
structures of these drugs show that they bind at the substratebinding site or one of the two allosteric sites identified11 (Fig. 1).
In the current study, we use molecular dynamics simulation
and an ensemble-based free energy approach, namely,
enhanced sampling of molecular dynamics with approximation
of continuum solvent (ESMACS),12,13 to investigate a subset of
the aforementioned drugs. For the drug–protein complexes
studied here, the binding sites of the protein are well
structured, and the binding modes are resolved from
Table 1 Compounds bind with 3CLpro noncovalently at the substrate-binding site and the two allosteric sites
Hit
Compound name
Substrate-binding site
#1
Adrafinil
#11
Fusidic acid
#18
LSN-2463359
#20
MUT056399
#27
SEN1269
#34
Tretazicar
#35
Triglycidyl isocyanurateb
#36
UNC-2327
Allosteric site I
#15
Ifenprodil
#22
PD-168568
#23
Pelitinib
#26
RS-102895
#32
Tofogliflozin
Allosteric site II
#3
AT7519
a
c
b
PDBea
Tested in antiviral assay
pdb
EC50c (uM)
RNW
FUA
S8B
RQN
S1W
CB1
RV8
RV5
+
+
−
+
−
+
+
+
7ANS
7A1U
7AWU
7AP6
7AVD
7AK4
7AQJ
7AQE
—
—
—
38.24
—
—
30.02
—
QEL
RMZ
93J
R6Q
RT2
+
+
+
+
+
7AQI
7AMJ
7AXM
7ABU
7APH
46.86
—
1.25
19.8
—
LZE
+
7AGA
25.16
pro
PDBe ligand code. The drug binds to 3CL
covalently and non-covalently. The non-covalent binding mode is used in the current study.
EC50 values from literature.10 “—” means no antiviral activity detected, or no cell assay performed.
124 | Mol. Syst. Des. Eng., 2022, 7, 123–131
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Fig. 1 Chemical structures of 14 repurposing drugs to SARS-CoV-2
main protease. The drugs are grouped according to the sites they bind
at: the substrate-binding site, the allosteric site I, and the allosteric site
II (see Table 1). The drugs are also shown in chemical representation
bound to 3CLpro (shown in cartoon) at the three binding sites. The
three domains (I, II and III) of 3CLpro are shown in light blue, blue and
green, respectively, along with some loops and links (white).
crystallography experiments. It is likely that a reasonable
prediction can be achieved, although the relatively large size of
the substrate-binding site (Fig. 2) may pose a challenge for the
conformational sampling and hence the convergence of the free
energy predictions. In addition, per-residue free energy
decomposition14 and close contacts between drugs and the
protein have been performed to elucidate amino acids crucial to
the binding and to understand the differential binding modes
of drugs.
Methods
Fourteen compounds were selected, all of which have X-ray
structures available (Table 1) and non-covalently bind at one
of the three binding sites: the substrate-binding site, and the
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Paper
allosteric sites I and II (Fig. 1). Two sets of ESMACS
simulations were performed as follows: set-1 using individual
X-ray structures for each of the compounds (see the PDB
codes in Table 1), and set-2 using the same protein structure
for all of the compounds. Set-2 simulation were performed
because individual X-ray structures were not commonly
available for all compounds in most drug development
projects. In such projects, only one or limited number of
protein structureIJs) are available, to which the compounds
are docked. Different initial structures have been shown to
have a significant effect on the predicted binding free
energies, especially when the timescale of simulations is
short.15 In set-2, the structure 7AQE was used as it had the
highest resolution among the PDB structures listed in
Table 1. The backbone atoms of binding site residues,
defined as those within 3 Å of any drug for a given binding
site, were used for alignment of different protein structures.
The structure 7AQE had drug RV5 bound at the substratebinding site. For all other drugs binding at the same site,
RV5 was replaced by them. No adjustment was made to the
residues at the substrate-binding site for these drugs except
S1W. The drug S1W had an obvious clash with the side chain
of MET49, which was adjusted to accommodate S1W. For
drugs binding at the allosteric sites, RV5 was removed and
each drug was inserted into its binding site after alignment
of the corresponding protein structure. The orientation of the
sidechain GLN256 in 7AQE was also adjusted to better
accommodate drugs at allosteric site I. All crystallographic
water molecules were retained unless they overlapped with
the inserted drug in set-2 models.
Drug parameterizations were created using the general
Amber force field 2 (GAFF2).16 All drugs were electrostatically
neutral except FUA which has a net charge of −1e. The AM1BCC partial charges were assigned using the Antechamber
component of the AmberTools package.16 The Amber ff14SB
force field was used for the protein, and TIP3P for water
molecules. The protonation states of the protein residues
were assigned using the reduce module of AmberTools. All
systems were solvated in orthorhombic water boxes with a
minimum extension from the protein of 14 Å.
The binding affinity calculator (BAC)17 software tool was
used to perform ESMACS studies. We employed an ESMACS
protocol, which consisted of performing 25 replicas for a
total of 10 ns production runs each. The molecular dynamics
simulations were conducted using the package NAMD 2.14
(ref. 18) for each of the molecular systems studied. The
protocol generates precise and reliable free energy
predictions, and has been adequately validated for a diverse
set of protein systems.13,19–23 The systems were minimized
with all heavy protein atoms restrained at their initial
positions, with restraining force constants related to their
β-factors in X-ray structures. Initial velocities were then
generated independently for each replica from a Maxwell–
Boltzmann distribution at 50 K. The systems were virtually
heated to 300 K over 60 ps with the NVT ensemble, followed
by 2 ns equilibration with the NPT ensemble, during which
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Fig. 2 Drugs bind at different subsites within the substrate-binding site, which are labelled for one of the drugs (RNW). The conformational
changes of the substrate-binding site are induced upon bindings of different drugs. The structures are generated from PDB IDs 7ANS, 7A1U, 7AWU,
7AP6, 7AVD, 7AK4, 7AQJ and 7AQE (Table 1).
the restraints on heavy atoms were gradually removed.
Finally, 10 ns production simulations were executed with
snapshots derived for analysis every 20 ps. A 2 fs time step
was used for all MD simulations. NPT simulations were
performed with pressure and temperature maintained at 1
bar and 300 K, respectively, during the equilibration and
production runs. The simulations were performed on
ARCHER2 (https://www.archer2.ac.uk/) and SuperMUC-NG
(https://doku.lrz.de/display/PUBLIC/SuperMUC-NG).
Results
The two sets of ESMACS simulations produce very similar
free energy predictions for most of the drugs studied
(Table 2). Only two drugs, RQN and FUA, have differences
larger than 2 kcal mol−1 between the two predictions.
Simulations in set-1 have been initiated from their individual
x-ray structures, and are expected to be more reliable than
those in set-2. Hereafter, we will focus on the results from
set-1. The set-2 results will be discussed only to address the
potential issues when a common protein structure is used for
all of the compounds.
Binding free energy ranking
For the drugs at the substrate-binding site, cell assays detect
two drugs with antiviral activity, RQN and RV8. From
simulations, RQN has the most favourable binding free
energy, while RV8 is also one of the drugs with the most
negative binding free energies. In cell assay, RV8 has slightly
higher antiviral activity than RQN, as indicated by a lower
EC50 concentration. ESMACS calculations observe a reversed
126 | Mol. Syst. Des. Eng., 2022, 7, 123–131
order for their binding affinities. It should be noted that
X-ray structures reveal both covalent and noncovalent
binding modes for RV8. The antiviral activity may be
attributable to both of the binding modes. In ESMACS
simulations, however, only the noncovalent binding mode is
Table 2 Comparison of the predicted binding free energies (kcal mol−1)
and the experimentally measured EC50 values (μM). The predictions were
made from two sets of simulations, differing in the protein structures
used. Bootstrapped errors, given to 67% confidence, are provided for the
predicted energies. The drugs are ordered according to the predicted free
energies from set-1 simulations
Ligand
ΔGset-1
ESMACS
Substrate-binding site
RQN
−22.16
CB1
−21.19
RV8
−20.70
S1W
−20.69
FUA
−18.07
RV5
−17.77
RNW
−13.20
S8B
−12.38
Allosteric site I
93J
−19.86
RT2
−18.36
R6Q
−15.33
RMZ
−13.55
QEL
−12.18
Allosteric site II
LZE
−18.36
a
ΔGset-2
ESMACS
EC50a
± 0.35
± 0.48
± 0.86
± 0.30
± 0.61
± 0.91
± 0.44
± 0.26
−15.98
−21.09
−21.29
−18.77
−12.98
−19.07
−13.57
−11.69
± 0.64
± 0.52
± 0.75
± 0.90
± 0.94
± 0.49
± 0.35
± 0.39
38.24
+
30.02
−
+
+
+
−
± 0.18
± 0.43
± 0.46
± 0.39
± 0.48
−19.78
−18.59
−14.93
−14.05
−11.78
± 0.15
± 0.40
± 0.41
± 0.45
± 0.53
1.25
+
19.8
+
46.86
−19.60 ± 0.30
25.16
± 0.32
10
EC50 data from literature. “+”: cell assay performed but no
antiviral activity detected at the highest concentration (100 μM)
tested; “−”: no cell assay performed.
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studied, which may contribute to the differences between the
experiment observations and the simulations.
For the drugs at the allosteric site I, excellent agreement is
obtained between the two sets of simulations. The
simulations generate not only the same ranking, but the
same binding free energies, within error bars, for all of the
compounds binding at this site (Table 2). The calculated
binding free energies also agree with the experimental
measurements for a subset of these drugs for which EC50
values are detected. For LZE at the allosteric site II there are
no other drugs to compare with at the same binding site, but
its binding free energy is very favourable. This drug has
antiviral activity observed in the cell assays.11
Per-residue contributions
The drugs studied here are highly diverse, interacting with
different binding sites (Fig. 1), and different subsites for these
at the substrate-binding site (Fig. 2). The subsites are defined
as the regions where the amino acid residues of the
polypeptide substrate bind. 3CLpro, like many other proteases,
has an extended substrate-binding site which is spacious for
common small-molecule drugs. The drugs interact with
different subsites; residues at the binding site are expected to
contribute differently to the bindings of the drugs.
Paper
To quantify the energetic contribution of each amino acid
to the bindings, we have performed a per-residue
decomposition analysis14 for the drugs at the substrate-binding
site. It is observed that the residues contributing the most to
the bindings are clustered with residue numbers between 25
and 27, 41 and 50, 140 and 145, 163 and 173, and 187 and 192
(Fig. 3). While most of these residues contribute to the
bindings in a favourable way, some residues show
unfavourable contributions to the binding energies. Although
there is a total of 37 residues having a contribution of |ΔG| >
0.1 kcal mol−1, only one residue, MET49, is universally
presented for all of the drugs at the substrate-binding site.
Another residue, MET165, appears for 7 out of 8 drugs, and is
merely missed for S8B with a contribution of −0.09 kcal mol−1.
23 of the residues have contributions (|ΔG| > 0.1 kcal mol−1)
only for one or two drugs. The contributions from the same
residue also vary significantly for the binding of different
drugs. HIS163, for example, provides the most favourable
contribution for one of the drugs, RQN, with an energy of
−1.96 kcal mol−1. Its contributions for three other drugs, S1W,
RV5 and CB1 are, however, negligible. It should be noted that
the residues do not interact with the drugs independently.
HIS163 contributes favourably for the binding of all eight
drugs, while its immediately adjacent residue, HIS164,
contributes unfavourably for 6 out of 8 drugs studied here.
Fig. 3 Decomposition of the binding free energy on a per-residue basis for the drugs in the substrate-binding site. The major contribution toward
the binding free energy comes from a few clusters of residues, indicated by the negative residue–drug interaction energies. The error bars
represent the variations of the energies from individual replicas. The residues with contributions between −0.1 and 0.1 kcal mol−1 are not shown
for reasons of clarity.
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In addition to the size and accommodation capacity of the
subsites, the occurrence of mutations at the binding site
should also be considered when designing new drugs or
modifying existing drugs. Correlated mutation analysis (CMA)
has revealed some key residues which contribute significantly
to the protein stability.24 These residues are unlikely to
mutate and thus can be considered as key anchoring residues
for drug design. Some of these anchoring residues24 overlap
with the ones which contribute significantly to the binding
from the per-residue analyses. Residue G143, for example, is
one of the anchoring residues identified from CMA study; it
also contributes significantly to the binding energies of RV8
(−1.09 kcal mol−1) and FUA (−0.49 kcal mol−1). These
interactions of drugs with the conserved residues are likely to
be maintained within potential 3CLpro variability, and hence
reduce the probability of resistance emergence. On the other
hand, drug resistance may be present when mutations occur
in the residues important to the drug–protein interactions.
Mutation of residue N142, for example, has been identified
from a sequencing study.25 The residue contributes
significantly to the binding of S8B, RV8 and FUA, with
binding free energy contributions of −0.87, −0.90 and −0.97
kcal mol−1, respectively. The mutation may confer resistance
against these drugs if the drug–residue interactions become
less favourable. The interference of the neighbouring
residues, however, would complicate the estimations of their
individual contributions to drug binding affinities. This
problem can be overcome by calculating the overall
contributions instead from the neighbouring residues as a
whole.
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Importance of the initial structures
Although most of the predicted binding free energies agree
well from the two sets of simulations, obvious differences
exist for two of the drugs: RQN and FUA (Table 2). The
protein structure (PDB id: 7AQE) used in the set-2
simulations has high overall similarity to others, with a
RMSD of main chain atoms less than 0.4 Å for all of the
drugs except S1W. A close inspection of the binding site
residues, however, reveals substantial differences. The
differences can have a large impact on the propensity for and
stability of drug binding. The orientation of MET49 in 7AQE,
for example, reduces the size of the S3′ subsite (Fig. 2),
making the binding site less able to accommodate drugs like
QRN to which the fully appearance of S3′ subsite is crucial.
To check the stability of drug binding, the close contacts
of the drugs with the protein are monitored (Fig. 4). A close
contact is defined when the distance is less than 4 Å between
two heavy atoms arising within drug and protein. The
number of contacts is a good qualitative indicator of the
predicted binding free energies.26 It has a good correlation
with the binding free energies from the ESMACS approach,
with a correlation coefficient of −0.78 (Fig. 5), indicating that
more contacts lead to more negative free energies.
In set-1 simulations, 5 out of 8 drugs have less contacts
during the simulations than those identified in the initial
X-ray structures, and 2 drugs have more contacts. Only one
drug, RQN, maintains roughly the same numbers of contacts
as these in its X-ray structure. Most of the contacts from the
X-ray structures are not maintained in the simulations. The
Fig. 4 Number of contacts between drugs at the substrate-binding site and the protein for the simulations starting from their individual X-ray
structures (a), or using the same protein structure (b). A contact is defined when the distance of heavy atoms between the drugs and the protein is
less than 4 Å. The total contacts observed during the simulations are shown in blue, whereas a subset of these that overlap with those observed in
the X-ray structures are shown in grey. The red lines indicate the numbers of contacts found in the X-ray structures.
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Fig. 5 Correlation between the number of contacts and the binding
free energies predicted from ESMACS approach. The error bars
represent the standard deviations of the properties from ensemble
simulations.
lost contacts are largely compensated by other pairs of drug
and protein atoms which are brought closer during the
simulations. In set-2 simulations, all drugs except S1W have
similar or less contacts comparing with those in set-1
simulations. This is not unexpected as conformational
differences are usually induced when small molecules bind
to a protein. A single protein structure is not optimal for the
binding of every drug. Consequently, the results from set-2
simulations are less reliable than these from set-1. Some
drugs may even drift away from the binding sites identified
in the crystallography experiments. We are currently
performing very long time scale MD simulations in which
binding and unbinding events are investigated. The drug
FUA in set-2, for example, is observed to either bind at
different binding subsites or is completely unbound in the
ensemble simulations, evidenced by its contact distribution
extending to 0 (Fig. 4). This is the reason for its less
favourable binding free energy predicted in set-2 simulations
(Table 2).
Conclusions
Using the ESMACS protocol, we have computed the free
energies of a series of drugs binding to the SARS-CoV-2 main
protease (3CLpro). The drugs are selected from an X-ray
screening study, in which the three-dimensional structures
have been determined for 3CLpro complexed with a set of
potentially repurposable drugs, and the antiviral activities
been measured for some of them in a cell-based assay. The
drugs studied here are highly diverse in their structural and
chemical properties, and in their binding modes in the
binding sites of the protein. For the drugs with antiviral
activity detected in cell assay,11 the rankings of binding free
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Paper
energies from ESMACS approach are in excellent agreement
with the experimentally determined drug potencies.
The per-residue energy decomposition provides some
favourable features of the binding sites, which are important
for the building of an energy-based pharmacophore model.
The analysis suggests the amino acids crucial for the binding
of drugs at the 3CLpro binding sites. The combination of
energy-based and structure-based pharmacophore models
could provide an improved virtual screening for the initial
selection of promising compounds.
Although the X-ray structures have a high degree of
similarity for the particular set of drugs studied here, the
difference in predicted binding free energies for the drugs
RQN and FUA when using individual X-ray structures
compared to using repurposed X-ray structures from similar
protein ligand complexes, highlights the importance of the
local conformations at the binding site. The orientations of
some side chains need to be adjusted to better accommodate
different drugs. While treating the protein as a rigid entity is
still common for most docking studies, induced fit docking
approaches have been attempted in which an ensemble of
protein conformations is used. The conformations are
collected from multiple experiments or more commonly are
generated by MD simulations. The close contacts between
the drugs and the proteins in the simulations differ
significantly from those present in the x-ray structures. Our
study provides detailed energetic insight into the nature of
drug–protein binding, which may be used to shed light on
the design and discovery of potential drugs.
Data accessibility
The molecular models and force parameters are available at https://
doi.org/10.23728/b2share.1c42a67a73e9424b8192ba65c81077e1
Conflicts of interest
There are no conflicts to declare.
Acknowledgements
We are grateful for funding from the UK MRC Medical
Bioinformatics project (grant no. MR/L016311/1), the EPSRC
funded UK Consortium on Mesoscale Engineering Sciences
(UKCOMES grant no. EP/L00030X/1), the European
Commission for EU H2020 CompBioMed2 Centre of
Excellence (grant no. 823712) and EU H2020 EXDCI-2 project
(grant no. 800957). We acknowledge the Gauss Centre for
Supercomputing for providing computing time on the
supercomputer SuperMUC-NG (https://doku.lrz.de/display/
PUBLIC/SuperMUC-NG) at Leibniz Supercomputing Centre
under project COVID-19-SNG1 and the very able assistance of
its scientific support staff. We also made use of the ARCHER2
UK National Supercomputing Service (http://www.archer2.ac.
uk). Access to ARCHER2 was provided through the UKCOMES
grant.
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