Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. The virus is highly prevalent in the Middle East and Africa particularly Egypt with more than 90% of infections due to genotype 4. Nonstructural (NS5B) viral proteins have emerged as an attractive target for HCV antivirals discovery. A potent class of inhibitors having benzisothiazole dioxide scaffold has been identified on this target, however they were mainly active on genotype 1 while exhibiting much lowered activity on other genotypes due to the high degree of mutation of its binding site. Based on this fact, we employed a novel strategy to optimize this class on genotype 4. This strategy depends on using a refined ligand-steered homological model of this genotype to study the mutation binding energies of the binding site amino acid residues, the essential features for interaction and provide a structure-based pharmacophore model that can aid optimization. This model was applied on a focused library which was generated using a reaction-driven scaffold-hopping strategy. The hits retrieved were subjected to Enovo pipeline pilot optimization workflow that employs R-group enumeration, core-constrained protein docking using modified CDOCKER and finally ranking of poses using an accurate molecular mechanics generalized Born with surface area method.
Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. The virus is highly prevalent in the Middle East and Africa particularly Egypt with more than 90% of infections due to genotype 4. Nonstructural (NS5B) viral proteins have emerged as an attractive target for HCV antivirals discovery. A potent class of inhibitors having benzisothiazole dioxide scaffold has been identified on this target, however they were mainly active on genotype 1 while exhibiting much lowered activity on other genotypes due to the high degree of mutation of its binding site. Based on this fact, we employed a novel strategy to optimize this class on genotype 4. This strategy depends on using a refined ligand-steered homological model of this genotype to study the mutation binding energies of the binding site amino acid residues, the essential features for interaction and provide a structure-based pharmacophore model that can aid optimization. This model was applied on a focused library which was generated using a reaction-driven scaffold-hopping strategy. The hits retrieved were subjected to Enovo pipeline pilot optimization workflow that employs R-group enumeration, core-constrained protein docking using modified CDOCKER and finally ranking of poses using an accurate molecular mechanics generalized Born with surface area method.
Hepatitis C virus genotype 4 (HCV-4) is the most common
variant of the hepatitis C virus (HCV) in the Middle East and
Africa, particularly Egypt. This region has the highest
prevalence of HCV worldwide, with more than 90% of
infections due to genotype 4. HCV-4 has recently spread in
several Western countries, particularly in Europe, due to
variations in population structure, immigration, and routes of
transmission. Employing HCV proteins as targets, directly
acting antiviral agents have been identified and collectively
described as ‘specifically targeted antiviral therapy for HCV’
(STAT-C) [1–
3]. Among the nonstructural proteins, NS3–4A
protease, NS5B polymerase, NS3 helicase and NS5A have been
the object of intense research efforts both by academia and
pharmaceutical companies. NS5B RNA-dependent RNA
polymerase is recognized as a key target for therapeutic
intervention mainly because it is not present in mammalian
cells and offers a wide range of possibilities for the discovery of
new molecular entities as anti-HCV agents [4–
7]. Mechanistic
and structural studies of this enzyme have revealed the
existence of multiple allosteric binding sites, and in particular
two thumb sites (thumb I and II) and three palm pockets (palm
I, II and III) have been identified to date. According to the target
site, the different inhibitors will be referred to as palm site I
NNIs (PSI-NNIs), palm site II NNIs (PSII-NNIs), palm site III
NNIs (PSIII-NNIs), thumb site I NNIs (TSI NNIs) and thumb
site II NNIs (TSII-NNIs) [8,
9]. Out of these different allosteric
sites and their corresponding inhibitors, we focused this study
on palm I site and particularly Benzoisothiazoles dioxide as one
of the main Palm I-NNI. The palm I site in genotype 4 shows
high degree of mutation with respect to the other genotypes.
This has an impact on the activity of the inhibitors of this site
where it decreases drastically. This triggered us to study the
impact of these mutations on binding by constructing a
validated homological model for this genotype and analyzing
the ligand-protein interactions. The main aim of this analysis
was to optimize the Benzoisothiazoles dioxide on this specific
genotype. On the other hand, from a ligand design perspective
we attempted to modify this class of ligands such that it has a
high diversification capability and high synthetic feasibility that
can enable us to optimize it rapidly within the binding site.
Thus, we decided to use a reaction-driven scaffold-hopping
procedure to achieve this aim.
Methodology:
The protocol consists of two workflows that intersect at some
point where the first depends on developing a ligand-protein
complex that can be used to study the essential interactions
criteria, calculating mutation binding energies and generating a
structure-based pharmacophore to filter ligands while the
second is ligand dependent where it is used to generate a
focused library of synthetically feasible ligands against this
target. They intersect at the point where the pharmacophore
(first workflow) is used to filter the focused library (second
workflow) to handle the hits to an optimization protocol (Enovo)
[10]. This is illustrated in
(Figure 1).
Figure 1
The workflow used to optimize the benzisothiazole
dioxide activity of NS5b polymerase of genotype 4
First workflow:
The aim of this workflow is to provide a refined homological
model of genotype 4 to be used for structure-based
pharmacophore screening and docking of the virtual library
generated for optimization aim.
Ligand-steered Homological modeling:
Homological model was constructed using Modeler
[11] in
Discovery Studio. Uniprot was searched for HCV polymerase
NS5b sequence for genotype 4a.It was found under accession
code O39929 [12]. According to the sequence annotation, the
RNA-directed RNA polymerase is represented by the sequence
from 2418 to 3008 [13]. Uniprot sequence was blasted using
Discovery Studio against PDB_nr95 database. This was done in
order to obtain template structure for homology modeling
where 3D5M was chosen as template with 77% identity. The
sequence alignment was done using align sequence protocol as
shown in (Figure 2). After that, the sequence alignment with the
template was further used to build a homology model using
Modeler while adjusting the settings to high optimization and
copying Water molecules and the ligand from the template to
the model. 5 models were created. From which, we have chosen
the first model that was further minimized using ligandX
algorithm, simulated using molecular dynamics protocol in
MOE to investigate essential features.
Figure 2
Alignment of the sequence of genotype 4 with that of
template (3D5M).Red boxes show the amino acids of the
binding site.
2D-interaction analysis:
Based on the refined complex, 2D interaction analysis was
carried out using MOE 2010 ligand interaction generation. This
was very useful to study the actual interactions that are
responsible for the activity.
Mutation binding energy studies:
In order to show the impact of the natural polymorphism in
genotype 4, we calculated the mutation binding energies of the
variable amino acid residues in the Palm I binding site. The
difference in binding due to mutation was calculated by finding
difference between the free energy of binding in case of
mutation and no mutation. The free energy of binding was
calculated using CHARMm force field according to this
equation: ΔG =αΔGFF +βTΔS where ΔS was calculated using
Abagyan and tortov amino acid chain entropy scale with
correction according to the side chain solvent accessibility.
Calculations were carried out by Accelrys Discovery Studio 3.0
using “calculate mutation binding energy protocol”. The
reported energy is the sum of weighed terms: electrostatic
(0.45), van-der Walls (0.45) and entropy (0.8). Generalized Born
implicit solvent model was used with dielectric constant of 80.
Structure-based pharmacophore:
Using the refined complex of the genotype 4 with
benzoisothiazole dioxide, a structure-based pharmacophore
was create using the technique that was developed by Wolber
and Langer for screening of new compounds instead of the
computationally expensive docking[14]. The technique was
implemented already in ligandscout software [15]. This
algorithm extracts information according to certain rules
depending on nearby contact residues. It was used here to
rapidly filter ligands of the virtual library used for
optimization.
Second workflow:
The aim of this workflow is to generate the virtual library that is
focused to this target such that it will be screened using the first
workflow.
Reaction-driven scaffold-hopping:
Due to the limited SAR expansion capability of the existing
scaffold that can hinder rapid probing of the effect of various
substituents on activity, we carried out a reaction-driven
scaffold hopping. Initially, the ligand co-crystallized with the
protein in 3D5M complex was used as a starting point for
retrosynthetic-disconnection [16] into two scaffolds: A and B. A
query was built according to scaffold A as shown in (Figure 5).
This query was used for substructure search in Scifinder such
that the retrieved hits synthesis can be done in not more than 2
steps. Besides, we focused while analyzing the results on the
high diversification capability, high synthetic feasibility and the
availability of a wide panel of the forming starting materials.
Regarding scaffold B, a bioisosteric replacement based on field
technology [17] in the Fieldstere software was carried out as
shown in the (Figure 5).
Figure 5
Reaction-driven scaffold hopping. The disconnection
approach resulted in two scaffolds A and B. A was modified
using a simple scifinder substructure query limiting results on 2
steps synthetic pathways while B was modified using a fieldbased
approach which was influenced too by the ease of
synthesis and overall yield. Regarding synthesis of A: (a) for
aromatic aldehydes :i—RCHO, TEA, MgSO4, THF, 25°C, 12 h;
ii—NaBH4, MeOH, 25 °C, 1 h; for aliphatic aldehydes: RCHO,
NaBH3CN, MeOH, 25°C, 12 h; (b) HO2CCH2CO2Et, EDC_HCl,
TEA, DCM, 25 _C,12 h; (c) (i) NaOEt, EtOH, 25°C, 12 h; (ii) 1 M
H2SO4 (aq), reflux, 1 h. Regarding synthesis of the new A
analogue: (d) malonic acid,SOCl2 ;(e)RNCO,140°C, 10 min.
Library design:
Based on the new scaffolds retrieved (Figure 1), we constructed
a reaction-based virtual library where enumeration of ligands
was carried out according to the reaction used to synthesize
scaffold A. Library design “Enumeration by reaction” module
in Accelrys discovery studio was used.
Pharmacophore-based screening:
Due to the fact that many of the ligands enumerated in the
virtual library will show steric hindrance with the binding site,
a rapid screening was carried out to filter those ligands which
show steric clash with the binding site. This was based on the
presence of excluded volumes in the structure-based
pharmacophore created.
E-novo optimization workflow:
The filtered library was screened using E-novo protocol. This
protocol is usually applied for structure-based lead
optimization as it is based on using core-constrained docking. A
scaffold core is generated from the ligand-bound protein
homology model. After that, Ligands are generated from that
scaffold using R-group fragmentation/enumeration tool such
that the cores are aligned. The ligands side chains are
conformationally sampled and are subjected to core-constrained
protein docking using modified CDOCKER. Finally, a physics–
based binding energy scoring function is applied to rank top
ligand CDOCKER poses using more accurate molecular
mechanics generalized Born with surface area method.
Results and Discussion:
The sequence alignment and the homological model clearly
indicate which amino acids are mutated in the palm I site. This
aided us to conduct the mutation binding energy calculations
on those varied amino acids. The results are shown in
(Table 1,
see supplementary material). They show that the mutation of
Met414 to valine is a strong effector on binding and that
optimization should focus on efficient binding with valine in
genotype 4 (it has shorter side chain than that of Met). Applying
minimization and molecular dynamics on the complex enabled
us to carry out a ligand-protein interaction analysis as depicted
in (Figure 3). This analysis shows that importance of
methansulfonamide group where it hydrogen bonds with
Asp318 and Asn291. It also showed that the hydroxyl group of
the tetramic acid is an important feature where it hydrogen
bonds with Tyr448. Regarding the important hydrophobic
features of the ligand, it is shown that the tertiary butyl group
and the substituted phenyl ring attached to the tetramic acid
interact with Val414, Pro197 and Leu384. Additionally, the
refined complex was used as a starting point to create a
structure-based pharmacophore that is totally dependent on the
actual interactions between the ligand and the protein as shown
in (Figure 4). The pharmacophore clearly takes into
consideration the excluded volume (amino acids of the binding
site that the ligand should not sterically clash with) besides the
important features that are responsible for the aforementioned
interactions in the 2D interaction analysis.
Figure 3
2D-interaction analysis of the reference ligand in the
homological model of NS5b polymerase enzyme of genotype 4.
Figure 4
Structure-based pharmacophore created using the
ligand-homological model of NS5b polymerase. The
pharmacophore represents HHAAA where H is hydrophobic
feature and A is the acceptor feature. Grey spheres represents
excluded volumes, blue represents hydrophobic features while
the green represent acceptor features.
The optimization of the benzisothiazole dioxide inhibitory
activity against genotype 4 was carried out with the aid of the
homological model and the pharmacophore. Initially, we
carried out a retrosynthetic dissection for the ligand as shown
in (Figure 5)
into two scaffolds A and B. In our case, we wanted
to find a bioisoster for A that is synthetically feasible and with
an economic capability of diversification such that it enables the
very rapid probing of different substituents in that binding site.
One of the best hits in scifinder that is based on two steps
synthetic procedure was that of pyrazolidine-3, 5-Dione: A
substituted hydrazine condensation with the readily available
diethylmalonate yield the desired product which can be further
substituted by any isocyante to form a urea using a simple
workup. On the contrary, tetramic acid derivatives require a
suitable amino acid that should be protected followed by
reductive amination with a suitable aldehyde in a reaction that
requires tedious purification by chromatographic techniques
and higher number of steps. Regarding the scaffold B, a
fieldstere hit was used based on the alignment of that hit with
the original scaffold. This was carried out in order to minimize
number of synthetic steps and avoid protection-deprotection
schemes which affect the final yield as depicted in
(Figure 6).
The library design module was applied on a reaction basis
where the scaffold A was varied by different isocaynates and
hydrazines that were retrieved from Scifinder such that they are
commercially available. The constructed virtual library was
screened rapidly using the pharmacophore in a way to remove
those bulky substituents that will not fit into the binding site.
The refined library was used to conduct the optimization study
using the E-novo protocol as mentioned in the methodology.
One of the top ranked-hits was checked for stability in the
binding site using molecular dynamics where it showed a stable
sigma-pi interaction between the ligand and Valine414
(Figure 7).
The simple synthetic feasibility of this hit triggered us to
verify it experimentally where it showed 70% inhibition at
10uM concentration on genotype 4.
Figure 7
2D interaction analysis of the hit chosen for
experimental validation. It is clear that Val414 can
form sigmapi interaction with the phenyl substituent.
Conclusion:
In this study, we provided a novel workflow that can be used to
optimize an inhibitor activity on another genotype that shows
mutation in the binding site. This workflow was applied on
HCV NS5b polymerase enzyme of genotype 4 to optimize
benzisothiazole dioxide inhibitors on it. A focused library
created using reaction enumeration was screened using
structure-based pharmacophore followed by core-constrained
docking and scoring using MM-GBSA. This tweaked protocol
was used to identify an optimized inhibitor for this genotype.
Authors: Raymond F Schinazi; Steven J Coats; Leda C Bassit; Johan Lennerstrand; James H Nettles; Selwyn J Hurwitz Journal: Handb Exp Pharmacol Date: 2009
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