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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5345_Библиотеки_им_академика_М_И_Перельмана.pdf
Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
to the external test set. As for CoMSIA study, the five following types of fields were analyzed in terms
of steric, electrostatic, hydrophobic, hydrogen bond donor and acceptor. The other parameters and the
calculation process were consistent with those used in the CoMFA analyses.
HQSAR emerges as a novel 2D-QSAR technique which employs specialized fragment fingerprints
(molecular holograms) as predictable variables of bioactivity. Two steps including calculation of molecular fingerprints and the subsequent PLS analysis were required in building a HQSAR model.
2
The reported model (CoMFA1) with q
=0.467 was not satisfactory when both of the traditional
electrostatic and steric fields were taken into the PLS analysis. Upon adding the H-bond fields, however,
2
the predictable power of the CoMFA model (q
=0.405) got even worse. Then, the traditional steric and
electrostatic fields were replaced by the indicator steric and electrostatic fields the CoMFA model was
2
established again (CoMFA4) and CoMFA4 exhibited a better result (q
=0.485).
In order to refine the model by enhancing the weights for those lattice points which was most
pertinent to the model, the region focusing method was then used. After applying region-focusing
calculations, the CoMFA model (CoMFA5) with the steric and electrostatic fields and the model
2
(CoMFA6) with the H-bond fields showed significant improvements (q
= 0.520 and q2= 0.459, re-
spectively). As for the CoMFA model (CoMFA7) with the steric, electrostatic and H-bond fields, the
2
after applying the region-focusing calculations became much better (q2= 0.558). More interestingly,
q
the CoMFA model (CoMFA8) with the indicator and hydrogen bond fields was also highly promoted
2
=0.662). As a result, the weights for the meaningful grid points could be improved and the noise
(q
could be partially eliminated.
The CoMSIA models with different combinations of fields. Compared with the CoMFA models,
2
the CoMSIA models did not show obvious improvements (all the q
values were less than 0.5). The
region-focusing technique was also employed to recalculate the CoMSIA fields but no improvements
were observed.
Reliable CoMFA or CoMSIA models were constructed based on molecular alignment. Since the
binding geometry generated by molecular docking is believed to the potential active conformation of
a molecule, there are possibilities to yield more reliable structural alignment on the basis of molecular
docking and then develop better CoMFA models. First, the molecular docking technique was validated
by re-docking narlaprevir to the binding site of NS3/NS4a. The results showed a relatively low RMSD
of 1.04 Å.
In HQSAR calculations, the effects of three parameters, including hologram length, fragment sizes,
and fragment distinctions were investigated. Initially, all of the hologram lengths were collected and
the default fragment size was used, and the various types of fragment distinctions were evaluated for
developing robust models. It is possible that some hydrogen atoms have unusual electronic structures,
thus leading to the properties that are different from those which are predefined by the HQSAR method.
Therefore, in the next cycle of model optimization, the hydrogen atom type was ignored. Zhu’s group
2
also found that adding the chirality and Donor & Acceptor types, the q
2
increase. The models 1, 4 and 5 (q
=0.542, 0.537, 0.550, respectively) were selected and optimized
values showed a remarkable
further by choosing different fragment sizes. After optimizing by two cycles, the best HQSAR model
2
with q
=0.604 was found, which employed a hologram length of 307. Then, this model was used to
predict the 40 compounds in the test set. According to the prediction for the test set, both of the best
CoMFA model and the best HQSAR model exhibited not very high predictive powers; for that reason,
they should be amended in order to use as valuable tools in optimizing lead compound prior to chemical
synthesis and biological assay.
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
From Zhu’s group results, there are two problems attached to building 3D-QSAR models of NS3/4a
protease inhibitors, which are using the best docking conformation as the template and PLS as the algorithm to identify correlation between descriptors and bioactivity. Employing the best docking scoring
conformation is a reasonable approach for 3D QSAR alignment template; however, it should be noticed
that a major shortcoming of docking program is scoring function, which causes the best scoring function
is not necessary the native one. Therefore, this solution should be used carefully based on the knowledge
of X-ray complex structure, in this case is the complex of Narlaprevir and NS3/4a protease. For the latter problem, some other algorithm, such as SVM, NN, deem more reasonable method to determine the
correlation between descriptors and bioactivity rather than PLS, which is only applicable in the case of
linear model.
In addition to 3D-QSAR, peptidomimetic ketoamide analogues were also studied by Cunha and her
group at Federal University of Lavras, using the 2D-QSAR models to discover a clearer insight into the
SAR of this class of compounds.A total of 124 molecules, were based on the structure of boceprevir cocrystallized with NS3 retrieved from the Protein data Bank and their experimental inhibitory activities
against HCV NS3/NS4a were prepared.
Cunha et al. attempted to explore the impact of substitutional variations at the P
, P1’, P3 and P4
1
positions shown in Figure 10 (Cunha et al., 2013). Using thermodynamic, structural and topological descriptors, including E-state decriptors, the activity data were subjected to QSAR studies.
The QSAR models were developed using a training set of 93 compounds. In addition, a test set
consisting of 31 structures selected from the initial 124 molecules was used for externally validating. Based on the standardized descriptor matrix, a hierarchical clustering method was applied in
order to partition all compounds into ten groups. Molecules with high, medium,and low-binding
affinities were arbitrary extracted from the subsets and placed in the test set. Genetic function approximation (GFA) method was used to construct QSAR models. The best model resultant of the
QSAR study was constructed on the basis of these criteria, namely, the LOO cross-validated cor-
2
relation coefficient Q
, used as the favorable parameter of model fitness; the number of significant
QSAR models, and real prediction using the test set compounds (external validation). In order to
identify outlier compounds, the SD of the residuals from the training set were calculated. Outliers
are referred to as compounds whose residuals are more than twice the SD of the residual of fit.
The results revealed that it was necessary to remove two compounds from the training set. This is
another effective approach to determine outliner among traditional methods such as principle component analysis (PCA), Z-score, and self-organization map (SOM); however, it should be employed
in combination with the others as mentioned above.
Figure 10. Structure of boceprevir and definition of the pharmacophoric groups (P
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, P2, P3 and P4)
1
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
The squared linear correlation r2 of 0.66 represents the best 2D-QSAR equation resulting from 93
compounds of the training set, indicating that the analyzed results exhibit a fairly good average fitness
compared to the in vitro data. The statistical significance of the relationship between the bioactivity
and the chemical structure descriptors was further demonstrated by a cross-validation analysis. LOO
2
cross-validation (LOOcv) analysis generated a Q
of 0.65 with standard error of 0.36. LOO cv correlation coefficient values over 0.5 showed that the model is a useful tool for predicting affinities for new
molecules in this set. The statistical significance of the relationship between the biological response and
the chemical structure descriptors was further evaluated by an external validation analysis. Taking only
2
2
(R
the test set compounds into account, the predicted R
) value was found to be 0.52. The standard
pred
deviation (SD) of the residual values is 0.28
As for the docking process, the most likely conformation of the ligand can be generated. The ligand conformation was identified by estimating the energy of its interaction with the target and the best (i.e. the lowest
energy) are then returned for further analysis. The docking scoring function, the Molegro Virtual Docker (MVD),
was derived from the piecewise linear potential (PLP), a simplified potential, whose parameters are fitted to
protein-ligand structures and binding data scoring and further extended by the Generic Evolutionary Method.
Results from Culha et al was fairly good, but it could be strengthened by combining both QSAR and
docking approaches rather than using them as separated parts. By doing this, significant interactions
in docking could be used as hints to select QSAR descriptors, for instance, number of hydrogen bond
acceptors and donors or the surface of van der Waals interactions, in addition to statistical methods;
furthermore, highly potent compounds predicted by QSAR model need to be re-evaluated using docking
by comparing with native conformation from X-ray structure. Additionally, Culha and Zhu also proved
that conventional 2D QSAR and 3D QSAR method with PLS algorithm are not successful in building a
robust model for NS3/4A protease uncovalent inhibitors, SVM, genetic function approximation (GFA),
or NN should be considered instead.
QSAR-Assisted Pharmacophore in Developing
Novel Agents against NS3/4A Protease
Using amino-terminal sequence analysis of mature proteins expressed in cell culture (Wei et al., 2008),
the NS3-dependent cleavage sites have been revealed. The X-ray of NS3/4a protease and determined
conformation of compound 2 (BOC–GLU–LEU–FKI) (Figure 11) was employed as the template to developing structures for the residuals in the same series due to its high potency against NS3/4a protease.
These molecules were divided into the training and test sets with each composing of 16 inhibitors and
covering approximately the same activity range. The ligand was then docked into the active site of HCV
NS3 protease using the GOLD. The covalent bond between the catalytic serine (Ser139) and the ketone
group of the inhibitors was fixed during the GOLD docking process.
Figure 11. Compound 2 (abu: 2-aminobutyric acid)
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
The electrostatic contributions at the lattice intersections where maximum steric interactions were
computed were ignored. The same lattice where each molecule was submerged for CoMFA was also used
3
for CoMSIA. In the latter, a sp
carbon atom of radius 1.0 Å and charge +1 was used as a probe to compute
the CoMSIA similarity indices, employing the Gaussian-type distance dependence between the probe and
atoms of the molecules of a data set. This functional form does not require arbitrary definition of cut-off
limits and the similarity indices can be calculated at all lattice points inside and outside of a molecule.
On the basis of the docked conformation generated by the GOLD, the structure was aligned in proceed-
ing with CoMFA and CoMSIA. The optimum number of components used to derive a model was defined
2
as the number of components leading to the highest cross-validated r
prediction. The robustness of models was judged by the conventional correlation coefficient r
(q2) and lowest standard error of
2
, standard
error of estimate, F-values, and coefficient correlation between predicted and real value of external test set.
Still, the same training set was used for establishing some pharmacophore models by the Catalyst.
All the parameters employed were set as default. There were five pharmacophore features selected for
the hypothesis generation procedure, namely, HD (H-bond donor), NEG (negative charge), HY (hydrophobic), and HR (hydrophobic aromatic). The best minimized conformation of each compound in
training set, adding 20 boronic acid-based inhibitors, was collected and then submitted to the hypothesis
generation process.
For generating logical structures for 3D-QSAR studies, the parameters of GOLD were refined to give
the minimum RMSD value between the docked and crystal structure of 1DY8. The docked structures were
aligned by using several backbone atoms selected as the correspondence points. The aligned structure
sets were then analyzed by the CoMFA, CoMSIA and results with significant statistics afforded are kept
2
for further studies. The best CoMFA result generated a LOO validated q
2
of 0.752 and conventional r2 of 0.998. The CoMSIA is carried out in a stepwise manner, testing any
q
of 0.749, cross-validated
loo
combination of five different field indices in total, namely, steric (S), electrostatic (E), hydrophobic
2
2
, q
(H), H-bond acceptor (A) and H-bond donor (D). Values of r
, q2, Standard Error of Prediction
loo
(SEP), and F-statistics of CoMSIA results are compared and the best result chosen in the analysis are
2
the combination of S, E, H and D (q
of 0.76, the best q2 of 0.79).
loo
The CoMFA and CoMSIA contour maps identified both common favorable and disfavorable regions
for steric interactions. The former region is located around P2 position, and the latter one covers around
P3 position. The contour map generated by CoMFA agreed with CoMSIA in determining the favorable
regions for negative charge interactions around position P3. However, the CoMSIA contour maps located some favorable regions around position P2, P3 and the capping group of inhibitors for lipophilic
interactions. Also, the favorable region for hydrogen bond donating interactions around position P3 is
determined. Based on the contour maps generated by CoMSIA, the structural features, which included
HD, NEG, HY, and HR, was selected for constructing a pharmacophore model of the truncated ketoacid
inhibitors. There were 10 hypotheses generated in total, then they were validated by the test set to identify
the optimal model with accuracy value of 88% (Hypo1 model).
In this study, 3D QSAR model demonstrated much higher prediction ability than the model gener-
2
ated by Culha or Zhu in terms of better validated values (q
, q2, …), which is probably due to the fact
loo
that the descriptors of compounds forming covalent bonds with NS3/4a protease has much more linear
relationship with bioactivity than compounds interacts via non-covalent bonds. Besides, generating
pharmacophore-based model from 3D-QSAR contour map features is considered a promising approach
to enhance speed of virtual screening in large dataset of compounds, which overcome the major drawback of 3D-QSAR.
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339
Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Fragment-Based Approach in Development of HCV Protease Inhibitors
Recent advances in the traditional QSAR led to introduction of fragment-based, a new branch of 2DQSAR methodologies (Karelson et al., 2012). In this approach, the compound is treated as a system of
separate units (fragments) combined in specific framework.
First step of building fragment-based model is of the calculation of the fragments’, usually the FQSAR
Model program is chosen. According to their definitions, these descriptors can be classified as follows:
1. Constitutional,
2. Geometrical,
3. Topological,
4. Charge-related,
5. Quantum chemical.
All fragments were treated by molecular mechanics, and then further optimized as random vacuum
conformer with the minimum potential energy. Hence, the quantum-mechanical semi-empirical calculation in the form of the AM1 energy minimization was applied such as the Polak-Ribiere Conjugate
Gradient (PRCG), Fletcher-Reeves method, or Hestenes-Stiefel approach.
Each compounds in the dataset needs to be fragmentized in certain and constant number of fragments. Further, the natural descriptors for the fragments, so-called derived descriptors (D
independent variables of the main FQSAR equation in which correlation between these descriptors and
bioactivity are determined by in multiple regression analysis (MLR). There are many other algorithms
that can be used for constructing it instead (SVM, BPNN, GFA, Multi-layer perceptron (MLP), etc.),
which are usually applied in more sophisticated cases such as non-linear relationship between descriptors
and biological activity. The validation of the multi-linear model was performed using ABC validation.
This method deploys three test sets in order to assess the predictability of the equation. Additionally, it
takes into account the data distribution of the property.
The main power of the FQSAR algorithm is that it permits automatic construction and prediction of
new compounds based on already developed FQSAR model using an internal fragments library which
is automatically built by combining the fragments. The library consists of the fragments separated
from the compounds in the training set or/and externally imported fragments. By posing the main
criteria derived from the FQSAR equation, the fragment library can be searched for active compounds
generated automatically from the assembled fragments. The assembly process includes combinatorial reconstruction of a large number of compounds using the fragments derived from the original
training set. Once a new molecule is generated after each step, its properties will be predicted by the
FQSAR equation.
From many reports, the studied data set of Karelson et al. contains 102 inhibitors of HCV NS3
protease. According to the separation, these compounds were based on several structurally different
central scaffolds. All these compounds were broken into three linearly connected fragments. On the
basis of the described FQSAR approach, the final fragmentation of all 102 compounds from the model
development set resulted in 103 fragments. The best fragment model developed in the study had six
descriptors describing log(1/K
permutations were Y-scrambling R
2
cross-validation (Q
) results were 0.799; 0.738; 0.767, respectively.
) in the fragment space. Validation by Randomization tests after 10,000
i
2
= 0.061, X-scrambling R2= 0.059, XY-scrambling R2= 0.062, and
), are used as
i
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
The descriptors in the FQSAR model are related to the atomic charges, electrostatic interactions
and molecular weight of the compounds. The most statistically important descriptor is the maximal net
atomic charge (AM1) for any atom type. This descriptor indicates that the largest charge (regardless
of the atom type) in the fragment plays an essential role in the electrostatic interactions of the ligand.
The second statistically important descriptor is the polarity parameter of the molecule, which can
be again ascribed to the electrostatic interactions between ligand and target. Another descriptor is the
FQSAR model confirming this conjecture is the lowest coulombic interaction for N-H bonds. For these
two descriptors the interactions between the fragments of the molecule is not linear and it reflects the
more interactive nature of these features. The feature of the ligand is probably related to the correct
positioning of the inhibitor in the pockets of the receptor.
Alongside with conventional 2D-QSAR method, fragment-based drug design has emerged as a novel
approach, which is proven significantly efficient in predicting the potency of NS3/4a protease inhibitors,
typically in combination with high throughput screening (HTS). However, there are several problems to
consider such as the size and potency of fragments, number of fragments in the library, as well as how
fragments are combined. Briefly, fragments’ weight should vary in the range of 150-250, a total number
of hydrogen bond acceptors and donors should be below 3, cLogP approximately 3 and binding affinity
ranging from nM to µM (Rees et al., 2004). Additionally, the number of fragments in the library and
how they link rely on the organic synthesizing ability of a certain laboratory.
QSAR Approach in the Discovery for Multi-Target
Inhibitors: The Case of HCV and HIV Co-Inhibitors
The following part aims at introducing QSAR approach in the discovery for multi-target inhibitor, including HIV-1 reverse transcriptase, HCV NS3/4a protease, and HCV NS5B polymerase.
HIV infection has become a major global public-health threat alongside HCV (Liu et al., 2011).
Even more remarkable, HIV-HCV co-infection is rapidly emerging as a leading cause of morbidity and
mortality all over the world, due to common rapid mutation characteristics of the two viruses as well as
their similar complex influence to immunology system. Nevertheless, few researches have been carried
out on the investigation of the molecular mechanism of their co-infection and designing of the multitarget co-inhibitors for HIV and HCV respectively.
In a study conducted by Liu et al., a multi-target QSAR study of the inhibitors for HIV-HCV coinfection were addressed with an in silico machine learning technique, i.e. multi-task learning (MTL),
to help to guide the co-inhibitors design. Firstly, an integrated dataset including 3 kinds of HIV target
subsets and 6 kinds of HCV target subsets was employed. These molecules targeted HIV protease, integrase and reverse RT, as well as HCV NS3 and NS5B, respectively.
It should be kept in mind that the QSAR data were reported by different research groups under different protocols with different activity measurements, which make difficulty for consistency of bioactivity
of inhibitors. However, these data can be integrated in an elegant multi-target QSAR relationship of the
HIV-HCV co-infection, which would expect to exploit the possible synergies between different datasets
and achieve a better QSAR model to guide the synthesis of certain inhibitors with improved activities
for both HIV and HCV.
A novel accelerated gradient descent algorithm based MTL model was performed for multi-target
QSAR modeling on the integrated datasets simultaneously. MTL has been developed in machine learning
research to situations where multiple related learning tasks are accomplished together. It has been proven
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341
Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
to be more effective than learning each task independently. The intuition underlying the framework is
that the multiple related tasks can benefit each other by sharing the data and features across the tasks,
which can often boost the learning performance of each single task. The QSAR modeling procedure is
generally formulated as a regression model for predicting the compound activity on the basis of a given
set of molecule descriptors. This study was elegantly formulated as a multi-task regression framework
to reveal useful clues for multi-target drug screening and synthesizing for HIV-HCV co-infections.
Briefly, the whole process is achieved by the following steps: First, all the compounds are gathered as the input for generating their common scaffolds. If more than one scaffold is presented, they
are topologically aligned to produce a common numbering system. Second, assigning the scaffold by
enumerating all possible substructures matches, then minimizing a pair-wise energy term which leads
to the lowest possible diversity of implied R-group substitutes, which is expected to provide a set of
analogous substitution points. Based on the pipeline, it was identified that the most efficient compound
modification strategy is able to improve the molecule affinity targeting on multiple HIV-HCV enzymes.
Furthermore, these identified strategies will be further explained by the joint feature ranking obtained
under the multi-target QSAR paradigm.
QSAR Integrated into Structure-Based Drug Design to
Discover Novel Agents against NS3/4A Protease
Recently, it has been reported that N-terminal cleavage products of substrate (typically hexapeptides)
form competitive inhibitors of the NS3 protease activity (Cunha et al., 2013). These native inhibitors was
employed as the template for designing novel inhibitors. Frecer and coworkers designed focused combinatorial library of hexapeptide-like inhibitors of HCV NS3 serine protease by structured-based molecular
design assisted by combinatorial of the individual residues (Frecer et al., 2004). These pseudo-peptidic
inhibitors were of cleavage product NS3/4A protease, which bind specifically to its active siteand their
experimentally determined inhibition constants, which allowed to predict the inhibitory effect of newly
designed an optimized analogues.
Crystal structures of NS3 protease with its cofactor NS4A were employed for the design of new
pseudo-peptide inhibitors. The bound conformations of the designed inhibitors were modelled by
adding two more residues to the initial backbone with the side-chains filling the S
pockets of
6-S1
the target’s active site. In order to accommodate the side chains of the individual residue of the
designed inhibitors in the pocket of the NS3/4A binding site, a complete torsion force scan of
potential energy hypersurface over all the rotatable bonds of the considered side chained was conducted. All possible combinations of individual torsion angles of the side chain were generated and
each structure was completely optimized using molecular mechanics. By relaxing the structures
stepwise, starting with the side chains and following by protein or peptide backbone relaxation,
minimizations of the enzyme-inhibitor complexes, free enzyme and free inhibitors were carried
out. A sufficient number of steepest descent and conjugate gradient iterative cycles were used in
all the geometry optimizations. All structures were treated as at neutral pH with the protonizable
and ionizable groups being charged.
Inclusion of solvent effects in the prediction of inhibition constants improved remarkably prediction
ability of enzyme-inhibitor binding, particularly for charged inhibitors. The solvation energy of the
enzyme-inhibitor complexes, free enzyme and free inhibitors were computed, deploying the version of
polarized continuum model (PCM) adapted for calculations on biopolymers.
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Modeling the interactions between the enzyme and a set of 16 known inhibitors was first carried out,
which is aimed at evaluating the validity of the structure-based computational process for prediction
of binding affinities of newly designed structures. Due to the strongest computed interactions between
compound15 and its target, it was chosen as the reference inhibitor (I
). Closer inspection of the rela-
ref
tive interaction and contributions of solvent effect to the affinity reveals that both the enzyme-inhibitor
(∆∆H
) as well as the solute-solvent (∆∆G
int
tions, both correlate well with the molecular charge of the inhibitor (Q
) interaction terms are dominated by electrostatic interac-
solv
) and ∆∆H
i
and ∆∆G
int
mutually
solv
compensate their contributions to the total enzyme-inhibitor binding affinity.
Interaction and solvent effect contributions were correlated with the experimental pK
MLR algorithm. Direct correlation between the pK
and ∆∆G
i
was not well balanced, which was
comp
values using
i
caused by utilization of different computational approaches. However, a significant correlation was
achieved between pK
Since a good correlation between a parameter such as the molecular charge and the ∆∆G
bution, the solvent term in the regression equation was replaced by the Q
and the individual contributions to the relative binding affinity.
i
descriptor to facilitate ration
i
contri-
solv
structured-based design and residue optimization for a larger series (hundreds) of designed and screened
peptidic inhibitors. Hence, a simple predictive QSAR model applicable to structure-based design of an
inhibitor library in the simplified form was obtained:
For tight binding of peptidic inhibitors to the NS3/4A, two binding anchors are vital: the P
trophile positioned near the catalytic site and the acidic residues in P
, P6 positions. Therefore, a dramatic
5
, P2 elec-
1
decrease in the inhibitory potency of shorter less acidic peptides was observed after removing the acidic
residues in P
should concern longer peptideswith a strong electrophile in the P
and P6 positions of known inhibitors. Rational design strategy of new more inhibitors
5
position. The predictive QSAR model
1
suggests that highly acidic hexapeptides, which strongly interacts with the cationic biding site of the
enzyme can form tight binding inhibitors of the NS3/4A with estimated K*
in the pM range. According
i
to the model, the stabilizing effects of the negative interaction contribution to the relative binding affinity of hexapeptides can exceed the destabilizing solvent effects upon the enzyme-inhibitors formation.
Using the QSAR model derived for the training set of peptidic inhibitors, it is predicted that the derivate
N1 (Figure 12) possesses an inhibition constant K*
of 0.04 nM towards the target, which suggests for
I
N1 higher inhibitory potency then that of the most effective inhibitor I11 (Figure 13) considered in the
training set with the K
towards the NS3/4a exceed by almost -40 kcal mol
of 0.75 nM. The computed interaction component of the binding affinity of N1
i
-1
, which demonstrated that further structure-based
modifications of the inhibitors may lead to a discovery of more potent and more specific derivatives
inhibiting the NS3/4a serine protease of HCV
From the set of 50 designed inhibitor candidates with individually optimized residues in the P
positions, a set of nine best residues (building blocks) useful in specific positions of hexapeptides
P
1
to
6
derived from N1 was chosen. The inhibition constants of the combinatorial analogues toward the enzyme were predicted using the QSAR model (or virtual screening), which is constructed on the basic
of computed enzyme-inhibitor interaction component of the binding affinity of analogues C1 to C8
towards the NS3/4a and the estimated solvent effects that are proportional to the molecular charge Q
of the derivatives. Compound C2 (Figure 14) shows significantly high potency against NS3/4a serine
protease with the Ki of 0.3 pM.
Briefly, the innovative approach in which QSAR was integrated into structure-based model was
proven significantly effective in case of designing inhibitors of NS3/4A protease. The method, however,
remains several drawbacks, such as the requirement of co-crystalized structure and the approximation
i
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Figure 12. Compound N1
Figure 13. Compound I11
Figure 14. Compound C2
of calculation of ligand-target interactions. These weaknesses are capable of influencing strongly on
prediction results; therefore, researchers should consider these drawbacks in prior to using the QSAR
approach mentioned above.
Miscellaneous Methods in Virtual Screening NS3/4A Protease Inhibitors
Only six researches with not very good results were carried out in literature, which means QSAR alone
is not robust enough in developing novel NS3/4a protease inhibitors due to the structure complexity of
both inhibitor and protein. For this reason, we introduce briefly numerous successful virtual screening approaches that are capable of being combined with QSAR in order to enhance prediction results.
However, this book is mainly devoted to QSAR, so we do not go with too detail into virtual screening.
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Docking is a virtual screening method with increasing interest in discovering novel NS3/4A protease
agents, especially compounds with diverse scaffolds rather than peptidomimetic structure such as telaprevir, danoprevir, and so on. There are two distinct methods in docking research in order to discover
potent NS3/4A protease inhibitors, including docking combined with molecular dynamics or consensus
docking. They all have their strength and weakness. Chaudhuri et al. used 5 different docking softwares,
including Surflex, GOLD, Glide, Autodock, and Amber in their protocol to search for some commercial
collections of compounds (Chaudhuri et al., 2012). First, Surflex was utilized to filter out compounds
with inappropriate shapes. Then, GOLD was employed to dock compounds with suitable shapes, and
5000 top scored compound chosen by GOLD was redocked with three different software packs: Glide,
Autodock, and Amber. A consensus list of top scoring compounds from the three softwares was generated, and 2% of 5000 compounds from the list was chosen to run in vitro assays. The result revealed
that 12 compounds from this shortlist possess the inhibitory activity of about 50 µM with 2 compounds
exhibiting remarkable promising IC
values.
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In another approach, only one docking algorithm was utilized in combination with molecular dynamics. In 2011, Takaya et al. employed a novel docking algorithm, GENIUS, involving molecular
dynamics to take account into induced fit effect (Takaya et al., 2011). 0.4 million compounds, that is
commercially available, were docked in order to choose 97 most promising ones to perform in vitro assay. Two compounds with inhibitory activity in micromole range, not very high active but new scaffold,
were found, and their derivatives were synthesized. Another research was done by Li et al., Glide was
chosen as docking solfware package, and top 2000 scoring compounds were chosen to perform MMGBSA, a kind of molecular dynamic function (Li et al., 2013). Hence, 218 interesting candidates were
passed into in vitro replicon assay; then, 16 compounds were determined as NS3/4A protease inhibitor
in nanomolar range.
Both of these approaches have their own advantages and drawbacks. Consensus docking-based approach above show significantly promising result, especially in searching for a new scaffold; however, it
has some remarkable disadvantages. Many researches on evaluation of docking algorithms (Englebienne
et al., 2009; Enyedy et al., 2008; Kim et al., 2008; Li et al., 2014; Moitessier et al., 2008; Plewczynski
et al., 2011; Warren et al., 2006) revealed that no docking algorithm really outperform the others in
most cases. For that reason, in some cases, only one docking algorithm is able to find active compounds
from collection instead of consensus docking, so the latter could filter out compounds with true active
activity. Another thing is docking a large number of compounds by many docking software is really
time-consuming and need a lot of labor effort. To combine with QSAR model, in the authors’ point of
view, the second approach is more suitable and time-reasonability. Particularly, a compound with top
scoring in only one docking algorithm in consistent with being predicted high inhibitor concentration
by QSAR model is reliable enough.
Pharmacophore-based virtual screening is another solution in searching for inhibitors with novel
scaffolds. However, only one successful pharmacophore model has been done to date due to the
complex structure of peptidomimetic compounds and protein binding pocket. Based on the crystal
structure of NS3/4A protease with a macrocyclic inhibitor, manual pharmacophore model was
generated including 7 key features (Wadood et al., 2014). This model was subsequently used as a
filter on application to commercial databases. As a result, 786 hits was chosen for further assessment including docking and in vitro assay; consequently, 16 compounds were identified as NS3/4A
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