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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5865_Библиотеки_им_академика_М_И_Перельмана.pdf
Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Figure 2. Some potent NS5B inhibitors that included in the domain of applicability
Proposed from (Melagraki et al., 2007).
HOMO energy, three topological indices including Kier and Hall index order 0, 2 and 3. The model
was validated employing different validation techniques. Subsequently, the structural modifications
were proposed to afford novel active patterns. The effects of various structural modifications on
biological activity were scrutinized and biological activities of innovative structures were estimated
utilizing the constructed QSAR model. In particular, the authors described a search for optimized
pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents
of the main building block scaffolds. The detection of the domain of applicability defined compounds
whose estimations could be accepted with confidence. Some potential NS5B inhibitors that included
the domain of applicability are shown in Figure 2.
Very recently, Wang et al. developed several QSAR models for prediction of the inhibitory
activity of 333 HCV NS5B polymerase inhibitors (Wang et al., 2014). All the inhibitors are NS5B
NNIs fitting into the pocket of the NNI III binding site (There are totally four binding sites of NNI
including Thumb domain 1, Thumb domain 2, Palm domain 1, and Palm domain 2. The first binding site is called NNI I binding site, and so forth for NNI II, NNI III, NNI IV binding site (Delang
et al., 2010)) . For each compound, global descriptors and 2D property autocorrelation descriptors
were computed from the program ADRIANA.Code. Pearson correlation analysis was employed to
choose the crucial descriptors for constructing models. The authors divided the entire dataset into
a training set including 232 molecules, and a test set having 101 compounds randomly or employing a Kohonen’s self-organizing map (SOM). In a next step, the randomly split training set was
utilized to build two models: model 1A by multilinear regression (MLR) analysis and model 1B by
support vector machine (SVM) method. The latter training set was also used to build two models:
model 2A by MLR, and model 2B by SVM. The results showed that the two SVM models (model
1B and 2B) had better performance than the two MLR models (model 1A and 2A). Among these
four models, model 2B performed the best. Some molecular descriptors, for instance, molecular
complexity, the number of hydrogen bonding donors and the solubility of the molecule in water
were found to be key factors which determined the bioactivity of the HCV NS5B inhibitors. Some
other molecular properties such as electrostatic and charge properties also played significant roles
in the interation between the ligand and the protein. The selected molecular descriptors were further
affirmed by evaluating the interaction between two representative inhibitors and the polymerase in
their crystal structures.
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
3D QSAR Approach
In their recent publication, Patel et al. conducted a study of benzimidazole derivatives as hepatitis C
virus NS5B polymerase inhibitors using three-dimensional quantitative structure-activity relationship
(3D-QSAR) and molecular docking method (Patel et al., 2008). They applied comparative molecular
field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR
methodologies to the training set (45 compounds) and test set (22 compounds) that include benzimidazole, tetracyclic indole, quinoxaline, and indole N-acetamide derivatives. The 3D structures of
these compounds were built by the Sketch Molecule function in SYBYL. Energy minimizations were
performed using the Tripos force field and Gasteiger-Marsili charges. Besides, two different alignment methods were employed. Some potent molecules used in this research are shown in Figure 3. In
the ligand-based model, compound 15 (the most prospective molecule that has the lowest IC
was selected as a template for atom based alignment in SYBYL. In the structured-based model, the
molecules were aligned according to the bioactive conformations obtained from docking. In docking,
all compounds were built by means of the fragment dictionary of Maestro and geometry optimized
using the Optimized Potentials for Liquid Simulations-All Atom (OPLS-AA) force field. The protein
was prepared and refined by SYBYL protein prepare structure tool. Then, the extra precision Glide
docking method was performed to dock all molecules into the allosteric site of HCV NS5B polymerase
(binding site NNI II). The final bioactive conformations were imported into a SYBYL molecular
database for CoMFA and CoMSIA studies.
The CoMFA and CoMSIA analyses were evaluated for both the ligand- and receptor-based alignment models. However, the ligand-based alignment model caught more attention since it yielded
suitably statistical data, and the conformations used in this alignment were more beneficial than
those of the docked conformations. Hence, the training set for the ligand-based alignment model
was examined for outliers. After removing two outlier compounds, 43 remaining compounds were
2
reused to build 3D-QSAR models. A high bootstrapped r
value for CoMFA and CoMSIA models
as well as a small standard deviation pointed out the existence of a similar relationship among all
of the compounds in the training set. Another assessment was that the model with hydrophobic,
hydrogen bond donor and acceptor fields were superior among all the model derived, which illustrated the higher predictive power of CoMSIA model than CoMFA model in the development
of novel NS5B inhibitors.
Finally, the CoMFA model asserted that the tryptophan derivatives are more active than the tyrosine ones, while CoMSIA results revealed the importance of hydrogen bond donor and acceptor
favorable contours at the R
-position of the indole ring and at the R-position at the chiral carbon,
1
respectively, for potent NS5B polymerase inhibitory activity. Docking results indicated that the N
cyclohexyl and the C
-furyl substituents of the benzimidazole ring of inhibitors were binding to a
2
deep and to a narrower hydrophobic pockets, respectively. The carbonyl oxygen atoms of the amide
linkage and the −COOH group at the chiral carbon were discovered to be related to a hydrogen bond
network with the guanidine of Arg503. The acidic H-bond donor group at the R
-position of the
1
indole ring was found to be solvent-exposed and able to forms hydrogen bonds with the backbone
of Val494. The 3D-QSAR models were assessed to accurately predict the HCV NS5B polymerase
inhibitory activity of structurally diverse test compounds and to provide trustworthy clues for further
optimization of the benzimidazole derivatives.
value)
50
1
-
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Figure 3. Potent NS5B inhibitors
Presented in (Patel et al., 2008).
Musmuca et al. presented the results of a combining 3D-QSAR with ligand based (LB) and structure
based (SB) alignment to identify innovative molecular scaffolds as NS5B inhibitors (Musmuca et al.,
2010). The study comprised 3D-QSAR model, LB and SB alignment methods, a LB-SB virtual screening and biological assessment. First, the structure based 3D-QSAR models were constructed using
NS5B NNIs. A total of 24 NS5B NNIs were selected from the Protein Data Bank (PDB) as training
sets, including 15 thumb and 10 palm allosteric NNIs (2JC0 complex included in both categories).
All subsequent molecular docking simulations and ligand based alignments were also conducted with
these training sets.
At first, the GRID/GOLPE method was employed to define two final SB 3D-QSAR models that were
internally and externally validated. The internal test was cross-validation utilizing the leave-some-out
with five random groups, and the external validation was using two test sets. LB and SB alignment
methods were examined to evaluate the reliability on the correct molecular alignment for unknown
binding mode modeled compounds. While the LB approach employed the concept of morphological
similarity performed in Surflex, the SB method used Autodock program. Both Surflex and Autodock
programs were capable of reproducing with minimal errors the experimental binding conformations of
24 training set compounds. 81 (thumb) and 223 (palm) modeled compounds taken from the literature
were LB and SB aligned and utilized as external validation sets for 3D-QSAR models. With low error
of prediction, the 3D-QSARs were proved to be useful scoring functions for the in silico screening
procedure. The virtual screening of the NCI Diversity Set 1990 compounds led to the selection of 20
top-scoring compounds for enzymatic assays for each final model. Among the 40 chosen molecules,
preliminary data provided four derivatives performing IC
values ranging between 45 and 75 µM.
50
Structures are displayed in Table 1. Among these four compounds, NSC 123526 exhibited a docked
conformation which was in good agreement with the most active compound in the thumb training set.
It is a new active molecule with an unprecedented scaffold among NS5B known inhibitors.
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Table 1. Potent HCV NS5B inhibitors
Thumb Domain Palm Domain
36 (NSC 123526, IC
38 (NSC 125626, IC
Presented in (Musmuca et al., 2010).
= 46.0 µM) 37 (NSC 169534, IC50 = 64.5 µM)
50
= 73.3 µM) 39 (NSC 3354, IC50 = 54.3 µM)
50
VIRTUAL SCREENING METHOD IN IDENTIFYING
NS5B POLYMERASE INHIBITORS
This part aims at discussing various virtual screening methods, including pharmacophore and docking,
in finding anti-NS5B polymerase agents. These methods could be used either independence or combination with QSAR to create a robust model.
In their publication, Kim et al. presented a pharmacophore-guided virtual screening in order to
identify novel anti-HCV aryl diketoacid (ADK) replacements through UNITY-based pharmacophore
search of a compound library, which was then used for docking-based virtual screening (Kim et al.,
2008). Based on their previous study on the binding mode analysis of the ADKs at the active site of
HCV RdRp, pharmacophore models were proposed and then successfully applied to the structure-based
3D-QSAR study. LeadQuest compound library implemented in SYBYL package was employed to initiate the virtual screening procedure. Three generated pharmacophore models were utilized by UNITY
search to filter the compound library. UNITY module used a conformationally flexible 3D-searching
algorithm to result in rapid identification of molecules that match with the given pharmacophore. As a
result, three independent compound groups with 183, 32 and 12 molecules were selected. Among 227
compounds attained from the UNITY search, only 103 molecules were successfully docked employing
the FlexX-Pharm module in the SYBYL. The chosen 103 molecules with the reasonable poses were
TM
then scored utizing the Cscore
(Dock, Chem, FlexX, PMF, and Gold). The top 10 molecules with the highest total scores were chosen
for biological assay. Among those, the compound 23 performed the most potent antiviral effect (1.26%
relative cell viability in comparison with the control). The docking pose of this compound was perfectly
matched with the pharmacophore model and the hydrophobic hole in the aryl-binding site. The molecule
structure is shown in Figure 4.
In 2009, Ryu et al. discovered a new binding pocket of NS5B, distinct from the nucleotide binding
site but highly conserved among diverse HCV isolates (Ryu et al., 2009). Then, they performed virtual
screening of compounds that fit this binding pocket from the available database of 3.5 million molecules.
As a result, it was found that the interface between thumb and palm domain was a promising binding
module of SYBYL, which included five different scoring functions
Figure 4. Potent NS5B inhibitor
Proposed by (Kim et al., 2008).
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
pocket. A pharmacophore map was created using PharmoMapTM for structure-based virtual screening.
The pharmacophore model illustrated the pharmacophoric features such as hydrogen donors/acceptors
and hydrophobic interations, which was employed as a search query for identifying inhibitors from a
3D small molecule database. The commercially available multi-conformer 3D database includes 3.5
TM
million compounds (PharmoDB
). After filtering out the primary screened compounds, 119 molecules
were obtained for further testing in vitro inhibitory activity. Among those, three compounds (shown in
Figure 5) with IC
values of about 20 µM were identified. For a refined docking model of hit compound
50
(compound 24) into the HCV NS5B, automated docking simulation was carried out using AUTODOCK.
X-ray crystal structure of NS5B of HCV genotype 1b from PDB (PDB ID: 2AX0) was also employed.
A new effort to discover novel polypharmacological NS5B inhibitors is described in the work of ElHefnawi et al. The study used docking screening guided by contact pharmacophores and neural-network
activity prediction models on 4 allosteric binding sites and molecular dynamics simulations to identify
the potential hits (Elhefnawi et al., 2012). Initially, the authors employed a two-phase docking screens
with Surflex and Glide Xp. The compounds were ranked in terms of scores, and important hydrogen
interactions. In the next step, a machine-learning target-trained artificial neural network PIC prediction
model was utilized for ranking. This provided a better correlation of IC
values of the training sets for
50
each site with distinct docking scores and sub-scores. The authors, subsequently, constructed the interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures
for the interaction pharmacophore (common interation modes) of inhibitors for the four NNI binding
sites. These were employed to filter the hits based on the critical binding feature of formerly reported
inhibitors. This filtration process resulted in identification of prospective novel inhibitors and formerly
reported ones for the thumb II and palm I sites (HCV-81) NS5B binding sites. Finally molecular dynamics simulations were conducted, confirming the binding modes and resulting in 4 leads.
The common trends of receptor-ligand interactions pharmacophores were generated using calculation
from 37 PDB co-ordinates compiled according to the corresponding sites. Classifying the palm region
into three sub-sites, hydrogen bonds with the backbone amide of Tyr448 and hydrophobic interaction
with the deep pocket were identified as a common binding feature among all chemical subclasses of
Figure 5. Chemical structure of the novel inhibitors
Proposed by (Ryu et al., 2009).
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
palm I inhibitors. In addition, polar interaction with Asn316 was common between palm II and palm III
inhibitors. While Ser476, Tyr477 and Arg501 polar contacts constituted the major features for thumb II
inhibitors, Arg503 formed the only common polar interation for thumb I inhibitors. These interactions
could be utilized for further study in the development of novel NS5B inhibitors.
In their publication, Louise-May et al. discovered novel dialkyl substituted thiophene inhibitors of
HCV using in silico screening procedure (Louise-May et al., 2007). Available drug-like compounds
were docked into the thumb domain of HCV NS5B so as to identify novel ligands. The 3D structure of
HCV NS5B (1GX6) and Glide program were used for the docking. The database of these compounds
was derived from an initial pool of about two million molecules from the Specs & BioSpecs, Bionet,
Microsource, Available Chemicals Directory, Nanoscale, ChemDiv, Orion, Asinex, Interbio-screen,
Timtec, and Chembridge databases. Tripos SLN Filters were employed to remove duplicates and reactive functionalities and to choose compounds based on the subsequent criteria: compounds having at
least one ring, ≤ 1 chiral center, ≤ 8 rotatable bonds, 275-400 molecular weights, and ClogP ≤ 3.5. This
procedure resulted in a library of 90,000 molecules. The top 1318 ligands with glide score of -7.17 or
better were visually scrutinized to select a structural diverse set of 50 putative ligands. In the following
step, the selected compounds were evaluated in an HCV replicase complex (RC) assay (cell-free) and
HCV replicon assay (cell-based). The most active compounds are shown in Figure 6.
Compound 1 was followed-up by substituting at thiophene 3-, 4-, and 5-positions, and in-house
synthesis of analogs. When the amide function in compound 1 was replaced by a cyano (compound 2),
there was an improvement in potency in the RC assay (IC
= 25 µM). The ethyl ester and carboxylic acid
50
at the 3-position were also tested but the results were inactive. A loss of activity was found for simple
substitutions of a methyl group for an ethyl at the 4 (acyclic) or 6 (cyclic) positions, whereas activities
of the 4-Et/5-Me (acyclic) and 6-Et (cyclic) substituted analogs were comparable (compounds 1 and 3)
in the RC assay. The active compounds in the RC assay were also assessed in the HCV replicon assay.
Lower than expected EC
/EC50) for these compounds is less than 10 (3-9).
(CC
50
values are partly derived from the cellular toxicity as the therapeutic window
50
Figure 6. Potent HCV NS5B inhibitors
Proposed from (Louise-May et al., 2007).
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Talele et al., in their recent work, identified the novel allosteric inhibitors of HCV NS5B by a com-
bination of structure-based virtual screening, synthesis and structure-activity relationship (SAR) optimization approach (Talele et al., 2010). A ChemBridge database containing 260,000 compounds was
used as the source to initiate the virtual screening procedure. Initially, the database was filtered based
on Lipinski’s Rule of Five, use of no tautomers, removal of metals from salts and one stereoisomer per
ligand. These filtering criteria filtered out nearly 80% of the compounds from the original 2D databases
to generate 52,000 3D molecules. A new database was docked in thumb II allosteric pocket (NNI II
binding site) of NS5B polymerase, using the Glide’s High Throughput Virtual Screening workflow. This
second screening eliminated about 98% compounds and resulted in a set of remaining 650 molecules.
These molecules were analyzed in context of several parameters that experience for improbable docking
orientations in NNI II binding site of NS5B. Consequently, a set of 23 compounds bearing structural
diversity and good docking orientations for biological assessment was yielded. Next, an in vitro NS5B
RdRp inhibition assay was conducted, and two compounds were worth further SAR investigations.
Between these two molecules, only compound 31 (shown in Table 2) had the active analogs. Following the initial rhodanine hit (compound 31), commercially available rhodanine analogs were attained
for exploration of SAR around 3- and 5-position of the rhodanine scaffold, leading to the discovery of
potent analog (compound 32). Subsequently, more than twofold enhancement of inhibitory activity was
observed when 3-chlorobenzylidene was replaced with either 3,4-dichlorobenzylidene (compound 33) or
3-phenoxybenzylidene (compound 34), resulting in the identification of the two most potent compounds
from the in-house synthetic endeavours on rhodanine analogs. Compounds 33 and 34 were then docked
into each of the three HCV NS5B binding sites represented by NNI II, NNI III, and NNI IV. Further
analysis of compound 34 with the tetracyclic indole- and benzylidene- binding allosteric pockets (NNI
II and NNI IV, respectively) of NS5B pointed out topological similarities between these two pockets.
Compound 34, an innovative rhodanine analog with NS5B inhibitory potency in the low micromolar
level range may be a promising lead for future development of more potent NS5B inhibitors.
An interesting work was indicated in the recent publication of Li et al. Molecular dynamics simulations, free energy decomposition and docking were employed with the aim of investigating and comparing
the binding conformations of five different scaffold inhibitors (Li et al., 2010). Among many publicly
available X-ray structures of HCV NS5B polymerase complexed with different NNIs, five structures
(1YVF, 2GC8, 2GIQ, 2JC0, 2QE5) were chosen based on three critical points: the protein is HCV
NS5B polymerase genotype 1b, their NNIs all bind to the same palm I binding site, and these five NNIs
represent different scaffolds. These NNIs included acrylic acid derivatives, proline sulfonamide derivatives, thiadiazine derivatives, acyl pyrrolidine derivatives, and anthranilic acid derivatives (Figure 7).
Table 2. Potent NS5B inhibitors proposed by (Talele et al., 2010)
Compound R Ar IC50 (µM)
31 −CH
32 −CH(COOH)CH
33 −CH
34 −CH
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COOH 4-CF3Ph 55.2 ± 1.10
2
Ph 2,4-ClPh 10.6 ± 1.5
2
COOH 3,4-ClPh 8.4 ± 1.5
2
COOH 3-PhenoxyPh 7.7 ± 1.4
2
Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
Figure 7. Structures of the five NNIs
Proposed by (T. Li et al., 2010).
The binding free energies were calculated employing the Molecular Mechanics/ Generalized Born
Surface Area (MM/GBSA), a molecular dynamics simulation method. The results showed that Tyr448
plays the most critical role in the binding of most inhibitors mainly through strong hydrogen bonds.
Detailed analysis pointed out that major contributions which are beneficial to the binding are van der
Waals and electrostatic energies while polar solvation energies prevent the binding. Nonpolar solvation
energies contribute slightly favorably. Furthermore, several key residues for NNI II binding site were
identified by the free energy decomposition with the MM/GBSA method. In the following step, the authors investigated the feasibility of docking-based drug design in this binding site. The crystal structure
of protein, 2GC8, was used for further screening. After that, two diversity sets: NCI diversity set and the
HitFinder collection of Maybridge library were employed for virtually screening using the optimized
docking protocol. The top compounds were extracted and their first-ranked binding poses were submitted to hydrogen bonding analysis. Based on the results of hydrogen bonds, molecular dynamics and free
energy decomposition analysis, the authors suggested two criteria which can help to choose the candidate
hits. The first one was that there are at least two stable hydrogen bonds formed between the molecule
and two residues of Cys366, Ser367, Arg386, Tyr415, Gln446, Tyr448, Gln449, and Ser556. Another
criterion is that tightly binding interactions are located in the deep hydrophobic pocket surrounding by
Pro197, Arg200, Cys366, Met414 and Tyr448, due to a strong van de Waals or electrostatic interaction.
In a nut shell, there were different methods employed in drug design studies for HCV NS5B inhibitors,
including construction of quantitative structure-activity relationship models, structure-based and ligand-based
drug design. Although several potent NS5B inhibitors have been proposed in literature, much more efforts
are needed to win the goal of rapidly identifying more effective and less toxic HCV antiviral compounds.
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
NS3/NS4A PROTEASE
The complete genomes of various HCV isolates were cloned and sequenced by several research groups. The
structure of HCV is an enveloped, positive-strand RNA virus and its genome with a length of approximately
9.6 kilobases encodes a polypeptide precursor consisting of about 3010 amino acid residues. Cleavage of
the polypeptide precursor by host cell and two viral proteases produces individual viral structural (core,
envelope) and non-structural (NS) proteins. NS3 is a multifunctional enzyme with serine protease activity
in the N-terminal third of the protein and an RNA helicase/NTPase activity in the C-terminal part. The
serine protease domain of NS3, which requires an accessory viral protein NS4A for optimal processing, is
responsible for four of five cleavage events in the non-structural region of the HCV protein. The NS3/4a
protease complex has been identified as one of the most promising targets for anti-HCV therapy because
it demonstrates a vital role in the replication of the HCV virus and formation of infectous viral particles.
A 2.5Å resolution structure of the serine protease domain of the NS3 with its essential cofactor NS4A
indicated that the NS3/4a complex adopts a chymotrypsin/ trypsin-like fold with structurally conserved
region typical of small chymotrypsin-like proteases. An eight-stranded β-barrel motif, with one of the
strands made of the NS4A cofactor, is located in the N-terminal region of NS3/4a (residues 1-93 of NS3
and residues 21-34 of NS4A). The C-terminal region (residues 94-175) contains a six-stranded β-barrel
with a helix as an end. The active site, which is composed of His57, Asp81 and Ser139 as the catalytic
triads, located in a crevice between the two domains and formed by a shallow solvent exposed pocket
requiring many interaction points for substrates or inhibitors binding. Substrates are decapeptides with
4 residues on the C terminal side and 6 residues on the N terminal residues, described as NH
P4-P3-P2-P1-P1’-P2’-P3’-P4’-OH. The pockets on the HCV NS3/4a in which the P1 site chain interacts
would be called S1 pocket, and so forth for S2,S3, S4, S5, or S6 pockets and S1’, S2’, S3’, or S4’ pockets.
Hence, the NS3 protease exhibits substrate specificity that requires relatively large peptides spanning
the active site from S
and S4’ pocket. The catalytic site contains an oxyanion hole, which stabilizes the
6
hemiketal quaternary cleavage intermediate by hydrogen bonds with amide protons of the catalytic serine
residues Ser139 and glycine Gly137. Conserved features of the substrates recognized by the NS3 protease
include acidic residues in P
. Substrates and inhibitors typically bind to the active site of the NS3 in an extended conformation and
P
4’
and P5 positions, preference for cysteine in P1 and hydrophobic residues in
6
form an antiparallel β-sheet with the protease in one strand contributed by the inhibitors and the remaining strand contributed by the protease. Furthermore, a zinc ion is tetrahedrally cooridinated with Cys97,
Cys 99, Cys145 and His149 residues of a site located opposite to the active site. The zinc ion plays a
structural role due to the fact that the removal of the ion causes unfolding and precipitation of the protein.
-P6-P5-
2
CONVENTIONAL QSAR APPROACH IN DISCOVERY
FOR NS3/4A PROTEASE INHIBITORS
To date, several inhibitors of NS3/NS4A with high potency against viral replication have been developed,
and some of them have already undergone clinical trials, including boceprevir (SCH503034, phase III),
telaprevir (VX-950, phase III), narlaprevir (SCH900518, phase III), ciluprevir (BILN 2061), danoprevir
(ITMN-191), and vaniprevir (MK-7009). They all belong to peptidomimetic ketoamide group, whose
general structure is shown in Figure 8, and exhibit their action by binding reversibly to the NS3 active site. Malcolm et al. attempted to improve the potency of these derivates by synthesizing a series
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Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
of peptidomimetic ketoamide analogues. However, they have not done yet theorical structure-activity
relationship (SAR) of these NS3/NS4A inhibitors and guiding researchers to design better derivatives.
(Malcolm et al., 2006)
Later in 2012, Zhu et al. conducted quantitative structure-activity relationship (QSAR) analyses for a
set of 190 narlaprevir derivatives collected from reports by Njorore’s group, using comparative molecular field analysis (CoMFA), comparative molecular indices analysis (CoMSIA), hologram quantitative
structure-activity relationship (HQSAR) and molecular docking simulation (Zhu et al., 2012). Of these
compounds, the number of test set selected was 40 according to the values which arrayed in ascending
order at regular intervals to guarantee reasonable performance. After removing a compound as an outlier
due to its unreasonable low binding affinity, the final training set composed of 150 molecules. A rigid
alignment was applied to superimpose all 190 compounds onto a certain common substructure, and the
structure with the highest bioactivity was used as the template (Figure 9). With an aim at determining
the precise binding conformations of the studied compounds, these compounds were docked into the
binding cavity of NS3/NS4A. The best-docked conformations of all molecules were directly subjected
to CoMFA and CoMSIA analyses.
The fields calculated in the CoMFA study included: traditional steric (Lennard-Jones, 6-12 potential)
and electrostatic (Coulomb potential) fields, hydrogen bond donor and acceptor fields, and indicator
steric and electrostatic fields. Among the six charge models used to generate the partial charge of the
studied structures, the Gasteiger-Hückel model was identified as the best choice and used in the CoMFA
and CoMSIA analyses. Partial least square (PLS) was used to correlate these fields with the biological
activities, and was validated by leave one out cross validation. Eventually, the 3D-QSAR model was
developed by the regression analysis with an optimum number of components obtained from the crossvalidation results. The actual predictive power of the final model was then evaluated by the predictions
Figure 8. General structure of peptidomimetic ketoamide derivatives
Figure 9. Common substructure used for superimposing the compounds in the data set
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