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Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC Ji-Xia Ren a,b, Huo-Lian Qian a, Yu-Xin Huang a, Ning-Yu Zhu c, Shu-Yi Si c, Yong Xie a,n a Institute of Medicinal Plant Development, Chinese Academy of Medical Science & Peking Union Medical College, 151 Malianwa North Road, Haidian District, Beijing 100193, People’s Republic of China b College of Life Science, Liaocheng University, Liaocheng 252059, People’s Republic of China c Institute of Medicinal Biotechnology, Chinese Academy of Medical Science & Peking Union Medical College, Tian Tan Xi Li, Beijing 100050, People’s Republic of China

art ic l e i nf o

a b s t r a c t

Article history: Received 10 August 2014 Accepted 24 December 2014

Background: Tuberculosis remains one of the deadliest infectious diseases in humans. It has caused more than 100 million deaths since its discovery in 1882. Currently, more than 5 million people are infected with TB bacterium each year. The cell wall of Mycobacterium tuberculosis plays an important role in maintaining the ability of mycobacteria to survive in a hostile environment. Therefore, we report a virtual screening (VS) study aiming to identify novel inhibitors that simultaneously target RmlB and RmlC, which are two essential enzymes for the synthesis of the cell wall of M. tuberculosis. Methods: A hybrid VS method that combines drug-likeness prediction, pharmacophore modeling and molecular docking studies was used to indentify inhibitors targeting RmlB and RmlC. Results: The pharmacophore models HypoB and HypoC of RmlB inhibitors and RmlC inhibitors, respectively, were developed based on ligands complexing with their corresponding receptors. In total, 20 compounds with good absorption, distribution, metabolism, excretion, and toxicity properties were carefully selected using the hybird VS method. Discussion: We have established a hybrid VS method to discover novel inhibitors with new scaffolds. The molecular interactions of the selected potential inhibitors with the active-site residues are discussed in detail. These compounds will be further evaluated using biological activity assays and deserve consideration for further structure-activity relationship studies. & 2015 Published by Elsevier Ltd.

Keywords: RmlB RmlC ADMET properties Pharmacophore model Molecular docking Virtual screening

1. Introduction Tuberculosis (TB) is a chronic infectious disease caused by the pathogen Mycobacterium tuberculosis. In 2012, an estimated 8.6 million people developed TB, and 1.3 million infected patients died of the disease [1]. A major concern is the rise of drug-resistant TB. Thus far, no solid data on the extent of the spread of drugresistant TB is available [1]. TB remains a disease with an enormous impact on public health worldwide. The cell wall of M. tuberculosis plays an important role in the ability of mycobacteria to survive in a hostile environment [2]. The approved anti-TB drugs, isoniazid [3,4] and ethambutol [5], target the mycobacterial cell wall; and in recent years, great progress for treatment of drug resistant TB has been made by targeting the cell wall of M. tuberculosis [6–12]. Thus, the cell wall is a valid target for anti-

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Corresponding author. Tel.: þ 86 10 57833280; fax: þ 86 10 57833020. E-mail addresses: [email protected], [email protected] (Y. Xie).

mycobacterials. The cell wall of M. tuberculosis is composed of three layers: an innermost peptidoglycan layer, an outermost mycolic acid layer, and a connecting arabinogalactan polysaccharide layer [13]. An L-rhamnosyl residue plays an important role in connecting the arabinogalactan layer and the peptidoglycan layer [14]. The synthesis of rhamnose has been shown to be essential for mycobacterial cell growth [15], indicating that rhamnose synthetic enzymes are potential drug targets. Due to the potential therapeutic values against TB, the discovery inhibitors targeting the synthesis of rhamnose has increasingly attracted much attention in the past decade. Different inhibitors with novel scaffolds have been reported by several groups [16–19]. In spite of the great progress in developing inhibitors against rhamnose synthesis, no compound has yet entered clinical trials for treating both drug-susceptible TB and the increasingly common drug-resistant strains. Therefore, discovering more potent inhibitors against the synthesis of rhamnose, particularly those with novel chemical scaffolds, is still needed and important, and may provide more lead candidates for anti-TB drug

http://dx.doi.org/10.1016/j.compbiomed.2014.12.020 0010-4825/& 2015 Published by Elsevier Ltd.

Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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development. In this work, we report a virtual screening (VS) study aiming to identify novel inhibitors simultaneously targeting RmlB and RmlC, which are essential enzymes in the synthesis of dTDP-L-rhamnose [20]. All drug-like compounds with good absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were identified from original in silico databases by using Lipinski’s rule of five [21] and ADMET prediction. Two pharmacophore models, namely HypoB and HypoC, were built. HypoB was developed on the basis of the three-dimensional (3D) structure of RmlB–dTDP–D-glucose, which was built by homology modeling, while HypoC was established on the basis of the crystal structure of RmlC–dTDP-rhamnose. All drug-like compounds that simultaneously mapped both pharmacophore models were subjected to a docking study to identify novel compounds possessing potent inhibitory activity against RmlB and RmlC. Only compounds, that had high scores and showed good interactions with the residues in the active sites of RmlB and RmlC simultaneously, were selected. In this way, 20 compounds with good ADMET properties were carefully selected and shifted to the in vitro activity test. To the best of our knowledge, these results represent the first VS effort that aims to discover inhibitors targeting RmlB and RmlC simultaneously.

2. Materials and methods 2.1. Drug-likeness prediction Lipinski’s rule of five and ADMET prediction were used to filter drug-like compounds from the original commercially available databases “Diversity Libraries” (129 087 compounds; Life Chemicals Inc., Burlington, Canada). Compounds that passed the filters had a higher probability of good oral bioavailability. All compounds in the databases were minimized to the closest local minimum based on the Charmm force field in the Discovery Studio 3.1 program package (Accelrys Inc., San Diego, CA, USA). Compounds that passed the Lipinski’s rule of five were furgher assessed for ADMET properties prediction. ADMET studies were performed by using the ADMET predictor module within the Discovery Studio 3.1. ADMET descriptors, including aqueous solubility, blood–brain barrier penetration (BBB), cytochrome P450 (CYP450) 2D6 inhibition, hepatotoxicity, human intestinal absorption (HIA), and plasma-protein binding (PPB) were estimated for compounds that passed the Lipinski’s rule of five. 2.2. Homology modeling for the structure of mtRmlB–dTDP-Dglucose Since the crystal structure of M. tuberculosis RmlB (mtRmlB) is not available for the moment, we thus first built the structure of mtRmlB–dTDP-D-glucose by homology modeling. The amino acid sequence of the mtRmlB was obtained from UniProtKB (ID: O06329). In order to obtain the optimal template for homology modeling, a sequence similarity search was carried out using NCBI-BLAST server to screen against the Protein Data Bank (PDB) database. The crystal structure of RmlB–dTDP-D-glucose from Streptococcus suis (PDB code: 1KER) was chosen as the template [22], as its similarity with mtRmlB is greater than 75%. The sequence of mtRmlB was aligned to the template sequence by using the alignment method in the “Sequence Analysis” module of Discovery Studio 3.1, and the “Build Homology Models” module was used to generate mtRmlB–dTDP-D-glucose coordinates. The coordinates of ligands that form complexes with the protein in the crystal structure (PDB code: 1KER) were copied during the homology modeling. The model with the lowest probability density functions (PDF) total energy was selected and subjected

to energy minimization by using the conjugated gradient method with 20000 step interations, until the maximum the derivative became less than 0.01 kJ/(mol Å). 2.3. Pharmacophore modeling The “Feature Mapping” protocol in the Discovery Studio 3.1 was employed for pharmacophore modeling. The pharmacophore model of RmlC inhibitors in this study was developed based on the crystal structure of the RmlC–dTDP-rhamnose complex obtained from RCSB PDB (PDB code: 2IXC) [23], while the pharmacophore model of RmlB inhibitors was established based on the 3D structure of the RmlB–dTDP-D-glucose complex developed using homology modeling. 2.4. Database screening based on pharmacophore models The established pharmacophore models were employed as 3D structural queries to screen the commercially available chemical databases “Diversity Libraries” including 129 087 compounds, using the “Search 3D Database” protocol in Discovery Studio 3.1. All queries were performed using the best search method, and all mapped conformations were saved. Only those compounds that mapped at least half number of pharmacophore features were chosen. 2.5. Molecular docking All the docking studies were performed by GOLD 4.0 [24], which adopts the genetic algorithm to dock flexible ligands into protein binding sites. The 3D structures of RmlC–dTDP–rhamnose (PDB code: 2IXC) determined by crystallized and RmlB–dTDP-Dglucose built by homology modeling were used in the docking study. All water molecules were removed, and hydrogen atoms were added to the protein by using Discovery Studio 3.1. The Charmm force field was assigned. The binding site was defined as a sphere containing the residues that stay within 9 Å from the ligand, which is large enough to cover the ligand binding region at the active site. The scoring functions and docking parameters were optimized in advance by docking the ligands complexed with the proteins back to the active site of their receptors. We adjusted the docking parameters and scoring functions until the docked structures were as close as possible to their original crystallized structures (Fig. S1); the root mean square deviation (RMSD) values between the docked and crystallized conformations for the dTDPrhamnose and the dTDP-D-glucose were both lower than 1.5 Å. Except that the genetic algorithm (GA) parameter was set to “GOLD Default”, the finally optimized docking parameters for RmlC were kept the same as their default settings; the ASP fitness function was selected. The finally optimized docking parameters for RmlB are as the same as the parameters used in the docking study for RmlC, except that the Goldscore fitness function was used.

3. Results and discussion 3.1. Drug-likeness prediction In total, 126 104 compounds from “Diversity Libraries” (129 087 compounds) passed the Lipinski’s rule of five. Most of the drugs in development failed during clinical trials due to poor pharmacokinetics parameters. These properties such as absorption, distribution, metabolism, excretion and toxicity (ADMET) play an important role in drug discovery, development, and safety. ADMET prediction can help eliminate compounds with unfavorable

Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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druggability properties and avoid expensive reformulation later. The following six ADMET properties included in Discovery Studio 3.1 were calculated for the compounds in the study: the aqueous solubility is the solubility of each compound in water at 25 1C; the human intestinal absorption descriptor indicates the absorption of the orally administered drugs in the intestine; the blood–brain barrier (BBB) defines the penetration of the molecules into the central nervous system after oral administration; the cytochrome P450 2D6 inhibition predicts the inhibition of the enzyme using the 2D chemical structure as an input; hepatotoxicity models predict the hepatotoxicity of the compounds; and plasma-protein binding (PPB) illustrates the degree of drug binding to the proteins within blood plasma. In the second level of drug-likeness screening, ADMET properties were calculated for every hit compounds. At this screening level, the compounds with good absorption, optimal solubility, non-hepatotoxicity, different levels of BBB and PPB were identified and considered further. Compounds predicted to be CYP2D6 inhibitors or non-inhibitors were all selected for further study. 19 984 compounds with good ADMET properties passed the ADMET prediction study.

3.2. Homology modeling for the structure of mtRmlB–dTDP-Dglucose As mentioned above, the synthetic enzymes, RmlA, RmlB, RmlC and RmlD involved in the synthesis of rhamnose, are potential drug targets for the treatment of TB. It has been found that RmlB and RmlC are essential for the synthesis of dTDP-L-rhamnose; therefore, we proposed that inhibitors targeting RmlB and RmlC simultaneously may possess a better inhibitory effect than inhibitors targeting only one of these enzymes. In this study, we used pharmacophore-based virtual screening to discover inhibitors targeting both RmlB and RmlC. The pharmacophores employed in the study were derived from the receptor–ligand complexes. Fortunately, the crystal structure of RmlC–dTDP–rhamnose has been reported [23]. However, the crystal structure of the RmlB– ligand has not been reported, and was built in this study by homology modeling. In order to build the pharmacophore model of RmlB inhibitors and perform the docking study, we first needed to build the structure of mtRmlB–dTDP-D-glucose by homology modeling. The template used for the homology modeling was the crystal structure of RmlB–dTDP-D-glucose from Streptococcus suis (PDB code: 1KER), whose similarity with mtRmlB is grater than

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75% (Fig. S2). The best homology model of mtRmlB–dTDP-Dglucose established corresponds to the one with the least PDF total energy ( 8853.5244), and its DOPE (Discrete Optimized Potential Energy) score is  37697.187500. The model was evaluated by Profiles-3D. The calculated “verify score” for each of residues in the mtRmlB–dTDP-D-glucose model is shown in Fig. 1A. Obviously most of the residues (99.4%) have a score large than zero, implying rational structures for these residues. We also notice that there are two residues, Arg159 and Asn97, whose verify scores are less than zero; but they locate far from the active site of mtRmlB–dTDP-D-glucose. The established mtRmlB–dTDP-Dglucose model was further evaluated by the Ramachandran plot, which is often used to check the correctness of predicted torsion angles of residues in proteins. Fig. 1B presents the Ramachandran plot of the homology model of mtRmlB–dTDP-D-glucose. We can see that only few of residues, 6 out of 331 residues (1.8%), locate in the region of unfavorable torsion angles; this should not have significant influence on our phrmacophore modeling and docking studies since they are far from the active site of mtRmlB–dTDP-Dglucose. Overall, the validation results demonstrate that the established homology model of mtRmlB–dTDP-D-glucose should have a reasonable structure, and could be used in the following studies.

3.3. Structural basis for discovering inhibitors targeting RmlB and RmlC simultaneously The active sites of RmlB and RmlC were analyzed by selecting neighbors located within 9 Å around the ligands bound to the active sites. The active sites of both RmlB and RmlC can be divided into four regions (Fig. 2): the pyrimidine-2,4(1H,3H)-dione binding region (PDR); tetrahydrofuran-3-ol binding region (TDR); phosphate groups binding region (PR); and hexose ring binding region (HR). Tyr204, Gln191, Arg190 and Lys202 compose the PDR region of RmlB, Tyr204 and Gln191 are responsible for π–π stacking and forming H-bonding with pyrimidine-2,4(1H,3H)-dione. The PDR region of RmlC is composed of Tyr138, Gln47 and Asn49, Tyr138 makes π–π stacking with pyrimidine and Asn49 forms a H-bonding with one oxygen atom of pyrimidine-2,4(1H,3H)-dione. There are no residues in the vicinity ofthe TDR region of RmlC, while Asp84, Asn246 and His273 are located in the vicinity of TDR region of RmlB. Arg residues play an important role in composing the PR region, Arg211 and Arg270 compose the PR region of RmlB, while

Fig. 1. (A) Calculated verify scores for residues of mtRmlB–dTDP-D-glucose model by Profiles-3D. (B) The Ramachandran plot for the mtRmlB–dTDP-D-glucose model. Residues that locate in favored region are shown in green, while the residues locate in the region of unfavorable torsion angles is shown in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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the PR region of RmlC consists of Arg59 and Arg170. These Arg residues are responsible for making charge–charge interactions with phosphate groups. His62, Lys72 and His119 compose the HR region of RmlC, and all three residues form H-bondings with the oxygen atoms of the hexose ring. Ser81, Thr120, Asp121, Tyr147and Lys186 compose the HR region of RmlB, all these residues form H-bondings with the oxygen atoms of the hexose ring. In summary, the active sites of both RmlB and RmlC can be divided into four regions, in active sites of both RmlB and RmlC, pyrimidine-2,4(1H,3H)-dione is responsible for π–π stacking with Tyr, and forms H-bondings with Gln (in RmlB) or Asn (in RmlC) in the PDR region. The Arg residues in the PR region form charge– charge interactions with phosphate groups. The oxygen atoms in the hexose ring form H-bondings with the residues composing the HR region. The common interactions between ligands and their receptors (RmlB or RmlC) provide a structural basis for designing inhibitors targeting RmlB and RmlC simultaneously.

3.4. Development and validation of the pharmacophore models Because the reported RmlB and RmlC inhibitors are limited in number and structural diversity, building a 3D quantitative

structure–activity relationship pharmacophore model or common feature pharmacophore is difficult. We first tried to build pharmacophore models using the “Receptor–Ligand Pharmacophore Generation” module in Discovery Studio 3.1. Unfortunately, this module could not build any pharmacophore models based on the RmlB–ligand complex or RmlC–ligand complex. Therefore, in this study, the pharmacophore models were first built based on the ligands complexed with the receptor, and then the pharmacophore models were further optimized according to the actual interactions between the ligands and their receptors. The pharmacophore model of RmlC inhibitors was derived from the crystal structure of dTDP-rhamnose complexed with RmlC, while the pharmacophore model of RmlB inhibitors was developed based on the 3D structure of dTDP-D-glucose bound to the active site of RmlB. In the first step, each ligand was taken out from its corresponding protein–ligand structure with the ligand active conformation being maintained. The “Feature Mapping” protocol in Discovery Studio was then performed to identify all the possible chemical features of each ligand, the results are shown in Fig. S3. As not all of the recognized chemical features for each ligand correctly reflected the interactions between the ligand and the receptor, we thus removed the non-matching features according to the actual interactions between the ligands and their receptors.

Fig. 2. Binding patterns of ligands to their receptors. (A) Binding pattern of dTDP-D-glucose to the active site of RmlB. (B) Binding pattern of dTDP-rhamnose to the active site of RmlC. The binding pockets of the receptors consist of four parts: PDR region, TDR region, PR region, and HR region. Blue arrow: hydrogen bond acceptor; Green arrow: hydrogen bond donor; Magenta arrow: charge–charge interaction; Orange solid line: π–π stacking interactions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. The established pharmacophore model HypoB (A) and HypoC (B). The features are color coded: green, hydrogen-bond acceptor; magenta, hydrogen-bond donor; orange, ring-aromatic feature; dark blue, neg_ionizable feature. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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Fig. 4. (A) Mapping of HypoC with RmlB–dTDP-D-glucose complex. (B) Mapping of HypoB with RmlC–dTDP–rhamnose complex. The key residues residing in the interaction region between ligands and receptors are labeled and shown as sticks. The location spheres of pharmacophore features are ignored for clarity. Dashed lines represent hydrogen bonds; orange solid lines represent π–π stacking interactions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

The final pharmacophore model of RmlB inhibitors, termed as HypoB, contains five features, including two hydrogen-bond acceptor features (HBA), a hydrogen-bond donor feature (HBD), a neg_ionizable feature (NI), and a ring aromatic (RA) feature (Fig. 3A). Fig. 4A shows the map of HypoB in the active site of mtRmlB–ligand complex. The final pharmacophore model of RmlC inhibitors, termed as HypoC, contains six features, including two hydrogen-bond acceptor features (HBA), two hydrogen-bond donor features (HBD), a neg_ionizable feature (NI), and a ring aromatic (RA) feature (Fig. 3B). Fig. 4B shows the map of HypoC in the active site of RmlC–ligand complex. From Fig. 4 we can see that the pharmacophore models developed from the ligands can correctly reflect the interactions between the ligands and their receptors. The test set method can reveal whether the pharmacophore model differentiates between inhibitors and noninhibitors. In this study, we used the test set method to validate the pharmacophore models. Fortunately, we collected a total of 29 RmlC inhibitors from different literature resources [17,18]. However, no RmlB inhibitors were found in the literature resources, therefore the test set method could not be carried out for the pharmacophore model HypoB. A test set containing 29 known RmlC inhibitors and 6172 decoys from the DrugBank database [25] was used to validate the pharmacophore HypoC. Each of the test set compounds was mapped onto HypoC. Of the 29 RmlC inhibitors, 15 were found to map very well with HypoC (fitnessu2.0), indicating a yield of 78.95%, and 97 of the 6172 (22.73%) decoys also mapped very well with HypoC, indicating a hit rate of 13.39% and an inrichment factor of 28.64. These results reveal two facts: (1) HypoC is capable of discriminating, to some extent, between RmlC inhibitors and non-inhibitors; and (2) Virtual screening by using a pharmacophore model alone may suffer from a high false positive rate, which is one of the most important reasons why we adopted the hybrid virtual screening method that combines drug-likeness prediction, pharmacophore model and molecular docking studies here. 3.5. Comparison of HypoB and HypoC In order to check whether the established pharmacophore models HypoB and HypoC could identify inhibitors that simultaneously targeted RmlB and RmlC, we compared the pharmacophore models in terms of their properties and locations in the 3D

Fig. 5. Superposition of mappings of HypoB–dTDP–D-glucose and HypoC–dTDP– rhamnose. The common features, RA feature and HDA feature, locating at the PDR region are encircled by a blue circle. The partially common feature, NI feature, residing in the PR region is encircled by a purple circle. The distance between the two NI features is 3.822 Å. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

space. HypoB and HypoC were first mapped onto dTDP-D-glucose and dTDP-rhamnose, respectively. The two pharmacophore–ligand maps were then superimposed (Fig. 5). Features with the same property and location in 3D space were identified as common features. As can be seen from Fig. 5, the ring aromatic features and hydrogen acceptor features in the PDR region are almost overlapping, implying that features located in the PDR region are common features to RmlB and RmlC inhibitors. The distance between the two NI features in the PR region is only 3.822 Å, which implies that the NI features are partially common between RmlB and RmlC inhibitors. The remaining hydrogen bond features of HypoB and HypoC are far away from each other and locate in their corresponding HR region, which means that they are targetspecific features of RmlB and RmlC inhibitors. In conclusion, the pharmacophore models HypoB and HypoC can be mainly divided into two parts; one part contains the chemical features common to both RmlB inhibitors and RmlC inhibitors, and the other part is composed of the target-specific chemical features corresponding to RmlB and RmlC inhibitors.

Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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Compounds that passed both HypoB- and HypoC-based screening had the ability to bind to the active sites of RmlB and RmlC via different binding poses, suggesting that these compounds can inhibit RmlB and RmlC simultaneously. 3.6. Virtual screening for the discovery of novel RmlB and RmlC inhibitors The combined VS (drug-likeness prediction þpharmacophoreþdocking) was carried out for the discovery of novel inhibitors since it can assess information derived from ligands and receptors. In order to quickly and more accurately discover inhibitors targeting RmlB and RmlC (Fig. 6), drug-likeness prediction was carried out first. Next, the faster screening method (Pharmacophore Based-VS) was used to screen the drug-like compounds.

Fig. 6. A hybrid VS protocol based on drug-likeness prediction, pharmacophore models, and molecular docking was applied to identify novel inhibitors targeting RmlB and RmlC.

Finally the interactions between ligand and active site were further considered in the VS process by the aid of a docking study. In total, 19 984 compounds from the original in silico databases were predicted to be drug-like hits. The pharmacophore models established in this study (HypoB and HypoC) were employed as 3D search queries for retrieving potent drug-like inhibitors targeting RmlB and RmlC. Only those compounds that mapped onto the pharmacophore models HypoB and HypoC simultaneously were selected. Thus, 93 compounds were yielded as hits from the pharmacophore-based screening. These compounds then underwent the third filtering process, namely, the docking-based VS. The structure of RmlB obtained by homology modeling was chosen as the reference protein, and the Goldscore fitness function available in GOLD suite was employed for sorting compounds in the RmlB as the receptor docking study. The 3D structure of RmlC was taken from the crystal structure of RmlC–dTDP–rhamnose (PDB code: 2IXC) and used as a reference protein. The ASP fitness function was used for sorting compounds in the RmlC as the receptor docking study. 20 compounds were selected according to their scores and whether they retained some important interactions with the active sites of the receptors, for example, interactions with residues in the active site of RmlB, such as Try204, Gln191, Arg211, Arg270, Lys186 and Thr120; and interactions with residues in the active site of RmlC, such as Tyr138, Asn49, Arg59, Arg170, Lys72, His119 and His62. Table 1 shows the ADMET properties for the 20 hits that were finally selected; and the chemical structures of these compounds are shown in Fig. S4. The representative structures of these promising inhibitors selected by the combined screening method mainly include neg_ionizable moiety, ring-aromatic moiety, hydrogen bond donor feature and hydrogen bond acceptor feature. Furthermore, the ring-aromatic moiety of the inhibitors forms π–π stacking with the phenyl ring of Tyr residue in the PDR region of the receptors. One hydrogen bond acceptor feature of the inhibitors is responsible for making a H-binding interaction with the Gln or Asn in the PDR region of the receptors. The neg_ionizable moiety makes charge– charge interactions with the side chains of Arg residues located in the PR region. The remaining hydrogen bond donor feature and hydrogen bond acceptor feature of the inhibitors form H-bondings

Table 1 Predicted ADMET properties for the finally selected 20 hit compounds. Compound

Absorption

Solubility

BBB

CYD2D6

Hepatotoxicity

PPB

AlogP98

PSA_2D

cpd1 cpd2 cpd3 cpd4 cpd5 cpd6 cpd7 cpd8 cpd9 cpd10 cpd11 cpd12 cpd13 cpd14 cpd15 cpd16 cpd17 cpd18 cpd19 cpd20

0 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1

4 3 4 4 4 4 4 4 4 4 4 4 5 4 4 4 4 4 3 4

4 4 4 4 4 4 4 4 4 4 3 4 4 4 4 3 4 3 4 4

False False False False False False False False False False False False False False False False False False False False

False False False False False False False False False False False False False False False False False False False False

False True False False False False False False False False False False False False False False False False False False

 2.609  0.669  2.107  2.614  2.564  2.929  2.614  3.123  2.564  2.929  2.583  3.41  3.776  1.867  2.773  2.198  3.106  2.547 3.047  2.699

112.902 121.832 132.79 119.713 111.18 119.713 119.713 128.643 111.18 119.713 107.827 123.431 131.964 132.79 114.135 102.25 119.713 102.25 105.816 136.143

ADMET_absorption_level refers to intestinal absorption: 0 (good), 1 (moderate), 2 (poor), 3 (very poor); ADMET_solubility_level refers to aqueous solubility: 0 (extremely low), 1 (no, very low, but possible), 2 (yes, low), 3 (yes, good), 4 (yes, optimal), 5 (no, too soluble); ADMET_BBB_level refers to blood brain barrier (BBB) penetration: 0 (very high penetrant), 1 (high), 2 (medium), 3 (low), 4 (undefined); ADMET_CYP2D6 refers to cytochrome P450 2D6 enzyme inhibition: false (non-inhibitor), true (inhibitor); ADMET_hepatotoxicity refers to hepatotoxicity: false (non-toxic), true (toxic); ADMET_PPB_level refers to plasma protein binding: false (non-binding), true (binding); AlogP98 refers to atom-based LogP; PSA_2D refers to polar surface area.

Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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Fig. 7. (A) One of the final hit compounds, F2721-0094, mapped with the pharmacophore model HypoB. The features are color coded: green, hydrogen-bond acceptor; magenta, hydrogen-bond donor; orange, ring-aromatic feature; dark blue, neg_ionizable feature. (B) The binding modes of F2721-0094 with RmlB. Dashed lines represent hydrogen bonds; orange solid lines represent π–π stacking interactions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

to the residues composing the HR region. For example, the pharmacophore mappings and binding modes of one of the best retrieved compounds, F2721-0094, will be discussed as follows. As can be seen from Fig. 7A, the hit compound F2721-0094 mapped well with these features of the pharmacophore model HypoB. The conformations of F2721-0094 mapping onto HypoB can be defined as two clusters according to the mapping poses of the hydroxyl group. In one cluster, the hydroxyl group mapped onto the hydrogen acceptor feature; in the other cluster, the hydroxyl group mapped onto the hydrogen donor feature. From Fig. 8A we can see that the features of the pharmacophore model HypoC map well with F2721-0094, except that the partially common feature NI of HypoC is not mapped very well onto the carboxyl group of F2721-0094, but the distance between the carboxyl group and the NI feature is very close. As a result the carboxyl group can interact with Arg170, and but not with Arg59 (Fig. 8B). The binding poses of F2721-0094 in the active pockets of RmlB and RmlC are also shown in Figs. 7B and 8B. The 4-methoxyphenyl moiety (ringaromatic feature, one common feature) forms π–π stacking with the phenyl ring of Tyr204 in RmlB and Tyr138 in RmlC, respectively. The oxygen atom of the 4-methoxyphenyl moiety (hydrogen accepter feature, one common feature) forms a H-bonding with the sidechian of Gln191 in RmlB; the H-bonding formed between the oxygen atom of N-methylacetamide moiety and Asn49 of RmlC corresponds to the same hydrogen accepter feature. The carboxyl group (neg_ionizable feature, a partially common feature) makes charge–charge interactions with the side chains of Arg residues, Arg211 and Arg270 in RmlB, and Arg170 in RmlC. The target-specific features of HypoB and HypoC, including hydrogen bond acceptor features and hydrogen bond donor features, correspond to the H-bondings formed between F27210094 and the residues composing the HR regions o f RmlB and RmlC, respectively.

Fig. 8. (A) F2721-0094 mapped with the pharmacophore model HypoC. The features are color coded: green, hydrogen-bond acceptor; magenta, hydrogenbond donor; orange, ring-aromatic feature; dark blue, neg_ionizable feature. (B) The binding modes of F2721-0094 with RmlC. Dashed lines represent hydrogen bonds; orange solid lines represent π–π stacking interactions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4. Conclusions In this study, a hybrid VS method including drug-likeness prediction, pharmacophore modeling and molecular docking was applied to identify inhibitors targeting RmlB and RmlC simultaneously. We first developed pharmacophore models of RmlB and RmlC inhibitors, which had both common chemical features and target-specific chemical features. The common chemical features include ring aromatic features and hydrogen acceptor features locat in the PDR region. There are also partially common features, i.e., the neg_ionizable features, in the PR region. The target-specific chemical features corresponding to RmlB inhibitors and RmlC inhibitors mainly locate in their corresponding HR region. The results of the mappings of the pharmacophore models, HypoB and HypoC, onto the receptor–ligand complexes indicate that the models can correctly reflect the interactions between ligands and their receptors. In addition, HypoC shows a yield rate of 78.95%, a hit rate of 13.39% and an enrichment factor of 28.64 in the test set method. These two models were then used to screen drug-like compounds with good ADMET properties for the identification of potential new inhibitors of M. tuberculosis cell wall synthesis. Then, a molecular docking method was used to further filter the screened compounds. Finally, 20 potential active compounds showing good ADMET properties and interactions with the active sites of the receptors were carefully selected from the final

Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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hits and have been purchased to complete the follow-up activity test. [10]

Conflict of interest statement We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

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Acknowledgments

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This work was supported by the China Postdoctoral Science Foundation (no. 2013M540886 to JXR), the National Natural Science Foundation of China (no. 81273432 to YX), and Program for New Century Excellent Talents in University, the Fundamental Research Funds for the Central Universities, and PUMC Youth Fund (no. 3332013124 to YX).

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Appendix A. Supporting information

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Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.compbiomed. 2014.12.020. [17]

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Please cite this article as: J.-X. Ren, et al., Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: Inhibitors targeting RmlB and RmlC, Comput. Biol. Med. (2015), http://dx.doi.org/10.1016/j.compbiomed.2014.12.020i

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