Tuberculosis (TB) is a severe respiratory infectious disease caused by Mycobacterium tuberculosis [1, 2]. The End TB Strategy by the World Health Organization aims to end the global TB epidemic as part of the Sustainable Development Goals. However, according to the World Health Organization global tuberculosis report 2021, over 10 million cases of TB were registered in 2019. Despite the remarkable progress in TB treatment, the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB has rendered the current antimycobacterial drugs ineffective. Furthermore, mutations resulting in resistance to isoniazid, the representative first-line anti-TB drug, have been found in up to 82% of clinical cases of XDR TB [3]. The rapid spread of MDR TB and XDR TB has resulted in the urgent need for developing antituberculosis drugs.

The system concerning mycomembrane biosynthesis, a unique component of the mycobacterial cell wall, has been recognized as a promising target for developing anti-TB drugs [4]. Mycolic acid, the main component of mycomembrane, is an essential material for mycobacterial cellular architecture and impermeability [5]. The proteins encoded by the fadD32–pks13–accD4 operon catalyze the last steps in the biosynthesis of mycolic acids [6]. Among the gene products, the polyketide synthase 13 (Pks13) catalyzes the synthesis of mycolic acid precursors, α-alkyl β-ketoacids, in a coupled reaction with FadD32 via the condensation of two long-chain fatty acid derivatives [7]. The thioesterase domain of Pks13 plays an essential role in lipid transport, an essential function in mycobacteria [8]. Therefore, Pks13 is a qualified drug target for M. tuberculosis, and recent studies have demonstrated that the small compounds targeting Pks13 could function as potential cell wall-active anti-TB agents [9,10,11,12,13,14].

The hurdles concerning the emergence of drug-resistant TB strains have necessitated the rapid and continuous development of novel anti-TB drugs. Drug discovery by in silico structure-based drug screening (SBDS) could provide a solution to the aforementioned problems because this technique facilitates the rapid identification of lead compounds compared with the ordinal screening strategy based on biological assays [15, 16]. Thus far, novel inhibitors for various enzymes (e.g., enoyl-acyl carrier protein reductase [17,18,19], mycobacterial cyclopropane mycolic acid synthase 1 [20], and 7, 8-diaminopelargonic acid synthase [21]) that are essential for mycobacterial survival have been identified through SBDS. Recently, we improved the SBDS strategy by utilizing two genetic algorithm-based programs from the perspective of effective lead compound identification [22]. The method is referred to as parallel compound screening (PCS). In this study, the PCS was applied to identify novel inhibitors targeting M. tuberculosis Pks13.

Experimental procedures

Preparation of protein and compound structures

The three-dimensional structural data of the chemicals (154,118 compounds) supplied by ChemBridge (San Diego, CA, USA) was obtained from the Ressource Parisienne en Bioinformatique Structurale (Paris, France). The protonation states were adjusted at pH 7.0, and multiple conformations of the compounds were generated using the Protonate 3D module and the conformation search module in the Molecular Operating Environment (Chemical Computing Group, Montreal, Canada), respectively. The molecular surface of the docking pocket was generated from the X-ray structural data of M. tuberculosis Pks13 thioesterase domain in complex with TAM16 (PDBid 5V3Y, the resolution is 1.98 Å) because a strong efficacy of TAM16 was reported in the murine TB model [13, 14]. Briefly, the hydrogen atoms and the partial charges were assigned to the protein structure with the Dock Prep tool in UCSF’s Chimera software (San Francisco, CA, USA) after removing the TAM16 structure from the active site. Finally, the molecular surface of the structure was calculated using the DMS program, and the docking pocket was determined using the SPHGEN program.

Docking simulation

The multistep in silico SBDS was performed using a combination of three docking programs, i.e., UCSF DOCK v.6.4 (DOCK; San Francisco, CA, USA) [23], GOLD suite v.5.0.1 (GOLD; Cambridge Crystallographic Data Center, Cambridge, UK) [24], and AutoDock Vina (ADV; Center for Computational Structural Biology, La Jolla, CA, USA) [25]. First, the compound library was screened using the DOCK program to select the top 2000 compounds with a binding energy of less than −50 kcal mol−1. The selected compounds were subjected to PCS (i.e., compounds were screened by GOLD and ADV programs individually), and acquired binding scores (GOLD and ADV scores) were individually ranked. Among the ranking lists, the top 60 coidentified compounds were extracted. Finally, the selected compounds were again subjected to the docking simulation by the GOLD program to evaluate their virtual affinity to the irrelevant target, M. tuberculosis l,d-transpeptidase LdtMt2. Herein, 43 compounds with 60 or more GOLD scores were excluded as false positives. According to Lipinski’s rule of five [26], seven top-ranked compounds (17) were selected for experimental validation after filtering. Seven candidate compounds were purchased from ChemBridge (

Selection of active compounds in compound (4/7) analogs

Structurally related compounds (compounds 4 and 7 (4/7)) were selected based on the Molecular ACCess System (MACCS) keys fingerprint (166 bit) [27] from the chemical library of 1.3 million compounds provided by Hit2Lead ( The compounds with the Tanimoto coefficient ≥0.60 were extracted and subjected to PCS. After acquiring GOLD and ADV scores, five compounds (A1A5) were selected. Drug-likeness of the compounds was confirmed based on Lipinski’s rule of five.

Antimycobacterial activity measurement

Mycobacterium smegmatis (IAM 12065 strain, RIKEN BioResource Center, Saitama, Japan) was grown overnight at 37 °C and 240 rpm in a 3.7% brain–heart infusion broth (Sigma, St. Louis, MO, USA). The cultures were diluted 40-fold with broth containing the test compounds before being aliquoted into 96-well flat-bottom clear plates with a total volume of 200 μλ. The plates were incubated at 37 °C for 24 h, after which the cultures were subjected to growth inhibition assays. OD595 was measured using a Bio-Rad Model 680 microplate reader (Bio-Rad, Hercules, CA, USA) to determine mycobacterial growth inhibition. All experiments were performed in quadruplicate, and significance was evaluated using Dunnett’s test.

Antimicrobial activity measurement

Escherichia coli JM109 strains (Toyobo, Osaka, Japan) were grown overnight at 37 °C and 240 rpm in LB broth (Sigma). The cultures were diluted with the test compounds containing broth in 96-well plates. The final concentration of the test compounds was 100 μM. The bacterial growth was evaluated by OD595 after incubation at 37 °C for 8 h. All experiments were performed in quadruplicate.

Molecular dynamics simulation

The structure of M. tuberculosis Pks13 complex with the compound A2, predicted by the GOLD program was subjected to the molecular dynamics (MD) simulation. The GROMACS package with the CHARMM36m force field was used as the MD simulation tool ( The simulation system composed of proteins, compounds, water molecules, and ions was assembled with the CHARMM-GUI web server (, where TIP3P was used as a water molecule. The cutoff value of 1.2 nm was used as an interatomic distance for the van der Waals force and electrostatic interaction. The particle mesh Ewald method was used to calculate long-range electrostatic interactions. The LINCS constraint algorithm was used for the energy minimization, equilibration, and production MD calculations. Energy minimization calculations were conducted in up to 5,000 steps using the steepest descent algorithm. The equilibration calculations were conducted in one step under the NVT conditions (310.00 K), followed by two steps under NPT conditions (310.00 K, 1 bar). Finally, 50 ns production MD calculations were performed with a time step of 2 fs. MD trajectories were analyzed using g_rms in the GROMACS package.


Identification of lead compounds by PCS assisted in silico SBDS

The binding pocket structure used in the docking simulations is shown in the upper panel of Fig. 1a. The cavity was prepared based on the three-dimensional structure of M. tuberculosis Pks13 after subtraction of the TAM16 inhibitor to select a competitive inhibitor (Fig. 1a lower panel). The in silico SBDS strategy for the lead compound identification is shown in Fig. 1b. First, the DOCK program rapidly selected the superior compounds (2000) with high binding energy as a high-throughput primary screening step. The PCS, screening by GOLD and ADV programs in parallel, were performed as a secondary screening. Seven compounds referred to as 17 were eventually selected from the candidate pool with high GOLD and ADV scores after excluding false positives and filtering based on Lipinski’s rule of five. The compound names, ChemBridge IDs, GOLD scores, and ADV scores are summarized in Table 1.

Fig. 1
figure 1

Identification of the lead compounds as M. tuberculosis Pks13 inhibitors. a The drug-binding cavity for docking simulation in van der Waals molecular surface of M. tuberculosis Pks13 is represented as spheres. The structure was built from X-ray structural data of M. tuberculosis Pks13 thioesterase domain in complex with TAM16 (PDBid 5V3Y), where TAM16 is presented by a ball-and-stick model (lower panel). b Schematic representation of the process in lead compound identification by in silico SBDS with the assistance of the PCS

Table 1 Compounds identified by in silico SBDS (compounds 1–7)

The inhibitory effects of compounds 17 on the growth of M. smegmatis were validated experimentally (Fig. 2). Nonpathogenic M. smegmatis (biosafety level 1) has been commonly used in studies on the Mycobacterium genus due to its fast growth and shared features with M. tuberculosis, including the mycolic acid biosynthesis system [28]. Figure S1, the thioesterase domain of M. smegmatis Pks13, shares a high similarity with the amino acid sequence of M. tuberculosis Pks13. Compounds 3, 4, 5, and 7 showed significant antimycobacterial activity, and compounds 4 and 7 completely suppressed mycobacterial growth. The lower panel in Fig. 2 showed that compounds 4/7 were structurally related.

Fig. 2
figure 2

Inhibitory effect of selected compounds (17, 100 µM) on M. smegmatis growth (upper panel) and the chemical structures of the bioactive compounds (lower panel). The vehicle is 0.3% DMSO and INH represents 100 µM isoniazid, respectively (****P < 0.0001, *P < 0.0021, and n.s. not significant)

Identification of bioactive analogs of compounds 4/7

The PCS was applied for structurally related bioactive compound screening to obtain additional bioactive analogs of the promising leads (4/7; Fig. 3a). In brief, the structurally related 2245 analogs of compounds 4/7 were initially extracted based on the Tanimoto coefficient with 0.60 or more from the chemical library; the potential inhibitors were then selected using the PCS. The selected five analogs are compounds A1A5 (Table 2 and Fig. 3b). All analogs significantly inhibited the M. smegmatis growth at 100 μM, especially the compounds A1, A2, and A5, which showed a potent inhibitory effect (Fig. 3c). Figure 4 shows that the IC50 value of compound A2 in mycobacterial growth inhibition (8.2 μM) was comparable to isoniazid (5.4 μM) [21]. The IC50 values of other compounds which showed potent antimycobacterial activity (4, 7, A1, and A5) are shown in Fig. S2.

Fig. 3
figure 3

Identification of bioactive analogs of the compounds 4/7. a The schematic represents the identification of bioactive analogs of the compounds 4/7. b The chemical structures of the selected analogs are referred to as compounds A1A5. c Inhibitory effects of the compounds (100 µM) on the M. smegmatis growth. The vehicle is 0.3% DMSO and INH represents 100 µM isoniazid (****P < 0.0001)

Table 2 The structurally related analogs of compounds 4/7
Fig. 4
figure 4

Concentration dependence of the compound A2 on the M. smegmatis growth. The IC50 value was determined by a nonlinear regression method

The microorganisms without the mycomembrane biosynthesis system are presumed to be not affected by treatment with Pks13 inhibitors. Therefore, the antimicrobial activity of the compounds 4/7, A1, A2, and A5 against E. coli (JM109), representative gram-negative bacteria, was examined. None of the compounds exhibited any significant antimicrobial activity (Fig. 5), and the result supported that the antimycobacterial activity was exerted by Pks13 inhibition.

Fig. 5
figure 5

Antimicrobial activity of the compounds 4/7 and its analogs (A1, A2, and A5) on E. coli (JM109). The black and gray columns represent 100 µg ml−1 ampicillin (Amp) and 100 µM isoniazid (INH), respectively

The cytotoxic effect of the active compounds on prostate cancer-derived PC3 cells was examined as previously described (Fig. S3) [19]. The compounds 4, A1, and A4 showed significant toxicity at 100 μM, while the other compounds did not exhibit cytotoxicity. In the present stage, a clear explanation could not be provided for the different damaging effects.

The MD simulation of M. tuberculosis Pks13 in complex with compound A2

The compound A2 showed the best IC50 value and was subjected to MD simulation to analyze the stability of enzyme–inhibitor interaction for a time frame of 50 ns (Fig. 6). The binding of compound A2 to M. tuberculosis Pks13 active site was maintained (Fig. 6a; the movie is also available as Supplementary material). The root mean square deviation (RMSD) plot reflects an average distance between M. tuberculosis Pks13 and compound A2 in 50 ns (Fig. 6b). The RMSD plot ranged from 0.4 to 0.7 nm during the entire span of the simulation and was found to maintain uniformity beyond 30 ns, suggesting compound A2 form a stable complex with M. tuberculosis Pks13. The other parameters: mean square fluctuation, a radius of gyration (Rg), several hydrogen bonds, and GROMAX energy are shown in Fig. S4.

Fig. 6
figure 6

MD simulation for the complex of M. tuberculosis Pks13 and compound A2. a In the MD simulation, the structure of M. tuberculosis Pks13 complex with compound A2 at 0, 25, and 50 ns. b The simulated RMSD plots for the Pks13–compound A2 complex


As mycolic acids are essential for maintaining the integrity of the TB cell wall, inhibitors targeting mycolic acid biosynthesis are being vigorously investigated. Small compounds capable of inhibiting enzymes concerning mycolic acid biosynthesis via in silico SBDS were identified [17,18,19,20,21]. As described in the previous section, given the need for new drugs to treat drug-resistant TB infection, an interest was observed in the continuous development of novel anti-infective agents. For instance, the first resistant MTB clinical isolates against bedaquiline and delamanid were reported within 2 years of their approval. Bedaquiline resistance is routine in clinical practice, suggesting the limited time window for a new drug [29, 30]. Here four inhibitors of the seven experimentally validated candidates were identified. In terms of efficiency in lead compound identification, the PCS conducted via dual screening with genetic algorithm-based (GOLD and ADV) programs was shown to improve the in silico SBDS platform substantially. The present study also demonstrated that the PCS applies to the search for bioactive analogs of lead compounds. Although an essential structural requirement to exert an antimycobacterial activity could not be attributed in detail, compounds 4/7 and these analogs are commonly composed of N(4)-alkyl piperazine (where alkyl = methyl or ethyl) linked with benzyl phenyl moiety with short ether chain.

The evidence of the inhibitory effect of the active compounds on the enzymatic activity of M. tuberculosis Pks13 is absent at the present stage because the kinetics of α-alkyl β-ketoacids synthesis is challenging [7, 13]. The physical association of compound A2 with M. tuberculosis Pks13 was then evaluated by an MD simulation study instead of an enzyme assay with recombinant protein. The MD simulation suggested the binding of compound A2 to the Pks13 active site. Furthermore, the active compounds did not affect the growth of E. coli. The observed antimycobacterial activity on M. smegmatis can be attributed to the inhibition of specific enzymes in mycobacteria [31].

The interactions between the M. tuberculosis Pks13 active site, and the active compounds (4, 7, A1, A2, and A5) were predicted using the Protein–Ligand Interaction Profiler (PLIP) ( The prediction suggested that Asn1640, Phe1670, and Tyr1674 of Pks13 interacted with all the compounds (Fig. S5), and corresponding amino acid residues were conserved in M. smegmatis Pks13 (Fig. S1). Cruz et al. recently reported MD simulation and the free energy calculations between Pks13 and inhibitors, TAM1, and its analogs [32]. Their energy decomposition analysis suggested that the residues interacting with the inhibitors are Ser1636, Tyr1637, Asn1640, Ala1667, Phe1670, and Tyr1674, from which the most significant energy contribution to Phe1670 was particularly notable. Although no structural relevance was observed between the inhibitors (TAM1 and its analogs) and the active compounds of this study, Asn1640/Phe1670/Tyr1674 were identified as common interacting residues. The interaction of an inhibitor with the residues is presumed to be essential in disturbing Pks13 function.

In conclusion, in silico SBDS through PCS identified small compounds with potent antimycobacterial activity with high efficiency. The screening strategy employed in this study could accelerate anti-TB drug development threatened by drug-resistant strains. Although the structure–activity relationships of the active compounds identified in this study have not been discussed in detail, N(4)-alkyl piperazine linked with benzyl phenyl groups via a short ether chain was proposed as a structural basis for novel Pks13 inhibitor.