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We have previously performed a hierarchical in silico screening of a Mycobacterium tuberculosis shikimic acid kinase [1]. Specifically, 11 compounds were screened from a library of 154,118 compounds provided by ChemBridge [2] using UCSF DOCK [3] and the GOLD [4] program in the first and second steps, respectively. Molecular dynamic simulations were further performed on compound 2 (2-[(5Z)-5-(1-benzyl-5bromo-2-oxoindol-3-(5Z)-5-(1-benzyl-5-bromo-2-oxoindol-3-(5Z)-4-oxo-2 ylidene)-4oxo-2-sulfanylidene-1,3-thiazolidin-3-yl] acetic acid), which showed antimicrobial efficacy. These processes yielded ligand docking scores and trajectories. In this data article, we have added solvent-accessible surface area and PCA analyses, which were calculated from the raw docking scores and trajectories. Data obtained from molecular docking and molecular dynamic simulations are useful in two ways:(1) Further support for previous work (2) Provides a stepping stone for experimental scientists to conduct in silico studies and research ideas for other drug discovery researchers and computational biologists. We believe that this article will provide an opportunity to develop new Mycobacterium tuberculosis therapeutics through searching for analogs and inhibitors against new targets.

Staphylococcus aureus (S. aureus) is a major bacterial infection in humans, leading to severe disease and causing death. The stagnation of antibiotic development in recent decades has made it difficult to combat drug-resistant infections. In this study, we performed an in silico structure-based drug screening (SBDS) targeting the S. aureus MurE (saMurE) enzyme involved in cell wall synthesis of S. aureus. saMurE is an enzyme that is essential for the survival of S. aureus but not present in humans. SBDS identified nine saMurE inhibitor candidates, Compounds 1–9, from a structural library of 154,118 compounds. Among them, Compound 2 showed strong antibacterial activity against Staphylococcus epidermidis (S. epidermidis) used as a model bacterium. Amino acid sequence homology between saMurE and S. epidermidis MurE is 87.4%, suggesting that Compound 2 has a similar inhibitory effect on S. aureus. Compound 2 showed an IC50 value of 301 nM for S. epidermidis in the dose-dependent growth inhibition assay. Molecular dynamics simulation showed that Compound 2 binds stably to both S. aureus MurD and S. aureus MurF, suggesting that it is a potential multi-pharmacological pharmacological inhibitor. The structural and bioactivity information of Compound 2, as well as its potential multiple-target activity, could contribute to developing new antimicrobial agents based on MurE inhibition.

The emergence of multidrug-resistant and extensively drug-resistant Mycobacterium tuberculosis (M. tuberculosis) has become a major medical problem. S-adenosyl-L-homocysteine hydrolase (MtSAHH) was selected as the target protein for the identification of novel anti-TB drugs. Dual hierarchical in silico Structure-Based Drug Screening was performed using a 3D compound structure library (with over 150 thousand synthetic chemicals) to identify compounds that bind to MtSAHH’s active site. In vitro experiments were conducted to verify whether the nine compounds selected as new drug candidates exhibited growth-inhibitory effects against mycobacteria. Eight of the nine compounds that were predicted by dual hierarchical screening showed growth-inhibitory effects against Mycobacterium smegmatis (M. smegmatis), a model organism for M. tuberculosis. Compound 7 showed the strongest antibacterial activity, with an IC50 value of 30.2 µM. Compound 7 did not inhibit the growth of Gram-negative bacteria or exert toxic effects on human cells. Molecular dynamics simulations of 40 ns using the MtSAHH–Compound 7 complex structure suggested that Compound 7 interacts stably with the MtSAHH active site. These in silico and in vitro results suggested that Compound 7 is a promising lead compound for the development of new anti-TB drugs.

The development of drugs targeting gene products associated with insulin resistance holds the potential to enhance our understanding of type 2 diabetes mellitus (T2DM). The virtual screening, based on a three-dimensional (3D) protein structure, is a potential technique to accelerate the development of molecular target drugs. Among the targets implicated in insulin resistance, the genetic characterization and protein function of Grb14 have been clarified without contradiction. The Grb14 gene displays significant variations in T2DM, and its gene product is known to inhibit the function of the insulin receptor (IR) by directly binding to the tyrosine kinase domain. In the present study, a virtual screening, based on a 3D structure of the IR tyrosine kinase domain (IRβ) in complex with part of Grb14, was conducted to find compounds that can disrupt the complex formation between Grb14 and IRβ. First, ten compounds were selected from 154,118 compounds via hierarchical in silico structure-based drug screening, composed of grid docking-based and genetic algorithm-based programs. The experimental validations suggested that the one compound can affect the blood glucose level. The molecular dynamics simulations and co-immunoprecipitation analysis showed that the compound did not completely suppress the protein–protein interaction between Grb14 and IR, though competitively bound to IR with the tyrosine kinase pseudosubstrate region in Grb14.

Autonomous driving systems are highly expected to be used on public roads to improve traffic throughput and road safety, but it will likely take a long transition period before all human-driven vehicles can be replaces with autonomous vehicles. Hence, CAVs have to safely cooperate with the surrounding human drivers, in order to gain the benefits of autonomous driving technologies during such transition periods. In this paper, we present a Distributed Synchronous Intersection Protocol (DSIP) and a Cooperative Perception-based High-Definition Map (CP-HD Map) for mixed traffic environments, in which autonomous vehicles co-exist with human-driven vehicles. First, in DSIP, each CAV utilizes dynamic decision-making mechanisms to adaptively change the vehicle behaviors based on the surrounding environments by using vehicle states and vehicle mode. In addition, in CP-HD Map, each CAV uses Vehicle-to-Vehicle (V2V) communications to share the information of detected objects to improve the road safety in the mixed traffic environments. Under these protocols, human-driven vehicles simply follow the traffic lights just like they do today. Finally, we show that DSIP and CP-HD Map increase the traffic throughput around the road intersections when we compared to existing signalized intersections and other V2V communications-based protocols.

Background: The emergence of frequent hitters (FHs) remains a challenge in drug discovery. We have previously used in silico structure-based drug screening (SBDS) to identify antimycobacterial candidates. However, excluding FHs has not been integrated into the SBDS system. Methods: A dataset comprising 15,000 docking score (protein–compound affinity matrix) was constructed by multiple target screening (MTS): DOCK–GOLD two-step docking simulations with 154,118 compounds versus the 30 target proteins essential for mycobacterial survival. After extraction of 141 compounds from the protein–compound affinity matrix, compounds determined to be FHs or false positives were excluded. Antimycobacterial properties of the top nine compounds selected through SBDS were experimentally evaluated. Results: Nine compounds designated KS1–KS9 were selected for experimental evaluation. Among the selected compounds, KS3, identified as adenosylhomocysteinase inhibitor, showed a potent inhibitory effect on antimycobacterial growth (inhibitory concentration [IC]50 = 1.2 M). However, the compound also showed potent cytotoxicity. Conclusion: The MTS method is applicable in SBDS for the identification of enzyme-specific inhibitors.

Background: Mycobacterium tuberculosis enoyl-acyl carrier protein reductase (mtInhA) is involved in the biosynthesis of mycolic acids, a major component of mycobacterial cell walls, and has been targeted in the development of anti-tuberculosis (TB) drugs. In our previous in silico structure-based drug screening study, we identified KES4, a novel class of mtInhA inhibitor. KES4 is composed of four ring structures (A–D-rings) and molecular dynamic simulation predicted that the D-ring is essential for the interaction with mtInhA. Methods: The structure–activity relationship study of the D-ring was attempted and aided by in silico docking simulations to improve the mtInhA inhibitory activity of KES4. A virtual chemical library of the D-ring-modified KES4 was then constructed and subjected to in silico docking simulation against mtInhA using the GOLD program. The candidate compound showing the highest GOLD score, referred to as KEN1, was synthesized, and its biological properties were compared with those of the lead compound KES4. Results: We achieved the synthesis of KEN1 and evaluated its effects on InhA activity, mycobacterial growth, and cytotoxicity. The antimycobacterial activity of KEN1 was comparable to that of the lead compound (KES4), although it exhibited superior activity in mtInhA inhibition. \Conclusions: We obtained a KES4 derivative with high mtInhA inhibitory activity by in silico docking simulation with a chemical library consisting of a series of D-ring-modified KES4.

Background: Enzymes responsible for cell wall development in Mycobacterium tuberculosis are considered as potential targets of anti-tuberculosis (TB) agents. Mycobacterial cyclopropane mycolic acid synthase 1 (CmaA1) is essential for mycobacterial survival because of its critical role in synthesizing mycolic acids. Materials and Methods: We screened compounds that were capable of interacting with the mycobacterial CmaA1 active site using a virtual compound library with an in silico structure-based drug screening (SBDS). Following the selection of such compounds, their antimycobacterial activity was examined. Results: With the in silico SBDS, for which we also used DOCK-GOLD programs and screening methods that utilized the structural similarity between the selected active compounds, we identified two compounds with potent inhibitory effects on mycobacterial growth. The antimycobacterial effect of the compounds was comparable to that of isoniazid, which is used as a first-line anti-TB drug. Conclusion: The compounds identified through SBDS were expected to be a novel class of anti-TB pharmacophores.

Objective/Background: Mycobacterium tuberculosis thymidine monophosphate kinase (mtTMPK) is a potential enzymatic target for the treatment of tuberculosis (TB). Materials and Methods: In this study, we performed pharmacophore-based in silico screening, targeting mtTMPK with a compound library of 461,383 chemicals. We evaluated the candidate compounds for inhibitory effects on the growth of the model mycobacteria, Mycobacterium smegmatis. Results: The compound KTP3 completely inhibited the growth of M. smegmatis at 100 μM. A similarity search and rescreening with the structure of compound KTP3 using a web-based database identified two similar compounds (KTPS1 and KTPS2) with improved potency. The KTP3 analogs, KTPS1 and KTPS2, exhibited strong growth inhibitory effects with half-maximal inhibitory concentration values of 8.04 μM and 17.1 μM, respectively, against M. smegmatis. Moreover, the most potent chemical compound, KTPS1, did not exhibit toxic effects on the model enterobacteria and several mammalian cells. Two active chemicals, KTPS1 and KTPS2, inhibited mtTMPK activity by 18% and 36%, respectively, suggesting that these compounds have off-target activities against Mycobacterium. Conclusion: Structural and biological information on these chemicals is likely to be useful for the development of novel antibiotics for the treatment of TB.