Identification of novel inhibitors for mycobacterial polyketide synthase 13 via in silico drug screening assisted by the parallel compound screening with genetic algorithm-based programs

Inhibitors of the Thioesterase Activity of Mycobacterium tuberculosis Pks13 Discovered Using DNA-Encoded Chemical Library Screening

DNA-encoded chemical library (DEL) technology provides a time- and cost-efficient method to simultaneously screen billions of compounds for their affinity to a protein target of interest. Here we report its use to identify a novel chemical series of inhibitors of the thioesterase activity of polyketide synthase 13 (Pks13) from Mycobacterium tuberculosis (Mtb). We present three chemically distinct series of inhibitors along with their enzymatic and Mtb whole cell potency, the measure of on-target activity in cells, and the crystal structures of inhibitor-enzyme complexes illuminating their interactions with the active site of the enzyme. One of these inhibitors showed a favorable pharmacokinetic profile and demonstrated efficacy in an acute mouse model of tuberculosis (TB) infection. These findings and assay developments will aid in the advancement of TB drug discovery.

In silico Identification of Potential Inhibitors against Staphylococcus aureus Tyrosyl-tRNA Synthetase

BACKGROUND

Drug-resistant Staphylococcus aureus (S. aureus) has spread from nosocomial to community-acquired infections. Novel antimicrobial drugs that are effective against resistant strains should be developed. S. aureus tyrosyl-tRNA synthetase (saTyrRS) is considered essential for bacterial survival and is an attractive target for drug screening.

OBJECTIVES

The purpose of this study was to identify potential new inhibitors of saTyrRS by screening compounds in silico and evaluating them using molecular dynamics (MD) simulations.

METHODS

A 3D structural library of 154,118 compounds was screened using the DOCK and GOLD docking simulations and short-time MD simulations. The selected compounds were subjected to MD simulations of a 75-ns time frame using GROMACS.

RESULTS

Thirty compounds were selected by hierarchical docking simulations. The binding of these compounds to saTyrRS was assessed by short-time MD simulations. Two compounds with an average value of less than 0.15 nm for the ligand RMSD were ultimately selected. The longtime (75 ns) MD simulation results demonstrated that two novel compounds bound stably to saTyrRS in silico.

CONCLUSION

Two novel potential saTyrRS inhibitors with different skeletons were identified by in silico drug screening using MD simulations. The in vitro validation of the inhibitory effect of these compounds on enzyme activity and their antibacterial effect on drug-resistant S. aureus would be useful for developing novel antibiotics.

Computational Screening and Experimental Validation of Inhibitor Targeting the Complex Formation of Grb14 and Insulin Receptor

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.

Identification of novel antimicrobial compounds targeting Mycobacterium tuberculosis shikimate kinase using in silico hierarchical structure-based drug screening

The development of new anti-TB drugs to prevent the spread of multidrug-resistant Mycobacterium tuberculosis (Mtb) strains is imperative. Mtb shikimate kinase (MtSK) was selected as the target protein to screen for new anti-TB drugs. We performed hierarchical in silico screening using a library of 154,118 compounds to search for novel compounds that could bind to the active site of MtSK. The growth-inhibitory effects of the candidate compounds on Mycobacterium smegmatis were evaluated in vitro. Nine of the 11 candidate compounds exhibited inhibitory effects against mycobacteria in vitro. The inhibitory activity of Compound 2 (IC₅₀ = 1.39 μM) was higher than that of isoniazid, the first-line drug for TB treatment. Moreover, Compound 2 did not exhibit toxicity against mammalian cells and Escherichia coli. Molecular dynamics simulations using the MtSK-Compound 2 complex structure in a timeframe of 100 ns suggested that Compound 2 could stably bind to MtSK. The binding free energy of Compound 2 was estimated to be -37.96 kcal/mol using the MM/PBSA method, demonstrating that Compound 2 can stably bind to MtSK. These in silico and in vitro results indicated that Compound 2 is a promising hit compound for the development of novel anti-TB drugs.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.