Development of antibacterial compounds that constrain evolutionary pathways to resistance

Conserved Evolutionary Trajectory Can Be Perturbed to Prevent Resistance Evolution under Norfloxacin Pressure by Forcing Mycobacterium smegmatis on Alternate Evolutionary Paths

Antibiotic resistance is a pressing health issue, with the emergence of resistance in bacteria outcompeting the discovery of novel drug candidates. While many studies have used Adaptive Laboratory Evolution (ALE) to understand the determinants of resistance, the influence of the drug dosing profile on the evolutionary trajectory remains understudied. In this study, we employed ALE on Mycobacterium smegmatis exposed to various concentrations of Norfloxacin using both cyclic constant and stepwise increasing drug dosages to examine their impact on the resistance mechanisms selected. Mutations in an efflux pump regulator, LfrR, were found in all of the evolved populations irrespective of the drug profile and population bottleneck, indicating a conserved efflux-based resistance mechanism. This mutation appeared early in the evolutionary trajectory, providing low-level resistance when present alone, with a further increase in resistance resulting from successive accumulation of other mutations. Notably, drug target mutations, similar to those observed in clinical isolates, were only seen above a threshold of greater than 4× the minimum inhibitory concentration (MIC). A combination of three mutations in the genes, lfrR, MSMEG_1959, and MSMEG_5045, was conserved across multiple lineages, leading to high-level resistance and preceding the appearance of drug target mutations. Interestingly, in populations evolved from parental strains lacking the lfrA efflux pump, the primary target of the lfrR regulator, no lfrR gene mutations are selected. Furthermore, evolutional trajectories originating from the ΔlfrA strain displayed early arrest in some lineages and the absence of target gene mutations in those that evolved, albeit delayed. Thus, blocking or inhibiting the expression of efflux pumps can arrest or delay the fixation of drug target mutations, potentially limiting the maximum attainable resistance levels.

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.

Synthesis, biological and molecular modelling for 1,3,4-thiadiazole sulfonyl thioureas: bacterial and fungal activity

Some substituted thioureas (6a-i) containing a 1,3,4-thiadiazole ring were synthesized by the reaction of the corresponding substituted 2-amino-1,3,4-thiadiazoles 3a-i with p-toluenesulfonyl isocyanate in a one-pot procedure. The antibacterial and antifungal activities of these sulfonyl thioureas were estimated using a minimum inhibitory concentration protocol. Almost all the thioureas exhibited remarkable antimicrobial activity. Amongst the studied compounds, thioureas 6a, 6c, 6h, and 6i were better inhibitors against the bacterium S. aureus, with MIC values of 0.78-3.125 μg mL-1. These compounds were also tested for their inhibition against S. aureus enzymes, including enzymes of DNA gyrase, DNA topoisomerase IV (Topo IV), and dihydrofolate reductase. Amongst the compounds, 6h was a strong inhibitor, with IC50 values of 1.22, 53.78, and 0.23, respectively. Induced fit docking calculations were performed to observe the binding efficiency and steric interactions of these compounds. The obtained results showed that compound 6h was compatible with the active sites of S. aureus DNA gyrase 2XCS. This ligand interacted with residues ASP1083 (chain D), MET1121 (chain B), ARG1122 (chain D), and also with HOH2035, HOH2089, HOH2110, HOH2162. Molecular dynamics simulation in a water solvent system showed that the active interactions with residues ASP083 and MET1121 (chain B), along with ASP1083, MET1121, and ARG1122 (chain D), played an important role in stabilizing complex 6h/2XCS in the active pocket.

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling

Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and biological properties that must be simultaneously optimized. To accelerate the design of photoactive drugs, we describe a procedure that combines ligand-protein docking with chemical property prediction based on machine learning (ML). We apply this procedure to 58 proteins and 9000 photo-drug candidates based on azobenzene cis-trans isomerism. We find that most proteins display a preference for trans isomers over cis and that the binding affinities of nominally active/inactive pairs are in fact highly correlated. These findings have significant value for photopharmacology research, and reinforce the need for virtual screening to identify compounds with rare desirable properties. Further, we combine our procedure with quantum chemical validation to identify promising candidates for the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer cells. The top compounds are predicted to have long-lived active forms, differential bioactivity, and absorption in the near-infrared therapeutic window.

Thiourea derivatives containing 4-arylthiazoles and d-glucose moiety: design, synthesis, antimicrobial activity evaluation, and molecular docking/dynamics simulations

Some substituted glucose-conjugated thioureas containing 1,3-thiazole ring, 4a-h, were synthesized by the reaction of the corresponding substituted 2-amino-4-phenyl-1,3-thiazoles 2a-h with 2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl isocyanate. The antibacterial and antifungal activities of these thiazole-containing thioureas were estimated using a minimum inhibitory concentration protocol. Among these compounds, 4c, 4g, and 4h were better inhibitors with MIC = 0.78-3.125 μg mL-1. These three compounds were also tested for their ability to inhibit S. aureus enzymes, including DNA gyrase, DNA topoisomerase IV (Topo IV), and dihydrofolate reductase, and compound 4h was found to be a strong inhibitor with IC50 = 1.25 ± 0.12, 67.28 ± 1.21, and 0.13 ± 0.05 μM, respectively. Induced-fit docking and MM-GBSA calculations were performed to observe the binding efficiencies and steric interactions of these compounds. The obtained results showed that compound 4h is compatible with the active site of S. aureus DNA gyrase 2XCS with four H-bond interactions with residues Ala1118, Met1121, and F:DC11 and also three interactions with F:DG10 (two interactions) and F:DC11 (one interaction). Molecular dynamics simulation in a water solvent system showed that ligand 4h had active interactions with enzyme 2XCS through residues Ala1083, Glu1088, Ala1118, Gly1117, and Met1121.

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Synthesis and Biological Evaluation of New Schiff Bases Derived from 4-Amino-5-(3-fluorophenyl)-1,2,4-triazole-3-thione

The treatment of infectious diseases is a challenging issue faced by the medical community. The emergence of drug-resistant strains of bacteria and fungi is a major concern. Researchers and medical professionals are working to develop new and innovative treatments for infectious diseases. Schiff bases are one a promising class of compounds. In this work, new derivatives were obtained of the 4-amino-5-(3-fluorophenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione reaction, with corresponding benzaldehydes with various substituents at position 4. The antibacterial and antifungal activities of all synthesized compounds were tested. Several new substances have shown moderate antifungal activity against Candida spp. The highest activity directed against C. albicans was shown by compound RO4, with a 4-methoxyphenyl moiety and an MIC value of 62.5 µg/mL. In order to check the toxicity of the synthesized compounds, their effect on cell lines was examined. Additionally, we tried to elucidate the mechanism of the antibacterial and antifungal activity of the tested compounds using molecular docking to topoisomerase IV, D-Alanyl-D-Alanine Ligase, and dihydrofolate reductase.

DHFR Mutants Modulate Their Synchronized Dynamics with the Substrate by Shifting Hydrogen Bond Occupancies

Antibiotic resistance is a global health problem in which mutations occurring in functional proteins render drugs ineffective. The working mechanisms of the arising mutants are seldom apparent; a methodology to decipher these mechanisms systematically would render devising therapies to control the arising mutational pathways possible. Here we utilize C_α-C_β bond vector relaxations obtained from moderate length MD trajectories to determine conduits for functionality of the resistance conferring mutants of Escherichia coli dihydrofolate reductase. We find that the whole enzyme is synchronized to the motions of the substrate, irrespective of the mutation introducing gain-of-function or loss-of function. The total coordination of the motions suggests changes in the hydrogen bond dynamics with respect to the wild type as a possible route to determine and classify the mode-of-action of individual mutants. As a result, nine trimethoprim-resistant point mutations arising frequently in evolution experiments are categorized. One group of mutants that display the largest occurrence (L28R, W30G) work directly by modifying the dihydrofolate binding region. Conversely, W30R works indirectly by the formation of the E139-R30 salt bridge which releases energy resulting from tight binding by distorting the binding cavity. A third group (D27E, F153S, I94L) arising as single, resistance invoking mutants in evolution experiment trajectories allosterically and dynamically affects a hydrogen bonding motif formed at residues 59-69-71 which in turn modifies the binding site dynamics. The final group (I5F, A26T, R98P) consists of those mutants that have properties most similar to the wild type; these only appear after one of the other mutants is fixed on the protein structure and therefore display clear epistasis. Thus, we show that the binding event is governed by the entire enzyme dynamics while the binding site residues play gating roles. The adjustments made in the total enzyme in response to point mutations are what make quantifying and pinpointing their effect a hard problem. Here, we show that hydrogen bond dynamics recorded on sub-μs time scales provide the necessary fingerprints to decipher the various mechanisms at play.