Targeting dormant phenotype acquired mycobacteria using natural products by exploring its important targets: In vitro and in silico studies

Generative AI, molecular docking and molecular dynamics simulations assisted identification of novel transcriptional repressor EthR inhibitors to target Mycobacterium tuberculosis

Tuberculosis (TB) remains a persistent global health threat, with Mycobacterium tuberculosis (Mtb) continuing to be a leading cause of mortality worldwide. Despite efforts to control the disease, the emergence of multi-drug-resistant (MDR) and extensively drug-resistant (XDR) TB strains presents a significant challenge to conventional treatment approaches. Addressing this challenge requires the development of novel anti-TB drug molecules. This study employed de novo drug design approaches to explore new EthR ligands and ethionamide boosters targeting the crucial enzyme InhA involved in mycolic acid synthesis in Mtb. Leveraging REINVENT4, a modern open-source generative AI framework, the study utilized various optimization algorithms such as transfer learning, reinforcement learning, and curriculum learning to design small molecules with desired properties. Specifically, focus was placed on molecule optimization using the Mol2Mol option, which offers multinomial sampling with beam search. The study's findings highlight the identification of six promising compounds exhibiting enhanced activity and improved physicochemical properties through structure-based drug design and optimization efforts. These compounds offer potential candidates for further preclinical and clinical development as novel therapeutics for TB treatment, providing new avenues for combating drug-resistant TB strains and improving patient outcomes.

Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors

BACKGROUND

Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria Mycobacterium tuberculosis (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant role in activating the long-chain fatty acids.

RESEARCH DESIGN & METHODS

This study aims to generate novel compounds using Machine Learning (ML) algorithms to inhibit this synthase. Experimentally derived bioactive compounds were chosen from ChEMBL and used as inputs for effective molecule generation by Reinvent4. The library of new molecules generated was subjected to a two-tiered molecular docking protocol, and the results were further studied to obtain a binding free energy check.

RESULTS

The ML-based de novo drug design (DNDD) approach successfully generated a diverse library of novel molecules targeting Fatty Acyl-CoA synthase. After rigorous molecular docking and binding free energy analysis, four new compounds were identified as potential lead candidates with promising inhibitory effects on Mtb lipid metabolism.

CONCLUSIONS

The study demonstrated the effectiveness of a machine-learning approach in generating novel drug candidates against Mtb. The identified hit compounds show potential as inhibitors of Fatty Acyl-CoA synthase, offering a new avenue for developing treatments for tuberculosis, particularly in combating drug-resistant strains.

Amoebae as training grounds for microbial pathogens

Grazing of amoebae on microorganisms represents one of the oldest predator-prey dynamic relationships in nature. It represents a genetic "melting pot" for an ancient and continuous multi-directional inter- and intra-kingdom horizontal gene transfer between amoebae and its preys, intracellular microbial residents, endosymbionts, and giant viruses, which has shaped the evolution, selection, and adaptation of microbes that evade degradation by predatory amoeba. Unicellular phagocytic amoebae are thought to be the ancient ancestors of macrophages with highly conserved eukaryotic processes. Selection and evolution of microbes within amoeba through their evolution to target highly conserved eukaryotic processes have facilitated the expansion of their host range to mammals, causing various infectious diseases. Legionella and environmental Chlamydia harbor an immense number of eukaryotic-like proteins that are involved in ubiquitin-related processes or are tandem repeats-containing proteins involved in protein-protein and protein-chromatin interactions. Some of these eukaryotic-like proteins exhibit novel domain architecture and novel enzymatic functions absent in mammalian cells, such as ubiquitin ligases, likely acquired from amoebae. Mammalian cells and amoebae may respond similarly to microbial factors that target highly conserved eukaryotic processes, but mammalian cells may undergo an accidental response to amoeba-adapted microbial factors. We discuss specific examples of microbes that have evolved to evade amoeba predation, including the bacterial pathogens- Legionella, Chlamydia, Coxiella, Rickettssia, Francisella, Mycobacteria, Salmonella, Bartonella, Rhodococcus, Pseudomonas, Vibrio, Helicobacter, Campylobacter, and Aliarcobacter. We also discuss the fungi Cryptococcus, and Asperigillus, as well as amoebae mimiviruses/giant viruses. We propose that amoeba-microbe interactions will continue to be a major "training ground" for the evolution, selection, adaptation, and emergence of microbial pathogens equipped with unique pathogenic tools to infect mammalian hosts. However, our progress will continue to be highly dependent on additional genomic, biochemical, and cellular data of unicellular eukaryotes.

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA)

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, more than 10 million people were infected worldwide, and 1.3 million were children. The current study considered the in-silico and machine learning (ML) approaches to explore the potential anti-TB molecules from the SelleckChem database against Enoyl-Acyl Carrier Protein Reductase (InhA). Initially, the entire database of ∼ 119000 molecules was sorted out through drug-likeness. Further, the molecular docking study was conducted to reduce the chemical space. The standard TB drug molecule's binding energy was considered a threshold, and molecules found with lower affinity were removed for further analyses. Finally, the molecules were checked for the pharmacokinetic and toxicity studies, and compounds found to have acceptable pharmacokinetic parameters and were non-toxic were considered as final promising molecules for InhA. The above approach further evaluated five molecules for ML-based toxicity and synthetic accessibility assessment. Not a single molecule was found toxic and each of them was revealed as easy to synthesise. The complex between InhA and proposed and standard molecules was considered for molecular dynamics simulation. Several statistical parameters showed the stability between InhA and the proposed molecule. The high binding affinity was also found for each of the molecules towards InhA using the MM-GBSA approach. Hence, the above approaches and findings exposed the potentiality of the proposed molecules against InhA.

Rutin from Begonia roxburghii modulates iNOS and Sep A activity in treatment of Shigella flexneri induced diarrhoea in rats: An in vitro, in vivo and computational analysis

In developing countries, diarrhoea is a major issue of concern, where consistent use of antibiotics has resulted in several side effects along with development of resistance among pathogens against these antibiotics. Since natural products are becoming the treatment of choice, therefore present investigation involves mechanistic evaluation of antidiarrhoeal potential of Begonia roxburghii and its marker rutin against Shigella flexneri (SF) induced diarrhoea in rats following in vitro, in vivo and in silico protocols. The roots of the plant are used as vegetable in the North East India and are also used traditionally in treating diarrhoea. Phytochemically standardized ethanolic extract of B. roxburghii (EBR) roots and its marker rutin were first subjected to in vitro antibacterial evaluation against SF. Diarrhoea was induced in rats using suspension of SF and various diarrhoeagenic parameters were examined after first, third and fifth day of treatment at 100, 200 and 300 mg/kg, p.o. with EBR and 50 mg/kg, p.o. with rutin respectively. Additionally, density of SF in stools, stool water content, haematological and biochemical parameters, cytokine profiling, ion concentration, histopathology and Na+/K+-ATPase activity were also performed. Molecular docking and dynamics simulation studies of ligand rutin was studied against secreted extracellular protein A (Sep A, PDB: 5J44) from SF and Inducible nitric oxide synthase (iNOS, PDB: 1DD7) followed by network pharmacology. EBR and rutin demonstrated a potent antibacterial activity against SF and also showed significant recovery from diarrhoea (EBR: 81.29 ± 0.91% and rutin: 75.27 ± 0.89%) in rats after five days of treatment. EBR and rutin also showed significant decline in SF density in stools, decreased cytokine expression, potential antioxidant activity, cellular proliferative nature and recovered ion loss due to enhanced Na+/K+-ATPase activity, which was also supported by histopathology. Rutin showed a very high docking score of -11.61 and -9.98 kcal/mol against iNOS and Sep A respectively and their stable complex was also confirmed through dynamics, while network pharmacology suggested that, rutin is quite capable of modulating the pathways of iNOS and Sep A. Thus, we may presume that rutin played a key role in the observed antidiarrhoeal activity of B. roxburghii against SF induced diarrhoea.