Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis

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.

Machine Learning Models for Mycobacterium tuberculosisIn Vitro Activity: Prediction and Target Visualization

Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths per year. There is still a need to develop novel treatments for patients infected with Mycobacterium tuberculosis (Mtb). There have been many large-scale phenotypic screens that have led to the identification of thousands of new compounds. Yet, there is very limited investment in TB drug discovery which points to the need for new methods to increase the efficiency of drug discovery against Mtb. We have used machine learning approaches to learn from the public Mtb data, resulting in many data sets and models with robust enrichment and hit rates leading to the discovery of new active compounds. Recently, we have curated predominantly small-molecule Mtb data and developed new machine learning classification models with 18 886 molecules at different activity cutoffs. We now describe the further validation of these Bayesian models using a library of over 1000 molecules synthesized as part of EU-funded New Medicines for TB and More Medicines for TB programs. We highlight molecular features which are enriched in these active compounds. In addition, we provide new regression and classification models that can be used for scoring compound libraries or used to design new molecules. We have also visualized these molecules in the context of known molecular targets and identified clusters in chemical property space, which may aid in future target identification efforts. Finally, we are also making these data sets publicly available, representing a significant increase to the available Mtb inhibition data in the public domain.

A novel ensemble based recommendation approach using network based analysis for identification of effective drugs for Tuberculosis

Tuberculosis (TB) is a fatal infectious disease which affected millions of people worldwide for many decades and now with mutating drug resistant strains, it poses bigger challenges in treatment of the patients. Computational techniques might play a crucial role in rapidly developing new or modified anti-tuberculosis drugs which can tackle these mutating strains of TB. This research work applied a computational approach to generate a unique recommendation list of possible TB drugs as an alternate to a popular drug, EMB, by first securing an initial list of drugs from a popular online database, PubChem, and thereafter applying an ensemble of ranking mechanisms. As a novelty, both the pharmacokinetic properties and some network based attributes of the chemical structure of the drugs are considered for generating separate recommendation lists. The work also provides customized modifications on a popular and traditional ensemble ranking technique to cater to the specific dataset and requirements. The final recommendation list provides established chemical structures along with their ranks, which could be used as alternatives to EMB. It is believed that the incorporation of both pharmacokinetic and network based properties in the ensemble ranking process added to the effectiveness and relevance of the final recommendation.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Association of expression and genotypes of thymidylate synthase in non-small cell lung cancer patients with different clinicopathological characteristics

Objective

To explore the expression and genotypes of thymidylate synthase (TS) in patients of non-small cell lung cancer (NSCLC) with different clinicopathological characteristics.

Methods

The expression profiles of TS were examined by immunohistochemical staining and quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) in 160 patients with NSCLC. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to detect TS-5′UTR tandem repeats, G/C nucleotide polymorphisms, and 3′UTR 6 bp deletion/insertion polymorphisms. The relationships between clinicopathological characteristics and TS expression or genotypes were investigated through χ 2 test. Kaplan–Meier survival analysis was used to analyze the association between TS expression and overall survival (OS) and disease-free survival (DFS) of NSCLC patients.

Results

The expression levels of TS protein and TS gene in NSCLC tissues were significantly higher than that in paracancerous tissues (P < 0.05). Furthermore, high expression of TS protein and 5′UTR polymorphism of TS gene showed significant correlation with differentiation, TNM stage, and lymph node metastases. The frequency of −6 bp/−6 bp genotypes in patients with NSCLC was 43.13% (69/160), which was higher than others. In addition, the rate of TS protein overexpression in NSCLC patients with 3R/3R was 79.79%, which was higher than others. Interestingly, high expression of TS protein predicted shorter DFS and OS and lower 3-year DFS rate and 3-year OS rate.

Conclusions

The expression levels of TS in NSCLC were significantly increased and may help to predict the prognosis of NSCLC, and high expression of TS protein and 5′UTR polymorphism of TS gene were significantly related to differentiation, TNM stage, and lymph node metastases.

Mechanism of Naphthoquinone Selectivity of Thymidylate Synthase ThyX

Naphthoquinones (NQs) are natural and synthetic compounds with a wide range of biological activities commonly attributed to their redox activity and/or chemical reactivity. However, genetic and biochemical experiments have recently demonstrated that 2-hydroxy-NQs (2-OH-NQs) act as highly specific noncovalent inhibitors of the essential bacterial thymidylate synthase ThyX in a cellular context. We used biochemical experiments and molecular dynamics simulations to elucidate the selective inhibition mechanism of NQ inhibitors of ThyX from Mycobacterium tuberculosis (Mtb). Free energy simulations rationalized how ThyX recognizes the natural substrate dUMP in the N3-ionized form using an arginine, Arg199, in Mtb. The results further demonstrated that 2-OH-NQ, similar to dUMP, binds to ThyX in the ionized form, and the strong and selective binding of 2-OH-NQ to ThyX is also explained by electrostatic interactions with Arg199. The stronger binding of the close analog 5F-dUMP to ThyX and its inhibitory properties compared with dUMP were explained by the stronger acidity of the uracil N3 atom. Our results, therefore, revealed that the ionization of 2-OH-NQs drives their biological activities by mimicking the interactions with the natural substrate. Our observations encourage the rational design of optimized ThyX inhibitors that ultimately may serve as antibiotics.

Potential anti-TB investigational compounds and drugs with repurposing potential in TB therapy: a conspectus

The latest WHO report estimates about 1.6 million global deaths annually from TB, which is further exacerbated by drug-resistant (DR) TB and comorbidities with diabetes and HIV. Exiguous dosing, incomplete treatment course, and the ability of the tuberculosis bacilli to tolerate and survive current first-line and second-line anti-TB drugs, in either their latent state or active state, has resulted in an increased prevalence of multidrug-resistant (MDR), extensively drug-resistant (XDR), and totally drug-resistant TB (TDR-TB). Although a better understanding of the TB microanatomy, genome, transcriptome, proteome, and metabolome, has resulted in the discovery of a few novel promising anti-TB drug targets and diagnostic biomarkers of late, no new anti-TB drug candidates have been approved for routine therapy in over 50 years, with only bedaquiline, delamanid, and pretomanid recently receiving tentative regulatory approval. Considering this, alternative approaches for identifying possible new anti-TB drug candidates, for effectively eradicating both replicating and non-replicating Mycobacterium tuberculosis, are still urgently required. Subsequently, several antibiotic and non-antibiotic drugs with known treatment indications (TB targeted and non-TB targeted) are now being repurposed and/or derivatized as novel antibiotics for possible use in TB therapy. Insights gathered here reveal that more studies focused on drug-drug interactions between licensed and potential lead anti-TB drug candidates need to be prioritized. This write-up encapsulates the most recent findings regarding investigational compounds with promising anti-TB potential and drugs with repurposing potential in TB therapy.

Mycobacterium tuberculosis thymidylate synthase (ThyX) is a target for plumbagin, a natural product with antimycobacterial activity

Plumbagin derived from the plant Plumbago indica, known as Chitrak in India, is an example of a medicinal compound used traditionally to cure a variety of ailments. Previous reports have indicated that it can inhibit the growth of Mycobacterium tuberculosis (Mtb), the causative agent of the deadly disease TB. In this investigation, we provide an insight into its mode of action. We show here that a significant mycobacterial target that is inhibited by plumbagin is the enzyme ThyX, a form of thymidylate synthase, that is responsible for the synthesis of dTMP from dUMP in various bacterial pathogens, including Mtb. Using a purified preparation of the recombinant version of Mtb ThyX, we demonstrate that plumbagin, a 2,4 napthoquinone, but not lawsone, a structurally related medicinal compound, inhibits its activity in vitro. We also show that the intracellular [dTTP]/[dATP] ratio in Mycobacterium smegmatis (Msm) cells decrease upon treatment with plumbagin, and this, in turn, leads to cell death. Such a conclusion is supported by the observation that over-expression of thyx in the plumbagin treated Msm cells leads to the restoration of viability. The results of our investigation indicate that plumbagin kills mycobacterial cells primarily by targeting ThyX, a vital enzyme required for their survival.

Use of artificial intelligence in infectious diseases

Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. These pathogens are transmitted, directly or indirectly, and can lead to epidemics or even pandemics. The resulting infection may lead to mild-to-severe symptoms such as life-threatening fever or diarrhea. Infectious diseases may be asymptomatic in some individuals but may lead to disastrous effects in others. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. With the advent of mathematical tools, scientists are now able to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for their ability to better diagnose certain types of cancer from imaging data. This chapter aims at identifying potential applications of machine learning in the field of infectious diseases. We are deliberately focusing on key aspects of infection: diagnosis, transmission, response to treatment, and resistance. We are proposing the use of extreme values as an avenue of interest for future developments in the field of infectious diseases. This chapter covers a series of applications selectively chosen to showcase how artificial intelligence is moving the field of infectious disease further and how it helps institutions to better tackles them, especially in low-income countries.

Analysis of mutations leading to para-aminosalicylic acid resistance in Mycobacterium tuberculosis

Thymidylate synthase A (ThyA) is the key enzyme involved in the folate pathway in Mycobacterium tuberculosis. Mutation of key residues of ThyA enzyme which are involved in interaction with substrate 2′-deoxyuridine-5′-monophosphate (dUMP), cofactor 5,10-methylenetetrahydrofolate (MTHF), and catalytic site have caused para-aminosalicylic acid (PAS) resistance in TB patients. Focusing on R127L, L143P, C146R, L172P, A182P, and V261G mutations, including wild-type, we performed long molecular dynamics (MD) simulations in explicit solvent to investigate the molecular principles underlying PAS resistance due to missense mutations. We found that these mutations lead to (i) extensive changes in the dUMP and MTHF binding sites, (ii) weak interaction of ThyA enzyme with dUMP and MTHF by inducing conformational changes in the structure, (iii) loss of the hydrogen bond and other atomic interactions and (iv) enhanced movement of protein atoms indicated by principal component analysis (PCA). In this study, MD simulations framework has provided considerable insight into mutation induced conformational changes in the ThyA enzyme of Mycobacterium.

Synthesis and Structure-Activity Relationship Studies of Benzo[b][1,4]oxazin-3(4H)-one Analogues as Inhibitors of Mycobacterial Thymidylate Synthase X

Since the discovery of a flavin-dependent thymidylate synthase (ThyX or FDTS) that is absent in humans but crucial for DNA biosynthesis in a diverse group of pathogens, the enzyme has been pursued for the development of new antibacterial agents against Mycobacterium tuberculosis, the causative agent of the widespread infectious disease tuberculosis (TB). In response to a growing need for more effective anti-TB drugs, we have built upon our previous screening efforts and report herein an optimization campaign of a novel series of inhibitors with a unique inhibition profile. The inhibitors display competitive inhibition toward the methylene tetrahydrofolate cofactor of ThyX, enabling us to generate a model of the compounds bound to their target, thus offering insight into their structure-activity relationships.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

The EU approved antimalarial pyronaridine shows antitubercular activity and synergy with rifampicin, targeting RNA polymerase

The search for compounds with biological activity for many diseases is turning increasingly to drug repurposing. In this study, we have focused on the European Union-approved antimalarial pyronaridine which was found to have in vitro activity against Mycobacterium tuberculosis (MIC 5 μg/mL). In macromolecular synthesis assays, pyronaridine resulted in a severe decrease in incorporation of ¹⁴C-uracil and ¹⁴C-leucine similar to the effect of rifampicin, a known inhibitor of M. tuberculosis RNA polymerase. Surprisingly, the co-administration of pyronaridine (2.5 μg/ml) and rifampicin resulted in in vitro synergy with an MIC 0.0019-0.0009 μg/mL. This was mirrored in a THP-1 macrophage infection model, with a 16-fold MIC reduction for rifampicin when the two compounds were co-administered versus rifampicin alone. Docking pyronaridine in M. tuberculosis RNA polymerase suggested the potential for it to bind outside of the RNA polymerase rifampicin binding pocket. Pyronaridine was also found to have activity against a M. tuberculosis clinical isolate resistant to rifampicin, and when combined with rifampicin (10% MIC) was able to inhibit M. tuberculosis RNA polymerase in vitro. All these findings, and in particular the synergistic behavior with the antitubercular rifampicin, inhibition of RNA polymerase in combination in vitro and its current use as a treatment for malaria, may suggest that pyronaridine could also be used as an adjunct for treatment against M. tuberculosis infection. Future studies will test potential for in vivo synergy, clinical utility and attempt to develop pyronaridine analogs with improved potency against M. tuberculosis RNA polymerase when combined with rifampicin.

Unique Features and Anti-microbial Targeting of Folate- and Flavin-Dependent Methyltransferases Required for Accurate Maintenance of Genetic Information

Comparative genome analyses have led to the discovery and characterization of novel flavin- and folate-dependent methyltransferases that mainly function in DNA precursor synthesis and post-transcriptional RNA modification by forming (ribo) thymidylate and its derivatives. Here we discuss the recent literature on the novel mechanistic features of these enzymes sometimes referred to as “uracil methyltransferases,” albeit we prefer to refer to them as (ribo) thymidylate synthases. These enzyme families attest to the convergent evolution of nucleic acid methylation. Special focus is given to describing the unique characteristics of these flavin- and folate-dependent enzymes that have emerged as new models for studying the non-canonical roles of reduced flavin co-factors (FADH₂) in relaying carbon atoms between enzyme substrates. This ancient enzymatic methylation mechanism with a very wide phylogenetic distribution may be more commonly used for biological methylation reactions than previously anticipated. This notion is exemplified by the recent discovery of additional substrates for these enzymes. Moreover, similar reaction mechanisms can be reversed by demethylases, which remove methyl groups e.g., from human histones. Future work is now required to address whether the use of different methyl donors facilitates the regulation of distinct methylation reactions in the cell. It will also be of great interest to address whether the low activity flavin-dependent thymidylate synthases ThyX represent ancestral enzymes that were eventually replaced by the more active thymidylate synthases of the ThyA family to facilitate the maintenance of larger genomes in fast-growing microbes. Moreover, we discuss the recent efforts from several laboratories to identify selective anti-microbial compounds that target flavin-dependent thymidylate synthase ThyX. Altogether we underline how the discovery of the alternative flavoproteins required for methylation of DNA and/or RNA nucleotides, in addition to providing novel targets for antibiotics, has provided new insight into microbial physiology and virulence.

Host-pathogen systems for early drug discovery against tuberculosis

Tuberculosis (TB) is a global disease causing 1.8 million deaths each year. The appearance of drug-resistant strains raised the demand for new anti-mycobacterial drugs and therapies, because previously discovered antibiotics are shown to be inefficient. Moreover, the number of newly discovered drugs is not increasing in proportion to the emergence of drug resistance, which suggests that more optimized methodology and screening procedures are required including the incorporation of in vivo properties of TB infection. A way to improve efficacy of screening approaches is by introducing the use of different host-pathogen systems into primary screenings. These include whole cell-based screenings, zebrafish larvae-based screenings and the impact of artificial granuloma research on the drug discovery process. This review highlights current screening attempts and the identified molecular targets and summarizes findings of alternative, not fully explored host-pathogen systems for the characterization of anti-mycobacterial compounds.

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public-private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project.

Learning from the past for TB drug discovery in the future

Tuberculosis drug discovery has shifted in recent years from a primarily target-based approach to one that uses phenotypic high-throughput screens. As examples of this, through our EU-funded FP7 collaborations, New Medicines for Tuberculosis was target-based and our more-recent More Medicines for Tuberculosis project predominantly used phenotypic screening. From these projects we have examples of success (DprE1) and failure (PimA) going from drug to target and from target to drug, respectively. It is clear that we still have much to learn about the drug targets and the complex effects of the drugs on Mycobacterium tuberculosis. We propose a more integrated approach that learns from earlier drug discovery efforts that could help to move drug discovery forward.

Virtual Screening and X-ray Crystallography Identify Non-Substrate Analog Inhibitors of Flavin-Dependent Thymidylate Synthase

Thymidylate synthase X (ThyX) represents an attractive target for tuberculosis drug discovery. Herein, we selected 16 compounds through a virtual screening approach. We solved the first X-ray crystal structure of Thermatoga maritima (Tm) ThyX in complex with a nonsubstrate analog inhibitor. Given the active site similarities between Mycobacterium tuberculosis ThyX (Mtb-ThyX) and Tm-ThyX, our crystal structure paves the way for a structure-based design of novel antimycobacterial compounds. The 1H-imidazo[4,5-d]pyridazine was identified as scaffold for the development of Mtb-ThyX inhibitors.

Identification and validation of novel drug targets in Mycobacterium tuberculosis

Tuberculosis (TB) is a global epidemic associated increasingly with resistance to first- and second-line antitubercular drugs. The magnitude of this global health threat underscores the urgent need to discover new antimycobacterial agents that have novel mechanisms of action (MOA). In this review, we highlight some of the key advances that have enabled the strengths of target-led and phenotypic approaches to TB drug discovery to be harnessed both independently and in combination. Critically, these promise to fuel the front-end of the TB drug pipeline with new, pharmacologically validated drug targets together with lead compounds that act on these targets.