Structure guided prediction of Pyrazinamide resistance mutations in pncA

Insights to the role of phytoconstituents in aiding multi drug resistance - Tuberculosis treatment strategies

Multidrug resistant tuberculosis (MDR-TB) have emerged as a global challenge. There are several underlying mechanisms which are involved in causing mycobacterial resistance towards antitubercular agents including post translational modifications, efflux pumps and gene mutations. This resistance necessitates the investigation of complementary therapeutic options including the use of bioactive compounds from plants. Recent studies have focused on recognising and isolating the characteristics of these compounds to assess their potential against MDR-TB. Phytoconstituents such as alkaloids, flavonoids, terpenoids, glycosides, and essential oils have shown promising antimicrobial activity against Mycobacterium tuberculosis. These compounds can either directly kill or inhibit the growth of M. tuberculosis or enhance the immune system's ability to fight against the infection. Some studies suggest that combining phytoconstituents with standard antitubercular medications works synergistically by enhancing the efficacy of drug, potentially lowering the associated risk of side effects and eventually combating resistance development. This review attempts to elucidate the potential of phytoconstituents in combating resistance in MDR-TB which hold a promise to change the course of treatment strategies in tuberculosis.

A simplified pyrazinamidase test for Mycobacterium tuberculosis pyrazinamide antimicrobial susceptibility testing

Pyrazinamide (PZA) is an important first-line drug for tuberculosis (TB) treatment by eradicating the persisting Mycobacterium tuberculosis complex (MTBC). Due to cost and technical challenges, end TB strategies are hampered by the lack of a simple and reliable culture-based PZA antimicrobial susceptibility testing (AST) for routine use. We initially developed a simplified chromogenic pyrazinamidase (PZase) test in the TB reference laboratory using a training set MTBC isolates with various drug-resistant profiles, and validated its performance using consecutive BACTEC MGIT 960 (MGIT)-culture-positive culture in 10 clinical laboratories. The pncA gene Sanger sequencing results were used as the reference, and compared to the MGIT-PZA AST. Differential diagnosis of Mycobacterium bovis was conducted using patented in-house real-time PCR. Of the 106 training isolates, the PZase test and MGIT-PZA AST showed 100.0% and 99.1% concordance as compared to Sanger sequencing, respectively. We found 32.1% (34/106) isolates harbored pncA mutations, including one isolate with silent mutation S65S. For validation, 1,793 clinical isolates were tested including 150 duplicate isolates from specimens of the same cases and 16 isolates with uncharacterized drug resistance (UDR)-associated mutations. Excluding duplicated and UDR isolates, we identified 2.6% (43/1,627) PZA-resistant isolates, including 1.3% (21/1,627) M. bovis isolates. The kappa values were 0.851-1.000. In addition, the accuracy of the PZase test conducted by 10 laboratories was 98.5%-100.0%. Our simplified PZase test demonstrated high concordance with Sanger sequencing and MGIT-PZA AST. Integrating the PZase test into routine first-line AST is effortless and represents an improvement in laboratory services for ending TB.

IMPORTANCE

We developed and validated a simple pyrazinamidase (PZase) test for pyrazinamide (PZA) antimicrobial susceptibility testing (AST). Our results demonstrated that the PZase test had high agreement with the pncA gene sequencing and MGIT-PZA AST. Integrating PZase test into routine AST is effortless and represents an improvement in laboratory services for ending TB.

Studying the dynamics of the drug processing of pyrazinamide in Mycobacterium tuberculosis

Pyrazinamide (PZA) is a key drug in the treatment of Mycobacterium tuberculosis. Although not completely understood yet, the bactericidal mechanism of PZA starts with its diffusion into the cell and subsequent conversion into pyrazinoic acid (POA) after the hydrolysis of ammonia group. This leads to the acidification cycle, which involves: (1) POA extrusion into the extracellular environment, (2) reentry of protonated POA, and (3) release of a proton into the cytoplasm, resulting in acidification of the cytoplasm and accumulation of intracellular POA. To better understand this process, we developed a system of coupled non-linear differential equations, which successfully recapitulates the kinetics of PZA/POA observed in M. tuberculosis. The parametric space was explored, assessing the impact of different PZA and pH concentrations and variations in the kinetic parameters, finding scenarios of PZA susceptibility and resistance. Furthermore, our predictions show that the acidification cycle alone is not enough to result in significant intracellular accumulation of POA in experimental time scales when compared to other neutral pH scenarios. Thus, revealing the need of novel hypotheses and experimental evidence to determine the missing mechanisms that may explain the pH-dependent intracellular accumulation of POA and their subsequent effects.

Engineering G protein-coupled receptors for stabilization

G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

Mechanistic insights into the conformational changes and alterations in residual communications due to the mutations in the pncA Gene of Mycobacterium tuberculosis: A computational perspective for effective therapeutic solutions

Due to its emerging resistance to first-line anti-TB medications, tuberculosis (TB) is one of the most contagious illness in the world. According to reports, the effectiveness of treating TB is severely impacted by drug resistance, notably resistance caused by mutations in the pncA gene-encoded pyrazinamidase (PZase) to the front-line drug pyrazinamide (PZA). The present study focused on investigating the resistance mechanism caused by the mutations D12N, T47A, and H137R to better understand the structural and molecular events responsible for the resistance acquired by the pncA gene of Mycobacterium tuberculosis (MTB) at the structural level. Bioinformatics analysis predicted that all three mutations were deleterious and located near the active centre of the pncA, affecting its functional activity. Furthermore, molecular dynamics simulation (MDS) results established that mutations significantly reduced the structural stability and caused the rearrangement of FE2+ in the active centre of pncA. Moreover, essential dynamics analysis, including principal component analysis (PCA) and free energy landscape (FEL), concluded variations in the protein motion and decreased conformational space in the mutants. Additionally, the mutations potentially impacted the network topologies and altered the residual communications in the network. The complex simulation study results established the significant movement of the flap region from the active centre of mutant complexes, further supporting the flap region's significance in developing resistance to the PZA drug. This study advances our knowledge of the primary cause of the mechanism of PZA resistance and the structural dynamics of pncA mutants, which will help us to design new and potent chemical scaffolds to treat drug-resistant TB (DR-TB).

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches

Background

Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis; however, antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, encoding an enzyme that converts pyrazinamide into its active form.

Methods

We curated a dataset of 664 non-redundant, missense amino acid mutations in PncA with associated high-confidence phenotypes from published studies and then trained three different machine-learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features.

Results

The best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates.

Conclusions

This work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.

Exploring of pyrazinamidase recombinant activity from PZA-sensitive and resistant Mycobacterium tuberculosis expressed in Escherichia coli BL21 (DE3)

The mutations of pncA gene encoding pyrazinamidase/PZase in Mycobacterium tuberculosis are often associated with pyrazinamide/PZA resistance. The H and R1 isolates showed significant phenotypic differences to PZA. The H isolate was PZA sensitive, but R1 was PZA resistant up to 100 ug/ml. The paper reports the pncA profile for both isolates and the activity of their protein expressed in Escherichia coli BL21(DE3). The 0.6 kb of each pncA genes have been subcloned successfully into the 5.4 kb pET30a vector and formed the pET30a-pncA recombinant with a size of 6.0 kb. The pncAR1 profile exhibited base mutations, but not for pncAH against to pncA from the PZA-sensitive M. tuberculosis H37RV published in Genbank ID: 888260. Three mutations were found in pncAR1, ie T41C, G419A, and A535G that subsequently changed amino acids of Cys14Arg, Arg140His and Ser179Gly in its protein level. The mutant PZase R1 that expressed as a 21 kDa protein in E. coli Bl21(DE3) lost 32% of its performance in activating PZA drug to pyrazinoic acid/POA compared to the wild-type PZase H. The mutation in the pncAR1 gene that followed by the decreasing of its PZase activity underlies the emergence of pyrazinamide resistance in the clinical isolate. Structural studies for the R1 mutant PZase protein should be further developed to reveal more precise drug resistance mechanisms and design more effective TB drugs.

Prevalence, Transmission and Genetic Diversity of Pyrazinamide Resistance Among Multidrug-Resistant Mycobacterium tuberculosis Isolates in Hunan, China

Background

China is a country with a burden of high rates of both TB and multidrug-resistant TB (MDR-TB). However, published data on pyrazinamide (PZA) resistance are still limited in Hunan province, China. This study investigated the prevalence, transmission, and genetic diversity of PZA resistance among multidrug-resistant Mycobacterium tuberculosis isolates in Hunan province.

Methods

Drug susceptibility testing (DST) with the Bactec MGIT 960 PZA kit and pyrazinamidase (PZase) testing were conducted on all 298 MDR clinical isolates. Moreover, 24-locus MIRU-VNTR and DNA sequencing of pncA, rpsA, and panD genes were conducted on 180 PZA-resistant (PZA-R) isolates.

Results

The prevalence of PZA resistance among MDR-TB strains reached 60.4%. Newly diagnosed PZA-R TB patients and clustered isolates with identical pncA, rpsA, and panD mutations showed that transmission of PZA-R isolates played a significant role in the formation of PZA-R TB. Ninety-eight mutation patterns were observed in the pncA among 180 PZA-R isolates, and seventy-one (72.4%) were point mutations. Twenty-four of these mutations are new, including 2 base substitutions (V93G and T153S) and 22 nucleotide deletions or insertions. The W119C was found in PZA-S isolates, on the other hand, F94L and V155A mutations were found in both PZA resistant and susceptible isolates with positive PZase activity, indicating that they were not associated with PZA resistance. This is not entirely in line with the WHO catalogue. Ten novel rpsA mutations were found in 10 PZA-R isolates, which all combined with mutations in pncA. Thus, it is unpredictable whether these mutations in rpsA can impact PZA resistance. No panD mutation was found in all PZA-R isolates.

Conclusion

DNA sequencing of pncA and PZase activity testing have great potential in predicting PZA resistance.

Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease

BACKGROUND

Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints.

METHODS

To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation.

RESULTS

Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches.

CONCLUSION

This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach

The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis. However, molecular diagnostics to date have focused largely on first-line drugs and predicting susceptibilities in a binary manner (classifying strains as either susceptible or resistant). Here, we used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration (MIC) in 15,211 Mycobacterium tuberculosis clinical isolates from 23 countries across five continents. We identified 492 unique MIC-elevating variants across 13 drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

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.

THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model

MOTIVATION

Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level structural information, it can help to understand how single-point variations cause functional changes.

RESULTS

Here, we proposed THPLM, a sequence-based deep learning model for stability change prediction using Meta's ESM-2. With ESM-2 and a simple convolutional neural network, THPLM achieved comparable or even better performance than most methods, including sequence-based and structure-based methods. Furthermore, the experimental results indicate that the PLM's ability to generate representations of sequence can effectively improve the ability of protein function prediction.

AVAILABILITY AND IMPLEMENTATION

The source code of THPLM and the testing data can be accessible through the following links: https://github.com/FPPGroup/THPLM.

Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach

The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis; however, molecular diagnostics to date have focused largely on first-line drugs and predicting binary susceptibilities. We used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration in 15,211 Mycobacterium tuberculosis patient isolates from 23 countries across five continents. This identified 492 unique MIC-elevating variants across thirteen drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.

Deciphering the emerging role of phytocompounds: Implications in the management of drug-resistant tuberculosis and ATDs-induced hepatic damage

Tuberculosis is a disease of poverty, discrimination, and socioeconomic burden. Epidemiological studies suggest that the mortality and incidence of tuberculosis are unacceptably higher worldwide. Genomic mutations in embCAB, embR, katG, inhA, ahpC, rpoB, pncA, rrs, rpsL, gyrA, gyrB, and ethR contribute to drug resistance reducing the susceptibility of Mycobacterium tuberculosis to many antibiotics. Additionally, treating tuberculosis with antibiotics also poses a serious risk of hepatotoxicity in the patient's body. Emerging data on drug-induced liver injury showed that anti-tuberculosis drugs remarkably altered levels of hepatotoxicity biomarkers. The review is an attempt to explore the anti-mycobacterial potential of selected, commonly available, and well-known phytocompounds and extracts of medicinal plants against strains of Mycobacterium tuberculosis. Many studies have demonstrated that phytocompounds such as flavonoids, alkaloids, terpenoids, and phenolic compounds have antibacterial action against Mycobacterium species, inhibiting the bacteria's growth and replication, and sometimes, causing cell death. Phytocompounds act by disrupting bacterial cell walls and membranes, reducing enzyme activity, and interfering with essential metabolic processes. The combination of these processes reduces the overall survivability of the bacteria. Moreover, several phytochemicals have synergistic effects with antibiotics routinely used to treat TB, improving their efficacy and decreasing the risk of resistance development. Interestingly, phytocompounds have been presented to reduce isoniazid- and ethambutol-induced hepatotoxicity by reversing serum levels of AST, ALP, ALT, bilirubin, MDA, urea, creatinine, and albumin to their normal range, leading to attenuation of inflammation and hepatic necrosis. As a result, phytochemicals represent a promising field of research for the development of new TB medicines.

Identifying Innate Resistance Hotspots for SARS-CoV-2 Antivirals Using In Silico Protein Techniques

The development and approval of antivirals against SARS-CoV-2 has further equipped clinicians with treatment strategies against the COVID-19 pandemic, reducing deaths post-infection. Extensive clinical use of antivirals, however, can impart additional selective pressure, leading to the emergence of antiviral resistance. While we have previously characterized possible effects of circulating SARS-CoV-2 missense mutations on proteome function and stability, their direct effects on the novel antivirals remains unexplored. To address this, we have computationally calculated the consequences of mutations in the antiviral targets: RNA-dependent RNA polymerase and main protease, on target stability and interactions with their antiviral, nucleic acids, and other proteins. By analyzing circulating variants prior to antiviral approval, this work highlighted the inherent resistance potential of different genome regions. Namely, within the main protease binding site, missense mutations imparted a lower fitness cost, while the opposite was noted for the RNA-dependent RNA polymerase binding site. This suggests that resistance to nirmatrelvir/ritonavir combination treatment is more likely to occur and proliferate than that to molnupiravir. These insights are crucial both clinically in drug stewardship, and preclinically in the identification of less mutable targets for novel therapeutic design.

Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis

BACKGROUND

Bedaquiline is a core drug for the treatment of multidrug-resistant tuberculosis; however, the understanding of resistance mechanisms is poor, which is hampering rapid molecular diagnostics. Some bedaquiline-resistant mutants are also cross-resistant to clofazimine. To decipher bedaquiline and clofazimine resistance determinants, we combined experimental evolution, protein modelling, genome sequencing, and phenotypic data.

METHODS

For this in-vitro and in-silico data analysis, we used a novel in-vitro evolutionary model using subinhibitory drug concentrations to select bedaquiline-resistant and clofazimine-resistant mutants. We determined bedaquiline and clofazimine minimum inhibitory concentrations and did Illumina and PacBio sequencing to characterise selected mutants and establish a mutation catalogue. This catalogue also includes phenotypic and genotypic data of a global collection of more than 14 000 clinical Mycobacterium tuberculosis complex isolates, and publicly available data. We investigated variants implicated in bedaquiline resistance by protein modelling and dynamic simulations.

FINDINGS

We discerned 265 genomic variants implicated in bedaquiline resistance, with 250 (94%) variants affecting the transcriptional repressor (Rv0678) of the MmpS5-MmpL5 efflux system. We identified 40 new variants in vitro, and a new bedaquiline resistance mechanism caused by a large-scale genomic rearrangement. Additionally, we identified in vitro 15 (7%) of 208 mutations found in clinical bedaquiline-resistant isolates. From our in-vitro work, we detected 14 (16%) of 88 mutations so far identified as being associated with clofazimine resistance and also seen in clinically resistant strains, and catalogued 35 new mutations. Structural modelling of Rv0678 showed four major mechanisms of bedaquiline resistance: impaired DNA binding, reduction in protein stability, disruption of protein dimerisation, and alteration in affinity for its fatty acid ligand.

INTERPRETATION

Our findings advance the understanding of drug resistance mechanisms in M tuberculosis complex strains. We have established an extended mutation catalogue, comprising variants implicated in resistance and susceptibility to bedaquiline and clofazimine. Our data emphasise that genotypic testing can delineate clinical isolates with borderline phenotypes, which is essential for the design of effective treatments.

FUNDING

Leibniz ScienceCampus Evolutionary Medicine of the Lung, Deutsche Forschungsgemeinschaft, Research Training Group 2501 TransEvo, Rhodes Trust, Stanford University Medical Scientist Training Program, National Institute for Health and Care Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Bill & Melinda Gates Foundation, Wellcome Trust, and Marie Skłodowska-Curie Actions.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

Prediction of protein stability changes upon single-point variant using 3D structure profile

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

Drug Degradation Caused by mce3R Mutations Confers Contezolid (MRX-I) Resistance in Mycobacterium tuberculosis

Contezolid (MRX-I), a safer antibiotic of the oxazolidinone class, is a promising new antibiotic with potent activity against Mycobacterium tuberculosis (MTB) both in vitro and in vivo. To identify resistance mechanisms of contezolid in MTB, we isolated several in vitro spontaneous contezolid-resistant MTB mutants, which exhibited 16-fold increases in the MIC of contezolid compared with the parent strain but were still unexpectedly susceptible to linezolid. Whole-genome sequencing revealed that most of the contezolid-resistant mutants bore mutations in the mce3R gene, which encodes a transcriptional repressor. The mutations in mce3R led to markedly increased expression of a monooxygenase encoding gene Rv1936. We then characterized Rv1936 as a putative flavin-dependent monooxygenase that catalyzes the degradation of contezolid into its inactive 2,3-dihydropyridin-4-one (DHPO) ring-opened metabolites, thereby conferring drug resistance. While contezolid is an attractive drug candidate with potent antimycobacterial activity and low toxicity, the occurrence of mutations in Mce3R should be considered when designing combination therapy using contezolid for treating tuberculosis.

Detection of Mutations in pncA in Mycobacterium tuberculosis Clinical Isolates from Nepal in Association with Pyrazinamide Resistance

Without the proper information on pyrazinamide (PZA) susceptibility of Mycobacterium tuberculosis (MTB), PZA is inappropriately recommended for the treatment of both susceptible and multidrug-resistant tuberculosis (MDR-TB) in Nepal. This study aimed to collect information regarding PZA susceptibility in MTB isolates from Nepal by analyzing pncA and its upstream regulatory region (URR). A total of 211 MTB isolates were included in this study. Sequence analysis of pncA and its URR was performed to assess PZA resistance. First-line drug susceptibility testing, spoligotyping, and sequence analysis of rpoB, katG, the inhA regulatory region, gyrA, gyrB, and rrs were performed to assess their association with pncA mutation. Sequencing results reveal that 125 (59.2%) isolates harbored alterations in pncA and its URR. A total of 57 different mutation types (46 reported and 11 novel) were scattered throughout the whole length of the pncA gene. Eighty-seven isolates (41.2%) harbored mutations in pncA, causing PZA resistance in MTB. There was a more significant association of pncA alterations in MDR/pre-extensively drug-resistant (Pre-XDR) TB than in mono-resistant/pan-susceptible TB (p < 0.005). This first report on the increasing level of PZA resistance in DR-TB in Nepal highlights the importance of PZA susceptibility testing before DR-TB treatment.

Intra-host genetic population diversity: Role in emergence and persistence of drug resistance among Mycobacterium tuberculosis complex minor variants

Drug resistant tuberculosis (DR-TB) is an important public health issue in different parts of the world. Mycobacterium tuberculosis complex variants (MTBC vars) preferentially infect certain hosts, limiting their distribution to different ecosystems. However, MTBC vars can infect other hosts beyond their preferred target potentially contributing to persistence of drug resistance (DR) in other niches. Here, we performed a comprehensive intra-host genetic analysis for the identification of DR-related mutations among all MTBC minor vars whole genome sequences (8,095 strains) publicly available worldwide. High confidence drug-resistance mutations in katG (isoniazid), rpsL (streptomycin), pncA (pyrazinamide), rpoB (rifampicin) and gyrA (fluoroquinolones) genes were identified among intrahost minor sub-populations in 197 different strains (2.43%) belonging to vars africanum, bovis, caprae, microti, orygis and pinnipedii. In addition, a three-dimensional structure modeling analysis to assess the role of novel mutations was also performed. Our findings highlight the importance of detecting discrete intra-host populations carrying DR mutations.

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures

Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.

Estimating tuberculosis drug resistance amplification rates in high-burden settings

Background

Antimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not capture elements of this important aspect of TB epidemiology. To understand and estimate the likelihood of resistance spreading in high drug-resistant TB incidence settings, we used epidemiological data to develop a mathematical model of Mycobacterium tuberculosis (Mtb) transmission.

Methods

A four-strain (drug-susceptible (DS), isoniazid mono-resistant (INH-R), rifampicin mono-resistant (RIF-R) and multidrug-resistant (MDR)) compartmental deterministic Mtb transmission model was developed to explore the progression from DS- to MDR-TB in The Philippines and Viet Nam. The models were calibrated using data from national tuberculosis prevalence (NTP) surveys and drug resistance surveys (DRS). An adaptive Metropolis algorithm was used to estimate the risks of drug resistance amplification among unsuccessfully treated individuals.

Results

The estimated proportion of INH-R amplification among failing treatments was 0.84 (95% CI 0.79–0.89) for The Philippines and 0.77 (95% CI 0.71–0.84) for Viet Nam. The proportion of RIF-R amplification among failing treatments was 0.05 (95% CI 0.04–0.07) for The Philippines and 0.011 (95% CI 0.010–0.012) for Viet Nam.

Conclusion

The risk of resistance amplification due to treatment failure for INH was dramatically higher than RIF. We observed RIF-R strains were more likely to be transmitted than acquired through amplification, while both mechanisms of acquisition were important contributors in the case of INH-R. These findings highlight the complexity of drug resistance dynamics in high-incidence settings, and emphasize the importance of prioritizing testing algorithms which allow for early detection of INH-R.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07067-1.

HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in the body. Affected individuals lack functional levels of an enzyme required to breakdown HGA. Mutations in the homogentisate 1,2-dioxygenase (HGD) gene cause AKU and they are responsible for deficient levels of functional HGD, which, in turn, leads to excess levels of HGA. Although HGA is rapidly cleared from the body by the kidneys, in the long term it starts accumulating in various tissues, especially cartilage. Over time (rarely before adulthood), it eventually changes the color of affected tissue to slate blue or black. Here we report a comprehensive mutation analysis of 111 pathogenic and 190 non-pathogenic HGD missense mutations using protein structural information. Using our comprehensive suite of graph-based signature methods, mCSM complemented with sequence-based tools, we studied the functional and molecular consequences of each mutation on protein stability, interaction and evolutionary conservation. The scores generated from the structure and sequence-based tools were used to train a supervised machine learning algorithm with 89% accuracy. The empirical classifier was used to generate the variant phenotype for novel HGD missense mutations. All this information is deployed as a user friendly freely available web server called HGDiscovery (https://biosig.lab.uq.edu.au/hgdiscovery/).

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Functional and phenotypic consequences of HGD non-synonymous variations.

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Biophysical, structural and evolutionary analysis of novel and known clinical variants.

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Pathogenic mutations affected protein stability and conformational flexibility.

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Pathogenic mutations associated with deleterious scores for sequence-based features.

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HGDiscovery (http://biosig.unimelb.edu.au/hgdiscovery/) – webserver.

Next-generation sequencing to characterize pyrazinamide resistance in Mycobacterium tuberculosis isolates from two Balkan countries

OBJECTIVES

Next-generation sequencing (NGS) provide a comprehensive analysis of the genetic alterations that are most commonly linked with pyrazinamide (PZA) resistance. There are no studies reporting molecular background of PZA resistance in TB isolates from Balkan Peninsula. We aimed to examine the feasibility of full-length analysis of a gene linked with PZA resistance, pncA, using Ion Torrent technology in comparison to phenotypic BACTEC MGIT 960 DST in clinical TB isolates from two countries of the Balkan Peninsula.

METHODS

Between 1996 and 2017, we retrospectively selected 61 TB isolates. To identify gene variants related to drug resistance in genomic DNA extracted from TB isolates, AmpliSeq libraries were generated automatically using the AmpliSeq™ Kit for Chef DL8 and the Ion AmpliSeq TB Research Panel.

RESULTS

Of all 61 TB isolates included, 56 TB were phenotypically resistant to any antibiotic. Among them, 38/56 (67.9%) TB isolates were phenotypically resistant to pyrazinamide and pncA mutations were detected in 33/38 cases (86.8%). A mutation in the pncA promoter region was the most prevalent genetic alteration, detected in eight TB isolates. Comparison of NGS to conventional BACTEC MGIT 960 DST revealed very strong agreement (90.2%) between the two methods in identifying PZA resistance, with high sensitivity (89.5%) and specificity (95.7%) for NGS.

CONCLUSIONS

Detection of PZA resistance using NGS seems to be a valuable tool for surveillance of TB drug resistance also in the Balkan Peninsula, with great potential to provide useful information at least one weak earlier than is possible with phenotypic DST.

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

Kinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/.

Antibiotic resistance lessons for the herbicide resistance crisis

The challenges of resistance to antibiotics and resistance to herbicides have much in common. Antibiotic resistance became a risk in the 1950s, but a concerted global effort to manage it did not begin until after 2000. Widespread herbicide use began during the 1950s and was soon followed by an unabated rise in resistance. Here, we examine what lessons for combatting herbicide resistance could be learnt from the global, coordinated efforts of all stakeholders to avert the antibiotic resistance crisis. © 2021 Society of Chemical Industry.

Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

Resistance to drugs used to treat tuberculosis disease (TB) continues to remain a public health burden, with missense point mutations in the underlying Mycobacterium tuberculosis bacteria described for nearly all anti-TB drugs. The post-genomics era along with advances in computational and structural biology provide opportunities to understand the interrelationships between the genetic basis and the structural consequences of M. tuberculosis mutations linked to drug resistance. Pyrazinamide (PZA) is a crucial first line antibiotic currently used in TB treatment regimens. The mutational promiscuity exhibited by the pncA gene (target for PZA) necessitates computational approaches to investigate the genetic and structural basis for PZA resistance development. We analysed 424 missense point mutations linked to PZA resistance derived from ∼35K M. tuberculosis clinical isolates sourced globally, which comprised the four main M. tuberculosis lineages (Lineage 1-4). Mutations were annotated to reflect their association with PZA resistance. Genomic measures (minor allele frequency and odds ratio), structural features (surface area, residue depth and hydrophobicity) and biophysical effects (change in stability and ligand affinity) of point mutations on pncA protein stability and ligand affinity were assessed. Missense point mutations within pncA were distributed throughout the gene, with the majority (>80%) of mutations with a destabilising effect on protomer stability and on ligand affinity. Active site residues involved in PZA binding were associated with multiple point mutations highlighting mutational diversity due to selection pressures at these functionally important sites. There were weak associations between genomic measures and biophysical effect of mutations. However, mutations associated with PZA resistance showed statistically significant differences between structural features (surface area and residue depth), but not hydrophobicity score for mutational sites. Most interestingly M. tuberculosis lineage 1 (ancient lineage) exhibited a distinct protein stability profile for mutations associated with PZA resistance, compared to modern lineages.

Tuberculosis: An Update on Pathophysiology, Molecular Mechanisms of Drug Resistance, Newer Anti-TB Drugs, Treatment Regimens and Host- Directed Therapies

Human tuberculosis (TB) is primarily caused by Mycobacterium tuberculosis (Mtb) that inhabits inside and amidst immune cells of the host with adapted physiology to regulate interdependent cellular functions with intact pathogenic potential. The complexity of this disease is attributed to various factors such as the reactivation of latent TB form after prolonged persistence, disease progression specifically in immunocompromised patients, advent of multi- and extensivelydrug resistant (MDR and XDR) Mtb strains, adverse effects of tailor-made regimens, and drug-drug interactions among anti-TB drugs and anti-HIV therapies. Thus, there is a compelling demand for newer anti-TB drugs or regimens to overcome these obstacles. Considerable multifaceted transformations in the current TB methodologies and molecular interventions underpinning hostpathogen interactions and drug resistance mechanisms may assist to overcome the emerging drug resistance. Evidently, recent scientific and clinical advances have revolutionised the diagnosis, prevention, and treatment of all forms of the disease. This review sheds light on the current understanding of the pathogenesis of TB disease, molecular mechanisms of drug-resistance, progress on the development of novel or repurposed anti-TB drugs and regimens, host-directed therapies, with particular emphasis on underlying knowledge gaps and prospective for futuristic TB control programs.

Overcoming the Challenges of Pyrazinamide Susceptibility Testing in Clinical Mycobacterium tuberculosis Isolates

Pyrazinamide (PZA) is one of the first-line agents used for the treatment of tuberculosis. However, current phenotypic PZA susceptibility testing in the Bactec MGIT 960 system is unreliable, and false resistance is well documented. Rapid identification of resistance-associated mutations can confirm the phenotypic result. This study aimed to investigate the use of genotypic methods in combination with phenotypic susceptibility testing for confirmation of PZA-resistant Mycobacterium tuberculosis isolates. Sanger sequencing and/or whole-genome sequencing were performed to detect mutations in pncA, rpsA, panD, and clpC1. Isolates were screened for heteroresistance, and PZA susceptibility testing was performed using the Bactec MGIT 960 system using a reduced inoculum to investigate false resistance. Overall, 40 phenotypically PZA-resistant isolates were identified. Of these, PZA resistance was confirmed in 22/40 (55%) isolates by detecting mutations in the pncA, rpsA, and panD genes. Of the 40 isolates, 16 (40%) were found to be susceptible using the reduced inoculum method (i.e., false resistance). No mutations were detected in two PZA-resistant isolates. False resistance was observed in isolates with MICs close to the critical concentration. In particular, East African Indian strains (lineage 1) appeared to have an elevated MIC that is close to the critical concentration. While this study illustrates the complexity and challenges associated with PZA susceptibility testing of M. tuberculosis, we conclude that a combination of genotypic and phenotypic drug susceptibility testing methods is required for accurate detection of PZA resistance.

mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions

Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.

Distinguishing between PTEN clinical phenotypes through mutation analysis

Phosphate and tensin homolog on chromosome ten (PTEN) germline mutations are associated with an overarching condition known as PTEN hamartoma tumor syndrome. Clinical phenotypes associated with this syndrome range from macrocephaly and autism spectrum disorder to Cowden syndrome, which manifests as multiple noncancerous tumor-like growths (hamartomas), and an increased predisposition to certain cancers. It is unclear, however, the basis by which mutations might lead to these very diverse phenotypic outcomes. Here we show that, by considering the molecular consequences of mutations in PTEN on protein structure and function, we can accurately distinguish PTEN mutations exhibiting different phenotypes. Changes in phosphatase activity, protein stability, and intramolecular interactions appeared to be major drivers of clinical phenotype, with cancer-associated variants leading to the most drastic changes, while ASD and non-pathogenic variants associated with more mild and neutral changes, respectively. Importantly, we show via saturation mutagenesis that more than half of variants of unknown significance could be associated with disease phenotypes, while over half of Cowden syndrome mutations likely lead to cancer. These insights can assist in exploring potentially important clinical outcomes delineated by PTEN variation.

Insights Into Mutations Induced Conformational Changes and Rearrangement of Fe2+ Ion in pncA Gene of Mycobacterium tuberculosis to Decipher the Mechanism of Resistance to Pyrazinamide

Pyrazinamide (PZA) is the first-line drug commonly used in treating Mycobacterium tuberculosis (Mtb) infections and reduces treatment time by 33%. This prodrug is activated and converted to an active form, Pyrazinoic acid (POA), by Pyrazinamidase (PZase) enzyme. Mtb resistance to PZA is the outcome of mutations frequently reported in pncA, rpsA, and panD genes. Among the mentioned genes, pncA mutations contribute to 72-99% of the total resistance to PZA. Thus, considering the vital importance of this gene in PZA resistance, its frequent mutations (D49N, Y64S, W68G, and F94A) were investigated through in-depth computational techniques to put conclusions that might be useful for new scaffolds design or structure optimization to improve the efficacy of the available drugs. Mutants and wild type PZase were used in extensive and long-run molecular dynamics simulations in triplicate to disclose the resistance mechanism induced by the above-mentioned point mutations. Our analysis suggests that these mutations alter the internal dynamics of PZase and hinder the correct orientation of PZA to the enzyme. Consequently, the PZA has a low binding energy score with the mutants compared with the wild type PZase. These mutations were also reported to affect the binding of Fe2+ ion and its coordinated residues. Conformational dynamics also revealed that β-strand two is flipped, which is significant in Fe2+ binding. MM-GBSA analysis confirmed that these mutations significantly decreased the binding of PZA. In conclusion, these mutations cause conformation alterations and deformities that lead to PZA resistance.

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.

Value of routine whole genome sequencing for Mycobacterium tuberculosis drug resistance detection

Routine whole genome sequencing (WGS) of pathogens is becoming more feasible as sequencing costs decrease and access to benchtop sequencing equipment and bioinformatics pipelines increases. This study examined the added value gained from implementing routine WGS of all Mycobacterium tuberculosis isolates in New South Wales, Australia. Drug resistance markers inferred from WGS data were compared to commercial genotypic drug susceptibility testing (DST) assays and conventional phenotypic DST in all isolates sequenced between 2016 and 2019. Of the 1107 clinical M. tuberculosis isolates sequenced, 29 (2.6%) were multi-drug resistant (MDR); most belonged to Beijing (336; 30.4%) or East-African Indian (332; 30%) lineages. Compared with conventional phenotypic DST, WGS identified an additional 1% of isolates which were likely drug resistant, explained by mutations previously associated with treatment failure and mixed bacterial populations. However, WGS provided a 20% increase in drug resistance detection in comparison with commercial genotypic assays by identifying mutations outside of the classic resistance determining regions in rpoB, inhA, katG, pncA and embB genes. Gains in drug resistance detection were significant (p = 0.0137, paired t-test), but varied substantially for different phylogenetic lineages. In low incidence settings, routine WGS of M. tuberculosis provides better guidance for person-centered management of drug resistant tuberculosis than commercial genotypic assays.

Genome analyses of 174 strains of Mycobacterium tuberculosis provide insight into the evolution of drug resistance and reveal potential drug targets

Mycobacterium tuberculosis is a known human pathogen that causes the airborne infectious disease tuberculosis (TB). Every year TB infects millions of people worldwide. The emergence of multi-drug resistant (MDR), extensively drug resistant (XDR) and totally drug resistant (TDR) M. tuberculosis strains against the first- and second-line anti-TB drugs has created an urgent need for the development and implementation of new drug strategies. In this study, the complete genomes of 174 strains of M. tuberculosis are analysed to understand the evolution of molecular drug target (MDT) genes. Phylogenomic placements of M. tuberculosis strains depicted close association and temporal clustering. Selection pressure analysis by deducing the ratio of non-synonymous to synonymous substitution rates (dN/dS) in 51 MDT genes of the 174 M. tuberculosis strains led to categorizing these genes into diversifying (D, dN/dS>0.70), moderately diversifying (MD, dN/dS=0.35-0.70) and stabilized (S, dN/dS<0.35) genes. The genes rpsL, gidB, pncA and ahpC were identified as diversifying, and Rv0488, kasA, ndh, ethR, ethA, embR and ddn were identified as stabilized genes. Furthermore, sequence similarity networks were drawn that supported these divisions. In the multiple sequence alignments of diversifying and stabilized proteins, previously reported resistance mutations were checked to predict sensitive and resistant strains of M. tuberculosis. Finally, to delineate the potential of stabilized or least diversified genes/proteins as anti-TB drug targets, protein-protein interactions of MDT proteins with human proteins were analysed. We predict that kasA (dN/dS=0.29), a stabilized gene that encodes the most host-interacting protein, KasA, should serve as a potential drug target for the treatment of TB.

ThermoMutDB: a thermodynamic database for missense mutations

Proteins are intricate, dynamic structures, and small changes in their amino acid sequences can lead to large effects on their folding, stability and dynamics. To facilitate the further development and evaluation of methods to predict these changes, we have developed ThermoMutDB, a manually curated database containing >14,669 experimental data of thermodynamic parameters for wild type and mutant proteins. This represents an increase of 83% in unique mutations over previous databases and includes thermodynamic information on 204 new proteins. During manual curation we have also corrected annotation errors in previously curated entries. Associated with each entry, we have included information on the unfolding Gibbs free energy and melting temperature change, and have associated entries with available experimental structural information. ThermoMutDB supports users to contribute to new data points and programmatic access to the database via a RESTful API. ThermoMutDB is freely available at: http://biosig.unimelb.edu.au/thermomutdb.

Characterization of pncA Mutations and Prediction of PZA Resistance in Mycobacterium tuberculosis Clinical Isolates From Chongqing, China

Pyrazinamide (PZA) is widely used to treat drug-sensitive or multidrug resistance tuberculosis. However, conventional PZA susceptibility tests of clinical isolates are rather difficult because of the requirement of acid pH. Since resistance to pyrazinamide is primary mediated by mutation of pncA, an alternative way of PZA susceptibility test is to analyze the pyrazinamidase activities of Mycobacterium tuberculosis clinical isolates. Therefore, a database containing the full spectrum of pncA mutations along with pyrazinamidase activities will be beneficial. To characterize mutations of pncA in M. tuberculosis from Chongqing, China, the pncA gene was sequenced and analyzed in 465 clinical isolates. A total of 124 types of mutations were identified in 424 drug-resistant isolates, while no mutation was identified in the 31 pan-susceptible isolates. Ninety-four of the 124 mutations had previously been reported, and 30 new mutations were identified. Based on reported literatures, 294 isolates could be predicted resistant to pyrazinamide. Furthermore, pyrazinamidase activities of the 30 new mutations were tested using the Escherichia coli pncA gene knockout strain. The results showed that 24 of these new mutations (28 isolates) led to loss of pyrazinamidase activity and six (8 isolates) of them did not. Taken together, 322 isolates with pncA mutations could be predicted to be PZA resistant among the 424 drug-resistant isolates tested. Analysis of pncA mutations and their effects on pyrazinamidase activity will not only enrich our knowledge of comprehensive pncA mutations related with PZA resistance but also facilitate rapid molecular diagnosis of pyrazinamide resistance in M. tuberculosis.

Novel Approaches for the Treatment of Pulmonary Tuberculosis

Tuberculosis (TB) is a contagious airborne disease caused by Mycobacterium tuberculosis, which primarily affects human lungs. The progression of drug-susceptible TB to drug-resistant strains, MDR-TB and XDR-TB, has become worldwide challenge in eliminating TB. The limitations of conventional TB treatment including frequent dosing and prolonged treatment, which results in patient’s noncompliance to the treatment because of treatment-related adverse effects. The non-invasive pulmonary drug administration provides the advantages of targeted-site delivery and avoids first-pass metabolism, which reduced the dose requirement and systemic adverse effects of the therapeutics. With the modification of the drugs with advanced carriers, the formulations may possess sustained released property, which helps in reducing the dosing frequency and enhanced patients’ compliances. The dry powder inhaler formulation is easy to handle and storage as it is relatively stable compared to liquids and suspension. This review mainly highlights the aerosolization properties of dry powder inhalable formulations with different anti-TB agents to understand and estimate the deposition manner of the drug in the lungs. Moreover, the safety profile of the novel dry powder inhaler formulations has been discussed. The results of the studies demonstrated that dry powder inhaler formulation has the potential in enhancing treatment efficacy.

HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.

Combining structure and genomics to understand antimicrobial resistance

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .

mCSM-membrane: predicting the effects of mutations on transmembrane proteins

Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔT_m). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

Prediction of Mycobacterium tuberculosis pyrazinamidase function based on structural stability, physicochemical and geometrical descriptors

BACKGROUND

Pyrazinamide is an important drug against the latent stage of tuberculosis and is used in both first- and second-line treatment regimens. Pyrazinamide-susceptibility test usually takes a week to have a diagnosis to guide initial therapy, implying a delay in receiving appropriate therapy. The continued increase in multi-drug resistant tuberculosis and the prevalence of pyrazinamide resistance in several countries makes the development of assays for prompt identification of resistance necessary. The main cause of pyrazinamide resistance is the impairment of pyrazinamidase function attributed to mutations in the promoter and/or pncA coding gene. However, not all pncA mutations necessarily affect the pyrazinamidase function.

OBJECTIVE

To develop a methodology to predict pyrazinamidase function from detected mutations in the pncA gene.

METHODS

We measured the catalytic constant (kcat), KM, enzymatic efficiency, and enzymatic activity of 35 recombinant mutated pyrazinamidase and the wild type (Protein Data Bank ID = 3pl1). From all the 3D modeled structures, we extracted several predictors based on three categories: structural stability (estimated by normal mode analysis and molecular dynamics), physicochemical, and geometrical characteristics. We used a stepwise Akaike's information criterion forward multiple log-linear regression to model each kinetic parameter with each category of predictors. We also developed weighted models combining the three categories of predictive models for each kinetic parameter. We tested the robustness of the predictive ability of each model by 6-fold cross-validation against random models.

RESULTS

The stability, physicochemical, and geometrical descriptors explained most of the variability (R2) of the kinetic parameters. Our models are best suited to predict kcat, efficiency, and activity based on the root-mean-square error of prediction of the 6-fold cross-validation.

CONCLUSIONS

This study shows a quick approach to predict the pyrazinamidase function only from the pncA sequence when point mutations are present. This can be an important tool to detect pyrazinamide resistance.