Empirical ways to identify novel Bedaquiline resistance mutations in AtpE

Cross-District Transmission of Tuberculosis in a High-Mobility City in China: Implications for Regional Collaboration in Infectious Disease Control

Objective

This study aims to elucidate the transmission dynamics of tuberculosis in a Chinese city with high population mobility and to identify the associated risk factors.

Methods

We included the data from ten city-level surveillance sites in Shenzhen between 2018 and 2023. Genomic clusters were defined as having a genomic distance of 12 single nucleotide polymorphisms based on whole-genome sequencing. Cross-district clusters were characterized as clusters containing patients from at least two districts, indicating cross-district transmission. Risk factors for clustering were identified using logistic regression.

Results

Of the 2,519 enrolled patients, 263 (10.4%) were grouped into 119 genomic clusters. Notably, 52.1% (62/119) of these clusters were cross-district clusters. We analyzed the data from Shenzhen's 10 districts separately and compared the results with a citywide combined analysis, finding that the combined analysis revealed significantly higher clustering rates across all districts (P<0.001). Furthermore, the risk of cross-district transmission was 3.41 times higher (95% CI: 1.49-7.80) among internal migrants than among residents. Multivariable logistic regression analysis identified significant risk factors for TB transmission, including age under 25 years (OR=3.07, 95% CI: 1.17-8.03), age 25-44 years (OR=2.86, 95% CI: 1.13-7.23), and drug-resistant TB (OR=1.57, 95% CI: 1.15-2.13).

Conclusion

Cross-district transmission is a key factor in the spread of tuberculosis in cities with high population mobility. TB control institutions at all levels must transcend regional boundaries and enhance collaboration to achieve more effective tuberculosis control.

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.

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/.

Identification of Mycobacterium tuberculosis Resistance to Common Antibiotics: An Overview of Current Methods and Techniques

Multidrug-resistant tuberculosis (MDR-TB) is an essential cause of tuberculosis treatment failure and death of tuberculosis patients. The rapid and reliable profiling of Mycobacterium tuberculosis (MTB) drug resistance in the early stage is a critical research area for public health. Then, most traditional approaches for detecting MTB are time-consuming and costly, leading to the inappropriate therapeutic schedule resting on the ambiguous information of MTB drug resistance, increasing patient economic burden, morbidity, and mortality. Therefore, novel diagnosis methods are frequently required to meet the emerging challenges of MTB drug resistance distinguish. Considering the difficulty in treating MDR-TB, it is urgently required for the development of rapid and accurate methods in the identification of drug resistance profiles of MTB in clinical diagnosis. This review discussed recent advances in MTB drug resistance detection, focusing on developing emerging approaches and their applications in tangled clinical situations. In particular, a brief overview of antibiotic resistance to MTB was present, referred to as intrinsic bacterial resistance, consisting of cell wall barriers and efflux pumping action and acquired resistance caused by genetic mutations. Then, different drug susceptibility test (DST) methods were described, including phenotype DST, genotype DST and novel DST methods. The phenotype DST includes nitrate reductase assay, RocheTM solid ratio method, and liquid culture method and genotype DST includes fluorescent PCR, GeneXpert, PCR reverse dot hybridization, ddPCR, next-generation sequencing and gene chips. Then, novel DST methods were described, including metabolism testing, cell-free DNA probe, CRISPR assay, and spectral analysis technique. The limitations, challenges, and perspectives of different techniques for drug resistance are also discussed. These methods significantly improve the detection sensitivity and accuracy of multidrug-resistant tuberculosis (MRT) and can effectively curb the incidence of drug-resistant tuberculosis and accelerate the process of tuberculosis eradication.

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.

Identification of genetic determinants of bedaquiline resistance in Mycobacterium tuberculosis in Ural region, Russia

Collecting data on rare Mycobacterium tuberculosis (Mtb) clinical isolates with resistance to the new anti-tuberculosis drug bedaquiline is an important task for improving antimicrobial susceptibility testing methods. Nanopore whole genome sequencing, the proportion method on Middlebrook 7H11 medium, and BACTEC MGIT 960 assays were used to analyze genotypic and phenotypic resistance to bedaquiline. We found four mutations: atpE I66M, atpE А63Р, Rv0678 А36Т, and Rv0678 S53P in five isolates with different levels of phenotypic bedaquiline resistance.

IMPORTANCE

Bedaquiline (BDQ) is a new anti-tuberculosis drug. The phenotypic and genotypic data describing the mechanism of drug resistance are critical for the design of rapid and accurate diagnostic tests. We consider that our work, which describes genotypic and phenotypic resistance to BDQ, can contribute to the standardization of drug susceptibility testing.

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.

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.

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.

In silico structural and mechanical insights into bedaquiline resistance associated with high-grade non-synonymous mutations in atpE, mmpR5, and pepQ

Clinical resistance against bedaquiline (BDQ) remains intractable to anti-tuberculosis therapies since its introduction to the market over a decade ago. Herein, we investigated the structural and mechanical aspects of BDQ resistance in AtpE, MmpR5, and PepQ. The known target-specific resistant single non-synonymous mutations were refined to high-grade candidates. Thus, 7 (AtpE), 5 (MmpR5), and 1 (PepQ) single nucleotide polymorphisms (SNPs) and one insertion frameshift mutation in MmpR5 were recreated at the molecular level, and these phenotypic models were then directed to stringent dynamics to define time-scaled changes. The AtpE variants destabilized the structure; mainly, L59V, E61D, and I66M were detrimental to the complex fitness, while L74V and L114P boosted the BDQ binding to MmpR5. The first three and last two alterations gave rise to loss- and gain-of-function to AtpE and MmpR5, respectively. Hence, these five mutants are functionally relevant and therapeutically targetable hotspots of BDQ resistance. There were no noticeable changes in PepQ data analysis. The present study revealed that MmpR5 mutations confer BDQ resistance, whereas AtpE and PepQ SNPs display low susceptibility. These results were tallied with the published findings, which testified to the pursued method's reliability and accuracy. We hope these data and inferences could be helpful for the futuristic design of novel TB drugs.Communicated by Ramaswamy H. Sarma.

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.

Site-directed mutagenesis of Mycobacterium tuberculosis and functional validation to investigate potential bedaquiline resistance-causing mutations

Molecular detection of bedaquiline resistant tuberculosis is challenging as only a small proportion of mutations in candidate bedaquiline resistance genes have been statistically associated with phenotypic resistance. We introduced two mutations, atpE Ile66Val and Rv0678 Thr33Ala, in the Mycobacterium tuberculosis H37Rv reference strain using homologous recombineering or recombination to investigate the phenotypic effect of these mutations. The genotype of the resulting strains was confirmed by Sanger- and whole genome sequencing, and bedaquiline susceptibility was assessed by minimal inhibitory concentration (MIC) assays. The impact of the mutations on protein stability and interactions was predicted using mutation Cutoff Scanning Matrix (mCSM) tools. The atpE Ile66Val mutation did not elevate the MIC above the critical concentration (MIC 0.25-0.5 µg/ml), while the MIC of the Rv0678 Thr33Ala mutant strains (> 1.0 µg/ml) classifies the strain as resistant, confirming clinical findings. In silico analyses confirmed that the atpE Ile66Val mutation minimally disrupts the bedaquiline-ATP synthase interaction, while the Rv0678 Thr33Ala mutation substantially affects the DNA binding affinity of the MmpR transcriptional repressor. Based on a combination of wet-lab and computational methods, our results suggest that the Rv0678 Thr33Ala mutation confers resistance to BDQ, while the atpE Ile66Val mutation does not, but definite proof can only be provided by complementation studies given the presence of secondary mutations.

Mycobacterium tuberculosis - atpE gene profile of bedaquiline-treated pulmonary tuberculosis patients at the referral hospital Dr. Soetomo, Indonesia

Background

The atpE gene is a target for bedaquiline (Bdq)-activating drug action and mutations in the gene are fixed to cause resistance. However, changes in the amino acid of ATPase have been little reported from a clinical setting since it was first used in 2015 in Indonesia. This study aims to observe the sequence of nucleotide and amino acid from rifampicin-resistant (RR) pulmonary tuberculosis (TB) patients, both new and relapse cases treated with Bdq.

Methods

This is an observational descriptive study performed in the referral hospital Dr Soetomo, Indonesia, at August 2022-November 2022. We performed Sanger sequencing and comparison of the atpE gene from the patient's sputum from August to November 2022 to wild-type Mycobacterium tuberculosis H37Rv and species of mycobacteria using BioEdit version 7.2 and BLAST NCBI software. We also conducted an epidemiological study on patients' characteristics. This study uses a descriptive statistic to show the percentage of data.

Results

The total of 12 M. tuberculosis isolates showed that the atpE gene sequence was 100% similar to the wild-type M. tuberculosis H37Rv. No single-nucleotide polymorphisms or mutations were found, and no change in the amino acid structure at position 28 (Asp), 61 (Glu), 63 (Ala), and 66 (Ile). The percentage identity of atpE to M. tuberculosis H37Rv and M. tuberculosis complex was 99%-100%, while the similarity with the other mycobacteria species other than TB (Mycobacterium avium complex, Mycobacterium abscessus, and Mycobacterium lepraemurium) was 88%-91%.

Conclusions

This study revealed M. tuberculosis -atpE gene sequence profile of RR-TB patients had no mutations, as the specific gene region, and no change in the amino acid structure. Therefore, Bdq can be continually trusted as an effective anti-tubercular drug in RR-TB patients.

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.

A CRISPR-guided mutagenic DNA polymerase strategy for the detection of antibiotic-resistant mutations in M. tuberculosis

A sharp increase in multidrug-resistant tuberculosis (MDR-TB) threatens human health. Spontaneous mutation in essential gene confers an ability of Mycobacterium tuberculosis resistance to anti-TB drugs. However, conventional laboratory strategies for identification and prediction of the mutations in this slowly growing species remain challenging. Here, by combining XCas9 nickase and the error-prone DNA polymerase A from M. tuberculosis, we constructed a CRISPR-guided DNA polymerase system, CAMPER, for effective site-directed mutagenesis of drug-target genes in mycobacteria. CAMPER was able to generate mutagenesis of all nucleotides at user-defined loci, and its bidirectional mutagenesis at nick sites allowed editing windows with lengths up to 80 nucleotides. Mutagenesis of drug-targeted genes in Mycobacterium smegmatis and M. tuberculosis with this system significantly increased the fraction of the antibiotic-resistant bacterial population to a level approximately 60- to 120-fold higher than that in unedited cells. Moreover, this strategy could facilitate the discovery of the mutation conferring antibiotic resistance and enable a rapid verification of the growth phenotype-mutation genotype association. Our data demonstrate that CAMPER facilitates targeted mutagenesis of genomic loci and thus may be useful for broad functions such as resistance prediction and development of novel TB therapies.

Naringenin as a potential inhibitor of human cyclin-dependent kinase 6: Molecular and structural insights into anti-cancer therapeutics

Cancer is one of the major causes of global deaths and needs immediate therapeutic development. So far, several strategies have been undertaken to prevent cancer, including kinase targeting by small-molecule inhibitors. Cyclin dependent kinase 6 (CDK6) plays an essential role in cancer progression and development as its overexpression is associated with tumor development and progression. The present study demonstrated that Naringenin (NAG) binds strongly to CDK6 with a binding affinity of -7.51 kcal/mol. ATPase assay of CDK6 in the presence of NAG shows that it inhibits CDK6 with an IC₅₀ = 3.13 μM. Fluorescence and isothermal titration calorimetry studies demonstrated that NAG binds to CDK6 with the binding constant (K) values of 3.55 × 10⁶ M^-1 and 7.06 ± 2.70 × 10⁶ M^-1, respectively. The cell-based functional studies showed that NAG decreases the cell viability of human cancer cell lines, induces apoptosis, and reduces their colonization ability. Outcomes of the present in silico and in vitro studies highlighted the significance of NAG for the development of anti-cancer leads in terms of CDK6 inhibitors and provided future implications for combinatorial anti-cancer therapies.

Approaches for Targeting the Mycobactin Biosynthesis Pathway for Novel Anti-tubercular Drug Discovery: Where We Stand

INTRODUCTION

Several decades of antitubercular drug discovery efforts have focused on novel antitubercular chemotherapies. However, recent efforts have greatly shifted towards countering extremely/multi/total drug-resistant species. Targeting the conditionally essential elements inside Mycobacterium is a relatively new approach against tuberculosis and has received lackluster attention. The siderophore, Mycobactin, is a conditionally essential molecule expressed by mycobacteria in iron-stress conditions. It helps capture the micronutrient iron, essential for the smooth functioning of cellular processes.

AREAS COVERED

The authors discuss opportunities to target the conditionally essential pathways to help develop newer drugs and prolong the shelf life of existing therapeutics, emphasizing the bottlenecks in fast-tracking antitubercular drug discovery.

EXPERT OPINION

While the lack of iron supply can cripple bacterial growth and multiplication, excess iron can cause oxidative overload. Constant up-regulation can strain the bacterial synthetic machinery, further slowing its growth. Mycobactin synthesis is tightly controlled by a genetically conserved mega enzyme family via up-regulation (HupB) or down-regulation (IdeR) based on iron availability in its microenvironment. Furthermore, the recycling of siderophores by the MmpL-MmpS4/5 orchestra provides endogenous drug targets to beat the bugs with iron-toxicity contrivance. These processes can be exploited as chinks in the armor of Mycobacterium and be used for new drug development.

New Quinoline-Urea-Benzothiazole Hybrids as Promising Antitubercular Agents: Synthesis, In Vitro Antitubercular Activity, Cytotoxicity Studies, and In Silico ADME Profiling

A series of 25 new benzothiazole−urea−quinoline hybrid compounds were synthesized successfully via a three-step synthetic sequence involving an amidation coupling reaction as a critical step. The structures of the synthesized compounds were confirmed by routine spectroscopic tools (1H and 13C NMR and IR) and by mass spectrometry (HRMS). In vitro evaluation of these hybrid compounds for their antitubercular inhibitory activity against the Mycobacterium tuberculosis H37Rv pMSp12::GPF bioreporter strain was undertaken. Of the 25 tested compounds, 17 exhibited promising anti-TB activities of less than 62.5 µM (MIC90). Specifically, 13 compounds (6b, 6g, 6i−j, 6l, 6o−p, 6r−t, and 6x−y) showed promising activity with MIC90 values in the range of 1−10 µM, while compound 6u, being the most active, exhibited sub-micromolar activity (0.968 µM) in the CAS assay. In addition, minimal cytotoxicity against the HepG2 cell line (cell viability above 75%) in 11 of the 17 compounds, at their respective MIC90 concentrations, was observed, with 6u exhibiting 100% cell viability. The hybridization of the quinoline, urea, and benzothiazole scaffolds demonstrated a synergistic relationship because the activities of resultant hybrids were vastly improved compared to the individual entities. In silico ADME predictions showed that the majority of these compounds have drug-like properties and are less likely to potentially cause cardiotoxicity (QPlogHERG > −5). The results obtained in this study indicate that the majority of the synthesized compounds could serve as valuable starting points for future optimizations as new antimycobacterial agents.

Application of Next Generation Sequencing for Diagnosis and Clinical Management of Drug-Resistant Tuberculosis: Updates on Recent Developments in the Field

The World Health Organization’s End TB Strategy prioritizes universal access to an early diagnosis and comprehensive drug susceptibility testing (DST) for all individuals with tuberculosis (TB) as a key component of integrated, patient-centered TB care. Next generation whole genome sequencing (WGS) and its associated technology has demonstrated exceptional potential for reliable and comprehensive resistance prediction for Mycobacterium tuberculosis isolates, allowing for accurate clinical decisions. This review presents a descriptive analysis of research describing the potential of WGS to accelerate delivery of individualized care, recent advances in sputum-based WGS technology and the role of targeted sequencing for resistance detection. We provide an update on recent research describing the mechanisms of resistance to new and repurposed drugs and the dynamics of mixed infections and its potential implication on TB diagnosis and treatment. Whilst the studies reviewed here have greatly improved our understanding of recent advances in this arena, it highlights significant challenges that remain. The wide-spread introduction of new drugs in the absence of standardized DST has led to rapid emergence of drug resistance. This review highlights apparent gaps in our knowledge of the mechanisms contributing to resistance for these new drugs and challenges that limit the clinical utility of next generation sequencing techniques. It is recommended that a combination of genotypic and phenotypic techniques is warranted to monitor treatment response, curb emerging resistance and further dissemination of drug resistance.

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.

Genetic diversity of candidate loci linked to Mycobacterium tuberculosis resistance to bedaquiline, delamanid and pretomanid

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is one of the deadliest infectious diseases worldwide. Multidrug and extensively drug-resistant strains are making disease control difficult, and exhausting treatment options. New anti-TB drugs bedaquiline (BDQ), delamanid (DLM) and pretomanid (PTM) have been approved for the treatment of multi-drug resistant TB, but there is increasing resistance to them. Nine genetic loci strongly linked to resistance have been identified (mmpR5, atpE, and pepQ for BDQ; ddn, fgd1, fbiA, fbiB, fbiC, and fbiD for DLM/PTM). Here we investigated the genetic diversity of these loci across >33,000 M. tuberculosis isolates. In addition, epistatic mutations in mmpL5-mmpS5 as well as variants in ndh, implicated for DLM/PTM resistance in M. smegmatis, were explored. Our analysis revealed 1,227 variants across the nine genes, with the majority (78%) present in isolates collected prior to the roll-out of BDQ and DLM/PTM. We identified phylogenetically-related mutations, which are unlikely to be resistance associated, but also high-impact variants such as frameshifts (e.g. in mmpR5, ddn) with likely functional effects, as well as non-synonymous mutations predominantly in MDR-/XDR-TB strains with predicted protein destabilising effects. Overall, our work provides a comprehensive mutational catalogue for BDQ and DLM/PTM associated genes, which will assist with establishing associations with phenotypic resistance; thereby, improving the understanding of the causative mechanisms of resistance for these drugs, leading to better treatment outcomes.

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/.

Molecular Dynamics Simulations in Designing DARPins as Phosphorylation-Specific Protein Binders of ERK2

Extracellular signal-regulated kinases 1 and 2 (ERK1/2) play key roles in promoting cell survival and proliferation through the phosphorylation of various substrates. Remarkable antitumour activity is found in many inhibitors that act upstream of the ERK pathway. However, drug-resistant tumour cells invariably emerge after their use due to the reactivation of ERK1/2 signalling. ERK1/2 inhibitors have shown clinical efficacy as a therapeutic strategy for the treatment of tumours with mitogen-activated protein kinase (MAPK) upstream target mutations. These inhibitors may be used as a possible strategy to overcome acquired resistance to MAPK inhibitors. Here, we report a class of repeat proteins-designed ankyrin repeat protein (DARPin) macromolecules targeting ERK2 as inhibitors. The structural basis of ERK2-DARPin interactions based on molecular dynamics (MD) simulations was studied. The information was then used to predict stabilizing mutations employing a web-based algorithm, MAESTRO. To evaluate whether these design strategies were successfully deployed, we performed all-atom, explicit-solvent molecular dynamics (MD) simulations. Two mutations, Ala → Asp and Ser → Leu, were found to perform better than the original sequence (DARPin E40) based on the associated energy and key residues involved in protein-protein interaction. MD simulations and analysis of the data obtained on these mutations supported our predictions.

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.

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.

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.

Essential biochemical, biophysical and computational inputs on efficient functioning of Mycobacterium tuberculosis H37Rv FtsY

Mycobacterium tuberculosis (M. tuberculosis H₃₇Rv) utilizes the signal recognition particle pathway (SRP pathway) system for secretion of various proteins from ribosomes to the extracellular surface which plays an important role in the machinery running inside the bacterium. This system comprises of three major components FtsY, FfH and 4.5S rRNA. This manuscript highlights essential factors responsible for the optimized enzymatic activity of FtsY. Kinetic parameters include V_max and K_m for the hydrolysis of GTP by ftsY which were 20.25±5.16 μM/min/mg and 39.95±7.7 μM respectively. k_cat and catalytic efficiency of the reaction were 0.012±0.003 s^-1 and 0.00047±0.0001 μM/s^-1 respectively. These values were affected upon changing the standard conditions. Cations (Mg²⁺ and Mn²⁺) play important role in FtsY enzymatic activity as increasing Mg²⁺ decrease the activity. Mn²⁺on the other hand is required at higher concentration around 60 mM for carrying optimum GTPase activity. FtsY is hydrolyzing ATP and GDP as well and GDP acts as an inhibitor of the reaction. MD simulation shows effective binding and stabilization of the FtsY complexed structure with GTP, GDP and ATP. Mutational analysis was done at two important residues of GTP binding motif of FtsY, namely, GXXXXGK (K236) and DXXG (D367) and showed that these mutations significantly decrease FtsY GTPase activity. FtsY is comprised of α helices, but this structural pattern was shown to change with increasing concentrations of GTP and ATP which symbolize that these ligands cause significant conformational change by variating the secondary structure to transduce signals required by downstream effectors. This binding favors the functional stabilization of FtsY by destabilization of α-helix integrity. Revealing the hidden aspects of the functioning of FtsY might be an essential part for the understanding of the SRP pathway which is one of the important contributors of M. tuberculosis virulence.

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.

mCSM-AB2: guiding rational antibody design using graph-based signatures

MOTIVATION

A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity.

RESULTS

Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering.

AVAILABILITY AND IMPLEMENTATION

mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis

Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.

mycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria

Development of new potent, safe drugs to treat Mycobacteria has proven to be challenging, with limited hit rates of initial screens restricting subsequent development efforts. Despite significant efforts and the evolution of quantitative structure-activity relationship as well as machine learning-based models for computationally predicting molecule bioactivity, there is an unmet need for efficient and reliable methods for identifying biologically active compounds against Mycobacterium that are also safe for humans. Here we developed mycoCSM, a graph-based signature approach to rapidly identify compounds likely to be active against bacteria from the genus Mycobacterium, or against specific Mycobacteria species. mycoCSM was trained and validated on eight organism-specific and for the first time a general Mycobacteria data set, achieving correlation coefficients of up to 0.89 on cross-validation and 0.88 on independent blind tests, when predicting bioactivity in terms of minimum inhibitory concentration. In addition, we also developed a predictor to identify those compounds likely to penetrate in necrotic tuberculosis foci, which achieved a correlation coefficient of 0.75. Together with a built-in estimator of the maximum tolerated dose in humans, we believe this method will provide a valuable resource to enrich screening libraries with potent, safe molecules. To provide simple guidance in the selection of libraries with favorable anti-Mycobacteria properties, we made mycoCSM freely available online at http://biosig.unimelb.edu.au/myco_csm.

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.

Whole Genome Sequencing for the Analysis of Drug Resistant Strains of Mycobacterium tuberculosis: A Systematic Review for Bedaquiline and Delamanid

Tuberculosis (TB) remains the deadliest Infectious disease worldwide, partially due to the increasing dissemination of multidrug and extensively drug-resistant (MDR/XDR) strains. Drug regimens containing the new anti-TB drugs bedaquiline (BDQ) and delamanid (DLM) appear as a last resort for the treatment of MDR or XDR-TB. Unfortunately, resistant cases to these drugs emerged just one year after their introduction in clinical practice. Early detection of resistant strains to BDQ and DLM is crucial to preserving the effectiveness of these drugs. Here, we present a systematic review aiming to define all available genotypic variants linked to different levels of resistance to BDQ and DLM that have been described through whole genomic sequencing (WGS) and the available drug susceptibility testing methods. During the review, we performed a thorough analysis of 18 articles. BDQ resistance was associated with genetic variants in Rv0678 and atpE, while mutations in pepQ were linked to a low-level of resistance for BDQ. For DLM, mutations in the genes ddn, fgd1, fbiA, and fbiC were found in phenotypically resistant cases, while all the mutations in fbiB were reported only in DLM-susceptible strains. Additionally, WGS analysis allowed the detection of heteroresistance to both drugs. In conclusion, we present a comprehensive panel of gene mutations linked to different levels of drug resistance to BDQ and DLM.

Improvement in predicting drug sensitivity changes associated with protein mutations using a molecular dynamics based alchemical mutation method

While molecular-targeted drugs have demonstrated strong therapeutic efficacy against diverse diseases such as cancer and infection, the appearance of drug resistance associated with genetic variations in individual patients or pathogens has severely limited their clinical efficacy. Therefore, precision medicine approaches based on the personal genomic background provide promising strategies to enhance the effectiveness of molecular-targeted therapies. However, identifying drug resistance mutations in individuals by combining DNA sequencing and in vitro analyses is generally time consuming and costly. In contrast, in silico computation of protein-drug binding free energies allows for the rapid prediction of drug sensitivity changes associated with specific genetic mutations. Although conventional alchemical free energy computation methods have been used to quantify mutation-induced drug sensitivity changes in some protein targets, these methods are often adversely affected by free energy convergence. In this paper, we demonstrate significant improvements in prediction performance and free energy convergence by employing an alchemical mutation protocol, MutationFEP, which directly estimates binding free energy differences associated with protein mutations in three types of a protein and drug system. The superior performance of MutationFEP appears to be attributable to its more-moderate perturbation scheme. Therefore, this study provides a deeper level of insight into computer-assisted precision medicine.

Structure guided prediction of Pyrazinamide resistance mutations in pncA

Pyrazinamide plays an important role in tuberculosis treatment; however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: http://biosig.unimelb.edu.au/suspect_pza/.