Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

Mugumbate, Grace; Nyathi, Brilliant; Zindoga, Albert; Munyuki, Gadzikano

doi:10.3389/fmolb.2021.643849

Cited by 10 publications

(4 citation statements)

References 68 publications

(94 reference statements)

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“…Refinement of computational protein structure prediction using ML+MD has also been reported [heo2020]. With respect to variants that could impact protein function, several studies have used MD and ML to predict drug-resistant variants in Mycobacterium tuberculosis that is involved in tuberculosis [jama2020; mugu2021], variants that promote CTX-M9 mediated antibiotic resistance [lata2017], variants in epistatic enzyme that impact its stability and aggregation [li2021], and impacts of variants on protein binding dynamics [babb2020] or inducing the phenotypic alterations [garg2019].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Immadisetty¹,

Kekenes‐Huskey²

2021

Preprint

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1AbstractLong QT syndrome (LQTS) is a cardiac condition characterized by a prolonged QT-interval. This prolongation can contribute to fatal arrhythmias despite otherwise normal metrics of cardiac function. Hence, genetic screening of individuals remains a standard for initial identification of individuals susceptible to LQTS. Several genes, including KCNH2 that encodes for the Kv11.1 channel are known to cause LQTS2, however, only a small subset of variants found in the human population are established as pathogenic. Hence, the majority of its missense mutations are known to be benign or are variant of unknown significance (VUS). Here we use molecular dynamics (MD) simulations and machine learning (ML) to determine the propensity for Kv11.1 channel variants to present loss-of-function behavior. Specifically, we use these computational techniques to correlate structural and dynamic changes in an important Kv11.1 subdomain, the PAS-domain (PASD), with the channel’s ability to traffic normally to the plasma membrane. With these techniques, we have identified several molecular features, namely, number of hydrating waters and intra-PASD hydrogen bonds, as moderately predictive of trafficking. Together with bioinformatics data including sequence conservation and folding energies, we are able to predict with reasonable accuracy (≈75%) the ability of VUS’ to traffic. Additionally, we compared two ML algorithms i.e., Decision tree (DT)s and Random forest (RF) for their robustness, and we report that RF performed moderately better than DTs’. Features derived from MD trajectories particularly help improve the prediction of trafficking deficient variants by both ML techniques.

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Section: Discussionmentioning

confidence: 99%

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Immadisetty¹,

Kekenes‐Huskey²

2021

Preprint

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“…Genes involved in the INH pathway such as iniABC were shown to belong to its regulon; (ii) Rv3855 (EthR) is wellknown for its regulatory role on ethA (rv3854c), which encodes a Baeyer-Villager monooxygenase involved in the activation of ETO (Engohang-Ndong et al, 2004). Despite the role of mutations affecting ethA in ETO resistance is evident (Ushtanit et al, 2022), mutations in its transcriptional regulator seems relatively rare in clinical isolates (da Silva et al, 2018;Mugumbate et al, 2021); (iii) Rv0273c (EtbR) is a transcriptional repressor of the inhA gene, which encodes the target of INH. EMB can bind EtbR, increasing its repressing activity on inhA transcription, thus increasing susceptibility to INH (Zhu et al, 2018).…”

Section: Tetr Family Transcriptional Regulatorsmentioning

confidence: 99%

Transcriptional regulation and drug resistance in Mycobacterium tuberculosis

Miotto

Sorrentino

Giorgi

et al. 2022

Front. Cell. Infect. Microbiol.

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Bacterial drug resistance is one of the major challenges to present and future human health, as the continuous selection of multidrug resistant bacteria poses at serious risk the possibility to treat infectious diseases in the near future. One of the infection at higher risk to become incurable is tuberculosis, due to the few drugs available in the market against Mycobacterium tuberculosis. Drug resistance in this species is usually due to point mutations in the drug target or in proteins required to activate prodrugs. However, another interesting and underexplored aspect of bacterial physiology with important impact on drug susceptibility is represented by the changes in transcriptional regulation following drug exposure. The main regulators involved in this phenomenon in M. tuberculosis are the sigma factors, and regulators belonging to the WhiB, GntR, XRE, Mar and TetR families. Better understanding the impact of these regulators in survival to drug treatment might contribute to identify new drug targets and/or to design new strategies of intervention.

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“…22 The mutation C-15T confer a lowlevel resistance to INH and a cross-resistance to ETH. 67,68 The mutations Ala95Thr and Phe110Leu in the ethR gene confer resistance to ETH. 69 While the mutations Ile95Pro, Ser94Ala, and Ile21Thr in the inhA gene confer resistance to both INH and ETH in MTB resistant clinical isolates.…”

Section: Drug Target Genes and Mutations Conferring Resistance To Eth...mentioning

confidence: 99%

Drug-target genes and their spontaneous mutations associated with resistance to first-line, second-line, third-line, novel and repurposed anti-tuberculosis drugs in Mycobacterium tuberculosis resistant strains

Mumena

Kwenda²,

Ngugi³

et al. 2022

IJMMTD

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Drug-resistant tuberculosis is a threat to the control of tuberculosis globally, it develops mainly due to mutations in target genes of (MTB). Mutations in the rpoB gene confer resistance to rifampicin (RIF). The most frequent mutations conferring resistance to RIF include; Ser531Leu, Asp516Val, and His526Asp. Isoniazid resistance (INHr) occur most frequently due to mutations in the and its promoter. Most frequent mutation in is Ser315Thr 1, while in inhA include; Thr8Cys, Ala16Gly, and Cys15Thr. Mutations in , and genes confer resistance to ethambutol. 70% of mutations in the gene occur in codon 306, 406, or 497 and include; Met306Leu, Gly43Cys, and Ser412Pro. Mutations in the , and genes mediate resistance to pyrazinamide. Frequent mutations in A include; Tyr64Ser, Phe94Ala, and Trp68Gly. MTB resistance to streptomycin (STR) occur due to mutations in the , B, and L genes. Mutations (Ala80Pro), and L (Lys43Arg) confer resistance to STR. Fluoroquinolone resistance is mediated via mutations in the A and B genes. The most common mutations in the A gene include; Gly88Cys, Ala90Val, and Ser91Pro. While those in the gyrB gene include; Glu540Val, and Asn538Asp. Mutations in the rrs and promoter region cause resistance to the kanamycin and amikacin. While mutations in the and A cause resistance to capreomycin and viomycin. Common mutations in include; Cys1402Thr, Ala1401Gly, and Gly1484Thr. While mutations in the include; Cys12Thr, Gly10Ala, and Gly37Thr.Detection of drug-target genes and their mutations has therapeutic and diagnostic value.

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Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

Cited by 10 publications

References 68 publications

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Transcriptional regulation and drug resistance in Mycobacterium tuberculosis

Drug-target genes and their spontaneous mutations associated with resistance to first-line, second-line, third-line, novel and repurposed anti-tuberculosis drugs in Mycobacterium tuberculosis resistant strains

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