Synthesis and Structure–Activity relationship of 1-(5-isoquinolinesulfonyl)piperazine analogues as inhibitors of Mycobacterium tuberculosis IMPDH

Singh, Vinayak; Pacitto, A.; Donini, Stefano; Ferraris, Davide M.; Boros, Sándor; Illyés, Eszter; Szokol, Bálint; Rizzi, Menico; Blundell, Tom L.; Ascher, David B.; Pató, János; Mizrahi, Valerie

doi:10.1016/j.ejmech.2019.04.027

Cited by 27 publications

(19 citation statements)

References 41 publications

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“…The probability indexes were statistically derived from a dataset of 17,170 single amino acid variations in 5,305 proteins retrieved from data available at UniProtKB (release 2010_04), dbSNP (build 132), OMIM and ProTherm [15] . SDM2, mCSM and DUET have been used to evaluate the effects of stability changes in different applications: human cancer-related genes in the COSMIC database [77] , inhibition of inosine-5′-monophosphate dehydrogenase, isoniazid and rifampicin resistance in Mycobacterium Tuberculosis [78] , phosphodiesterase somatic mutations implicated in cancer and retinitis pigmentosa [79] , protein presenilin 2 linked to familial Alzheimer’s disease [80] .…”

Section: Challenges In the Applications Of Protein Stability Predictimentioning

confidence: 99%

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Sanavia

Birolo

Montanucci

et al. 2020

Computational and Structural Biotechnology Journal

110

109

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Section: Challenges In the Applications Of Protein Stability Predictimentioning

confidence: 99%

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Sanavia

Birolo

Montanucci

et al. 2020

Computational and Structural Biotechnology Journal

110

109

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“…These single amino acid changes can readily disrupt the intricate network of intramolecular interactions, affecting how a protein folds, its stability, dynamics, and ultimately protein function. Beyond phenotypic outcomes, 1–22 it also has direct implications for their experimental study, protein engineering, 23,24 drug design, 25–30 and use in industrial processes 31 …”

Section: Introductionmentioning

confidence: 99%

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

2020

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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 (ΔTm). 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.

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“…While no general pre-emptive predictor for AMR has been developed, we and others have shown that computational tools for understanding the underlying molecular mechanisms of mutations can be used to identify likely resistant variants [79] , [80] , [81] , [82] , [86] , [87] , [88] , [89] , [90] , [91] , [92] , [93] , [94] , [95] . This insight has even been used to guide medicinal chemistry design of inhibitors less prone to resistance [96] , [97] , [98] , [99] .…”

Section: Computational Tools Measuring the Effect Of Mutationsmentioning

confidence: 99%

“…Anticipating these variants before they arise in a population can inform the drug discovery pipeline, especially in developing compounds less prone to resistance emergence. Such an approach has already been used as part of the drug development efforts against the TB drug target IMPDH [99] . The mutation predicted was the only resistant variant detected in subsequent in vitro resistant assays.…”

Section: Designing Better Antibacterial Drugsmentioning

confidence: 99%

Combining structure and genomics to understand antimicrobial resistance

Tunstall

Portelli

Phelan

et al. 2020

Computational and Structural Biotechnology Journal

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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.

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Synthesis and Structure–Activity relationship of 1-(5-isoquinolinesulfonyl)piperazine analogues as inhibitors of Mycobacterium tuberculosis IMPDH

Cited by 27 publications

References 41 publications

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

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

Combining structure and genomics to understand antimicrobial resistance

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