Abstract:VCC234718, a molecule with growth
inhibitory activity against Mycobacterium tuberculosis (Mtb), was identified by phenotypic screening of
a 15344-compound library. Sequencing of a VCC234718-resistant mutant
identified a Y487C substitution in the inosine monophosphate dehydrogenase,
GuaB2, which was subsequently validated to be the primary molecular
target of VCC234718 in Mtb. VCC234718 inhibits Mtb GuaB2 with a Ki of 100 nM
and is uncompetitive with respect to IMP and NAD+. This
compound binds at the NAD+ site… Show more
“…During the development of a recent class of Mycobacterium tuberculosis IMPDH inhibitors, structural guided mutational prediction was used to identify likely resistance mutations, defined in this case as point mutations that disrupted inhibitor binding, but did not affect NAD binding, protein solubility or formation of the active tetramer. One mutation in particular, Y487C, was highlighted, and subsequently confirmed to be one of the few mutations to arise during resistance screening 91 . Subsequent drug development attempts avoided this resistance hotspot and were active against the Y487C mutant 92 .…”
Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.
“…During the development of a recent class of Mycobacterium tuberculosis IMPDH inhibitors, structural guided mutational prediction was used to identify likely resistance mutations, defined in this case as point mutations that disrupted inhibitor binding, but did not affect NAD binding, protein solubility or formation of the active tetramer. One mutation in particular, Y487C, was highlighted, and subsequently confirmed to be one of the few mutations to arise during resistance screening 91 . Subsequent drug development attempts avoided this resistance hotspot and were active against the Y487C mutant 92 .…”
Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.
“…While these inhibitors were shown to be active against Mtb in vitro through the inhibition of GuaB2, further work is needed to explore the potential therapeutic benefit of GuaB2 inhibition in vivo . Figure 3.Avoiding resistance hotspots.The GuaB2 inhibitor VCC234718 (represented as yellow sticks [52]) stacks on top of IMP (represented as black sticks) in a similar manner to NAD (represented as orange sticks), but makes different interactions to the neighbouring protomer unit through Y487, which is mutated leading to resistance. Optimisation of a separate scaffold (Compound 6, represented as purple sticks [53]) more closely mimicked the interactions made by NAD, and was active against the Y487C mutant.…”
For over four decades structural biology has been used to understand the mechanisms of disease, and structure-guided approaches have demonstrated clearly that they can contribute to many aspects of early drug discovery, both computationally and experimentally. Structure can also inform our understanding of impacts of mutations in human genetic diseases and drug resistance in cancers and infectious diseases. We discuss the ways that structural insights might be useful in both repurposing off-licence drugs and guide the design of new molecules that might be less susceptible to drug resistance in the future.
“…To this end, many enzymes from Mtb metabolic pathways, such as secretory tyrosine phosphatases (MptpA, MptpB) or the inosine 5 -monophosphate dehydrogenase (GuaB2), have been identified and characterized [5][6][7][8]. Many amino acid biosynthetic pathways, including those of arginine and methionine, have also been proposed as promising therapeutic targets given their essential role in bacterial growth [9][10][11].…”
Mycobacterium tuberculosis is still the deadliest bacterial pathogen worldwide and the increasing number of multidrug-resistant tuberculosis cases further complicates this global health issue. M. tuberculosis phosphoserine phosphatase SerB2 is a promising target for drug design. Besides being a key essential metabolic enzyme of the pathogen’s serine pathway, it appears to be involved in immune evasion mechanisms. In this work, a malachite green-based phosphatase assay has been used to screen 122 compounds from an internal chemolibrary. Trisubstituted harmine derivatives were found among the best hits that inhibited SerB2 activity. Synthesis of an original compound helped to discuss a brief structure activity relationship evaluation. Kinetics experiments showed that the most potent derivatives inhibit the phosphatase in a parabolic competitive fashion with apparent inhibition constants ( K i ) values in the micromolar range. Their interaction modes with the enzyme were investigated through induced fit docking experiments, leading to results consistent with the experimental data. Cellular assays showed that the selected compounds also inhibited M. tuberculosis growth in vitro. Those promising results may provide a basis for the development of new antimycobacterial agents targeting SerB2.
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