Biochemistry and Pharmacology - Research Publications
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ItemNo Preview AvailableWidespread remodeling of proteome solubility in response to different protein homeostasis stresses(National Academy of Sciences, 2020-02-04)The accumulation of protein deposits in neurodegenerative diseases has been hypothesized to depend on a metastable subproteome vulnerable to aggregation. To investigate this phenomenon and the mechanisms that regulate it, we measured the solubility of the proteome in the mouse Neuro2a cell line under six different protein homeostasis stresses: 1) Huntington’s disease proteotoxicity, 2) Hsp70, 3) Hsp90, 4) proteasome, 5) endoplasmic reticulum (ER)-mediated folding inhibition, and 6) oxidative stress. Overall, we found that about one-fifth of the proteome changed solubility with almost all of the increases in insolubility were counteracted by increases in solubility of other proteins. Each stress directed a highly specific pattern of change, which reflected the remodeling of protein complexes involved in adaptation to perturbation, most notably, stress granule (SG) proteins, which responded differently to different stresses. These results indicate that the protein homeostasis system is organized in a modular manner and aggregation patterns were not correlated with protein folding stability (ΔG). Instead, distinct cellular mechanisms regulate assembly patterns of multiple classes of protein complexes under different stress conditions.
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ItemGlutathione transferase P1-1 as an arsenic drug-sequestering enzyme(WILEY, 2017-02)Arsenic-based compounds are paradoxically both poisons and drugs. Glutathione transferase (GSTP1-1) is a major factor in resistance to such drugs. Here we describe using crystallography, X-ray absorption spectroscopy, mutagenesis, mass spectrometry, and kinetic studies how GSTP1-1 recognizes the drug phenylarsine oxide (PAO). In conditions of cellular stress where glutathione (GSH) levels are low, PAO crosslinks C47 to C101 of the opposing monomer, a distance of 19.9 Å, and causes a dramatic widening of the dimer interface by approximately 10 Å. The GSH conjugate of PAO, which forms rapidly in cancerous cells, is a potent inhibitor (Ki = 90 nM) and binds as a di-GSH complex in the active site forming part of a continuous network of interactions from one active site to the other. In summary, GSTP1-1 can detoxify arsenic-based drugs by sequestration at the active site and at the dimer interface, in situations where there is a plentiful supply of GSH, and at the reactive cysteines in conditions of low GSH.
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ItemA Family of Dual-Activity Glycosyltransferase-Phosphorylases Mediates Mannogen Turnover and Virulence in Leishmania Parasites(CELL PRESS, 2019-09-11)Parasitic protists belonging to the genus Leishmania synthesize the non-canonical carbohydrate reserve, mannogen, which is composed of β-1,2-mannan oligosaccharides. Here, we identify a class of dual-activity mannosyltransferase/phosphorylases (MTPs) that catalyze both the sugar nucleotide-dependent biosynthesis and phosphorolytic turnover of mannogen. Structural and phylogenic analysis shows that while the MTPs are structurally related to bacterial mannan phosphorylases, they constitute a distinct family of glycosyltransferases (GT108) that have likely been acquired by horizontal gene transfer from gram-positive bacteria. The seven MTPs catalyze the constitutive synthesis and turnover of mannogen. This metabolic rheostat protects obligate intracellular parasite stages from nutrient excess, and is essential for thermotolerance and parasite infectivity in the mammalian host. Our results suggest that the acquisition and expansion of the MTP family in Leishmania increased the metabolic flexibility of these protists and contributed to their capacity to colonize new host niches.
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ItemOptimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation(COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT, 2017-10)Gene panel and exome sequencing have revealed a high rate of molecular diagnoses among diseases where the genetic architecture has proven suitable for sequencing approaches, with a large number of distinct and highly penetrant causal variants identified among a growing list of disease genes. The challenge is, given the DNA sequence of a new patient, to distinguish disease-causing from benign variants. Large samples of human standing variation data highlight regional variation in the tolerance to missense variation within the protein-coding sequence of genes. This information is not well captured by existing bioinformatic tools, but is effective in improving variant interpretation. To address this limitation in existing tools, we introduce the missense tolerance ratio (MTR), which summarizes available human standing variation data within genes to encapsulate population level genetic variation. We find that patient-ascertained pathogenic variants preferentially cluster in low MTR regions (P < 0.005) of well-informed genes. By evaluating 20 publicly available predictive tools across genes linked to epilepsy, we also highlight the importance of understanding the empirical null distribution of existing prediction tools, as these vary across genes. Subsequently integrating the MTR with the empirically selected bioinformatic tools in a gene-specific approach demonstrates a clear improvement in the ability to predict pathogenic missense variants from background missense variation in disease genes. Among an independent test sample of case and control missense variants, case variants (0.83 median score) consistently achieve higher pathogenicity prediction probabilities than control variants (0.02 median score; Mann-Whitney U test, P < 1 × 10-16). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy.
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ItemLarge expert-curated database for benchmarking document similarity detection in biomedical literature search(OXFORD UNIV PRESS, 2019-10-29)Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
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ItemHARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae(ELSEVIER, 2020)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.
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ItemCombining structure and genomics to understand antimicrobial resistance(ELSEVIER, 2020)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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ItemVariation in Human Cytochrome P-450 Drug-Metabolism Genes: A Gateway to the Understanding of Plasmodium vivax Relapses (vol 11, e0160172, 2016)(PUBLIC LIBRARY SCIENCE, 2018-02-01)[This corrects the article DOI: 10.1371/journal.pone.0160172.].
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ItemPrediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.(Nature Publishing Group, 2020-10-22)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/ .
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ItemNedd8 hydrolysis by UCH proteases in Plasmodium parasites(PUBLIC LIBRARY SCIENCE, 2019-10)Plasmodium parasites are the causative agents of malaria, a disease with wide public health repercussions. Increasing drug resistance and the absence of a vaccine make finding new chemotherapeutic strategies imperative. Components of the ubiquitin and ubiquitin-like pathways have garnered increased attention as novel targets given their necessity to parasite survival. Understanding how these pathways are regulated in Plasmodium and identifying differences to the host is paramount to selectively interfering with parasites. Here, we focus on Nedd8 modification in Plasmodium falciparum, given its central role to cell division and DNA repair, processes critical to Plasmodium parasites given their unusual cell cycle and requirement for refined repair mechanisms. By applying a functional chemical approach, we show that deNeddylation is controlled by a different set of enzymes in the parasite versus the human host. We elucidate the molecular determinants of the unusual dual ubiquitin/Nedd8 recognition by the essential PfUCH37 enzyme and, through parasite transgenics and drug assays, determine that only its ubiquitin activity is critical to parasite survival. Our experiments reveal interesting evolutionary differences in how neddylation is controlled in higher versus lower eukaryotes, and highlight the Nedd8 pathway as worthy of further exploration for therapeutic targeting in antimalarial drug design.