Biochemistry and Pharmacology - Research Publications
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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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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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ItemdendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction(Nature Publishing Group, 2019-10-29)Nanomedicine development currently suffers from a lack of efficient tools to predict pharmacokinetic behavior without relying upon testing in large numbers of animals, impacting success rates and development costs. This work presents dendPoint, the first in silico model to predict the intravenous pharmacokinetics of dendrimers, a commonly explored drug vector, based on physicochemical properties. We have manually curated the largest relational database of dendrimer pharmacokinetic parameters and their structural/physicochemical properties. This was used to develop a machine learning-based model capable of accurately predicting pharmacokinetic parameters, including half-life, clearance, volume of distribution and dose recovered in the liver and urine. dendPoint successfully predicts dendrimer pharmacokinetic properties, achieving correlations of up to r = 0.83 and Q2 up to 0.68. dendPoint is freely available as a user-friendly web-service and database at http://biosig.unimelb.edu.au/dendpoint. This platform is ultimately expected to be used to guide dendrimer construct design and refinement prior to embarking on more time consuming and expensive in vivo testing.
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ItemStructural and Biochemical Insights into the Function and Evolution of Sulfoquinovosidases(AMER CHEMICAL SOC, 2018-09-26)An estimated 10 billion tonnes of sulfoquinovose (SQ) are produced and degraded each year. Prokaryotic sulfoglycolytic pathways catabolize sulfoquinovose (SQ) liberated from plant sulfolipid, or its delipidated form α-d-sulfoquinovosyl glycerol (SQGro), through the action of a sulfoquinovosidase (SQase), but little is known about the capacity of SQ glycosides to support growth. Structural studies of the first reported SQase (Escherichia coli YihQ) have identified three conserved residues that are essential for substrate recognition, but crossover mutations exploring active-site residues of predicted SQases from other organisms have yielded inactive mutants casting doubt on bioinformatic functional assignment. Here, we show that SQGro can support the growth of E. coli on par with d-glucose, and that the E. coli SQase prefers the naturally occurring diastereomer of SQGro. A predicted, but divergent, SQase from Agrobacterium tumefaciens proved to have highly specific activity toward SQ glycosides, and structural, mutagenic, and bioinformatic analyses revealed the molecular coevolution of catalytically important amino acid pairs directly involved in substrate recognition, as well as structurally important pairs distal to the active site. Understanding the defining features of SQases empowers bioinformatic approaches for mapping sulfur metabolism in diverse microbial communities and sheds light on this poorly understood arm of the biosulfur cycle.
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ItemmCSM-PPI2: predicting the effects of mutations on protein-protein interactions(OXFORD UNIV PRESS, 2019-07-02)Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein-protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.
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ItemTumour risks and genotype-phenotype correlations associated with germline variants in succinate dehydrogenase subunit genes SDHB, SDHC and SDHD(BMJ PUBLISHING GROUP, 2018-06)BACKGROUND: Germline pathogenic variants in SDHB/SDHC/SDHD are the most frequent causes of inherited phaeochromocytomas/paragangliomas. Insufficient information regarding penetrance and phenotypic variability hinders optimum management of mutation carriers. We estimate penetrance for symptomatic tumours and elucidate genotype-phenotype correlations in a large cohort of SDHB/SDHC/SDHD mutation carriers. METHODS: A retrospective survey of 1832 individuals referred for genetic testing due to a personal or family history of phaeochromocytoma/paraganglioma. 876 patients (401 previously reported) had a germline mutation in SDHB/SDHC/SDHD (n=673/43/160). Tumour risks were correlated with in silico structural prediction analyses. RESULTS: Tumour risks analysis provided novel penetrance estimates and genotype-phenotype correlations. In addition to tumour type susceptibility differences for individual genes, we confirmed that the SDHD:p.Pro81Leu mutation has a distinct phenotype and identified increased age-related tumour risks with highly destabilising SDHB missense mutations. By Kaplan-Meier analysis, the penetrance (cumulative risk of clinically apparent tumours) in SDHB and (paternally inherited) SDHD mutation-positive non-probands (n=371/67 with detailed clinical information) by age 60 years was 21.8% (95% CI 15.2% to 27.9%) and 43.2% (95% CI 25.4% to 56.7%), respectively. Risk of malignant disease at age 60 years in non-proband SDHB mutation carriers was 4.2%(95% CI 1.1% to 7.2%). With retrospective cohort analysis to adjust for ascertainment, cumulative tumour risks for SDHB mutation carriers at ages 60 years and 80 years were 23.9% (95% CI 20.9% to 27.4%) and 30.6% (95% CI 26.8% to 34.7%). CONCLUSIONS: Overall risks of clinically apparent tumours for SDHB mutation carriers are substantially lower than initially estimated and will improve counselling of affected families. Specific genotype-tumour risk associations provides a basis for novel investigative strategies into succinate dehydrogenase-related mechanisms of tumourigenesis and the development of personalised management for SDHB/SDHC/SDHD mutation carriers.
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ItemDynaMut: predicting the impact of mutations on protein conformation, flexibility and stability(OXFORD UNIV PRESS, 2018-07-02)Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.
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ItemKinact: a computational approach for predicting activating missense mutations in protein kinases(OXFORD UNIV PRESS, 2018-07-02)Protein phosphorylation is tightly regulated due to its vital role in many cellular processes. While gain of function mutations leading to constitutive activation of protein kinases are known to be driver events of many cancers, the identification of these mutations has proven challenging. Here we present Kinact, a novel machine learning approach for predicting kinase activating missense mutations using information from sequence and structure. By adapting our graph-based signatures, Kinact represents both structural and sequence information, which are used as evidence to train predictive models. We show the combination of structural and sequence features significantly improved the overall accuracy compared to considering either primary or tertiary structure alone, highlighting their complementarity. Kinact achieved a precision of 87% and 94% and Area Under ROC Curve of 0.89 and 0.92 on 10-fold cross-validation, and on blind tests, respectively, outperforming well established tools (P < 0.01). We further show that Kinact performs equally well on homology models built using templates with sequence identity as low as 33%. Kinact is freely available as a user-friendly web server at http://biosig.unimelb.edu.au/kinact/.