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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ItemmCSM: predicting the effects of mutations in proteins using graph-based signatures(OXFORD UNIV PRESS, 2014-02-01)MOTIVATION: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein-protein and protein-nucleic acid interactions. RESULTS: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario. AVAILABILITY AND IMPLEMENTATION: A web server is available at http://structure.bioc.cam.ac.uk/mcsm.
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ItemDUET: a server for predicting effects of mutations on protein stability using an integrated computational approach(OXFORD UNIV PRESS, 2014-07-01)Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.
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ItemPlatinum: a database of experimentally measured effects of mutations on structurally defined protein-ligand complexes(OXFORD UNIV PRESS, 2015-01-28)Drug resistance is a major challenge for the treatment of many diseases and a significant concern throughout the drug development process. The ability to understand and predict the effects of mutations on protein-ligand affinities and their roles in the emergence of resistance would significantly aid treatment and drug design strategies. In order to study and understand the impacts of missense mutations on the interaction of ligands with the proteome, we have developed Platinum (http://structure.bioc.cam.ac.uk/platinum). This manually curated, literature-derived database, comprising over 1000 mutations, associates for the first time experimental information on changes in affinity with three-dimensional structures of protein-ligand complexes. To minimize differences arising from experimental techniques and to directly compare binding affinities, Platinum considers only changes measured by the same group and with the same amino-acid sequence used for structure determination, providing a direct link between protein structure, how a ligand binds and how mutations alter the affinity of the ligand of the protein. We believe Platinum will be an invaluable resource for understanding the effects of mutations that give rise to drug resistance, a major problem emerging in pandemics including those caused by the influenza virus, in infectious diseases such as tuberculosis, in cancer and in many other life-threatening illnesses.
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ItempkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures(AMER CHEMICAL SOC, 2015-05-14)Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.