Computing and Information Systems - 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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ItemGASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms(OXFORD UNIV PRESS, 2017-07-03)Enzyme active sites are important and conserved functional regions of proteins whose identification can be an invaluable step toward protein function prediction. Most of the existing methods for this task are based on active site similarity and present limitations including performing only exact matches on template residues, template size restraints, despite not being capable of finding inter-domain active sites. To fill this gap, we proposed GASS-WEB, a user-friendly web server that uses GASS (Genetic Active Site Search), a method based on an evolutionary algorithm to search for similar active sites in proteins. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures. The method has shown to be very effective on a range of experiments and was able to correctly identify >90% of the catalogued active sites from the Catalytic Site Atlas. It also managed to achieve a Matthew correlation coefficient of 0.63 using the Critical Assessment of protein Structure Prediction (CASP 10) dataset. In our analysis, GASS was ranking fourth among 18 methods. GASS-WEB is freely available at http://gass.unifei.edu.br/.
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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/.