Ranganathan, Shoba - Macquarie University - Department of Chemistry and Biomolecular Sciences

研究领域

Shoba’s research interests are in the area of computational biology and bioinformatics. Specific examples of her recent research are outlined below. Alternative splicing: Alternative pre-mRNA splicing is an important mechanism for controlling gene expression in higher eukaryotes. A single gene produces several functionally diverse proteins by alternative usage of exons or introns within pre-mRNA transcripts. These gene products can be specific to tissue, developmental stage, and disease state. We have pioneered the use of graph theory for genome-wide analysis of alternative splicing in the fruitfly, chicken compared to mouse and human and more recently, the cow, contributing to the annotation of the bovine genome as well as where the cow can be used as a model for human diseases. Immunoinformatics: Major histocompatibility complexes (MHC) play a vital role for antigen presentation and recognition by binding immunogenic peptide epitopes (p) and forming peptide-MHC (pMHC) complexes, for recognition by T cell receptors (TR), culminating in T cell activation. For efficient vaccine design and to minimize experimental T cell binding assays, advanced computational strategies for predicting strong MHC-binding epitopes with high propensity to activate T cells are required. By developing a fast accurate method for pMHC binding and analysing the physicochemical basis of TR binding to pMHC, we can accurately predicted T cell epitopes amongst high-binders for disease-implicated human MHC alleles. Protein interaction networks: Biological processes are governed by generic rules embedded in the complex connectivity or networks. Systematic studies over the past few years have unveiled how these rules maintain cellular complexity by coordinating a large number of biological processes and their molecular components. Integrating data from protein-protein and metabolite-linked protein interaction networks, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. Our results indicate that the metabolic network adds value to the information in the protein interaction network for the localisation process of proteins in human subcellular compartments. Transcriptomics: Parasitic nematodes infect humans, other animals and plants, and impose a significant public health and economic burden worldwide due to the diseases that they cause. A better understanding of parasite genomes, host-parasite relationships and the molecular biology of parasites themselves will enable the rational development of diagnostic tests and/or safe anti-parasitic compounds, following the functional annotation of parasite genomic sequences. With only a few completely sequenced nematode genomes, expressed sequence tag (EST) data-sets provide a low-cost alternative (“poor man’s genome”) to whole genome sequences and a glimpse of the transcriptome of an organism. EST data require a number of computational methods for their pre-processing, clustering, assembly and annotation to yield biologically relevant information. We have developed semi-automated bioinformatic pipelines, ESTExplorer and EST2Secretome to identify molecules involved in key biological processes or pathways that might serve as targets for new drugs or vaccines. Biodiversity and Chem Informatics: Biodiversity informatics is emerging as a discipline focusing on the collection, digitisation, collation, dissemination and analysis of species-level data. We have applied biodiversity informatics tools and resources to conserve and protect customary Aboriginal medicinal plants (CMP) and their associated indigenous knowledge. In collaboration with Aboriginal communities, a web-based multidisciplinary customary medicinal knowledgebase (CMKb) was developed as a unique Australian resource. GIS and ecological niche modeling techniques were used to identify CMP species-rich areas and evaluate the cultural worth of the habitats. Further, to understand the likely impacts of climate change on the areas of highest cultural worth, predictive models were developed for current and future climate scenarios, based on spatial distributions of select CMP species. Bioactive compounds from natural sources are lead compounds for many therapeutic agents. Starting from the bioactive compounds in CMKb, we have carried out cheminformatics analysis of large public datasets as well as used machine learning methdos to predict novel lead molecules.

近期论文

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Tong, JC, Tan TW, Ranganathan S (2014) Structure-based clustering of MHC proteins for broad-based T-cell vaccine design. In: De RK, Tomar N (Eds.) Immunoinformatics, Springer, Methods in Molecular Biology, in press. Khanna V, Ranganathan S (2014) Chemogenomics approach to computer-aided drug discovery. In: Sakharkar K, Sakharkar MK, Chandra R (Eds.) Genomics and Drug Discovery, River Publishers, in press. Tang YT, Gao X, Rosa BA, Abubucker S, Hallsworth-Pepin K, Martin J, Tyagi R, Heizer E, Zhang X, Bhonagiri-Palsikar V, Minx P, Warren WC, Zhan B, Hotez PJ, Sternberg PW, Dougall A, Gaze ST, Bethony J, Mulvenna J, Ranganathan S, Rabelo EM, Wilson RW, Felgner PL, Hawdon JM, Gasser RB, Loukas A, Mitreva M (2014) Genome of the human hookworm Necator americanus. Nature Genetics, 46, 261-269. Sowmya G, Khan JM, Anand S, Ahn SB, Baker MS, Ranganathan S (2014) A site for direct integrin αvβ6•uPAR interaction from structural modelling and docking. J Struct Biol, 185, 327-335. Krajaejun T, Lerksuthirat T, Garg G, Lowhnoo T, Yingyong W, Khositnithikul R, Tangphatsornruang S, Suriyaphol P, Ranganathan S, Sullivan TD (2014) Transcriptome Analysis Reveals Pathogenicity and Evolutionary History of the Pathogenic Oomycete Pythium insidiosum. Fungal Biol, accepted 27 Jan. 2014 Islam MT, Garg G, Hancock WS, Risk BA, Baker MS, Ranganathan S (2014) Protannotator: A Semiautomated Pipeline for Chromosome-Wise Functional Annotation of the "Missing" Human Proteome. J Proteome Res. 13, 76-83. Sowmya G, Ranganathan S (2014) Protein-protein interactions and prediction: a comprehensive overview. Peptide & Protein Letters, 21, 779-789 (invited)