Journal article

Annotation of the Giardia proteome through structure-based homology and machine learning

Brendan RE Ansell, Bernard J Pope, Peter Georgeson, Samantha J Emery-Corbin, Aaron R Jex



Background: Large-scale computational prediction of protein structures represents a cost-effective alternative to empirical structure determination with particular promise for non-model organisms and neglected pathogens. Conventional sequence-based tools are insufficient to annotate the genomes of such divergent biological systems. Conversely, protein structure tolerates substantial variation in primary amino acid sequence and is thus a robust indicator of biochemical function. Structural proteomics is poised to become a standard part of pathogen genomics research; however, informatic methods are now required to assign confidence in large volumes of predicted structures. Aims: Our aim was to..

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Awarded by Jack Brockhoff Foundation

Awarded by Australian Research Council Linkage grant

Funding Acknowledgements

B.R.E.A. was partly supported by an Australian Post-Graduate Award (Australian Government) and the Victorian Life Sciences Computation Initiative (Victoria, Australia). S.J.E. was supported by a Jack Brockhoff Foundation Early Career grant (ID JBF 4184, 2016). A.R.J. was partially supported by an Australian Research Council Linkage grant (LP120200122). B.R.E.A., S.J.E., and A.R.J. were supported by the Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council Independent Research Institute Infrastructure Support Scheme. B.J.P. was supported by a Victorian Health and Medical Research Fellowship.