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Dr Mark Wass

Senior Lecturer in Computational Biology

School of Biosciences

 

Mark joined the School of Biosciences in October 2012. He obtained his first degree in Natural Sciences at Cambridge University in 2000 followed by a Masters in Computing at Imperial College London. After a few years working in Industry as an IT consultant Mark studied for a PhD with Prof Mike Sternberg at Imperial (2004-2008) and continued onto a post-doctoral position in the group until 2011. In 2011 Mark was awarded a FEBS Long Term Fellowship to work in the group of Alfonso Valencia at the CNIO (Spanish National Cancer Research Centre, Madrid, Spain).

Mark's research interests are in Structural Bioinformatics particularly the analysis and prediction of protein function, structure and interactions. He is also interested in using such approaches to analyse genetic variation and identify the functional effects that are associated with disease.

Mark is a member of the Cytogenomics and Bioinformatics Group.

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Also view these in the Kent Academic Repository

Article
Williams, L. et al. (2016). Mitochondrial diversity within ciliates. Mitochondrial diversity within ciliates.
Wass, M., Rossman, J. and Michaelis, M. (2016). Ebola outbreak highlights the need for wet and dry laboratory collaboration. Journal of Virology and Emerging Diseases [Online] 2. Available at: http://dx.doi.org/10.16966/2473-1846.e102.
Michaelis, M. et al. (2016). Substrate-specific effects of pirinixic acid derivatives on ABCB1-mediated drug transport. Oncotarget [Online] 7:11664-76. Available at: http://dx.doi.org/10.18632/oncotarget.7345.
Pappalardo, M. et al. (2016). Conserved differences in protein sequence determine the human pathogenicity of Ebolaviruses. Scientific reports [Online] 6:23743. Available at: http://dx.doi.org/10.1038/srep23743.
Wong, K., Wass, M. and Thomas, K. (2016). The Role of Protein Modelling in Predicting the Disease Severity of Cystinuria. European Urology [Online] 69:543-544. Available at: http://dx.doi.org/10.1016/j.eururo.2015.10.039.
Shagari, H. et al. (2016). The 2014 Ebola Outbreak: Preparedness in West African Countries and its Impact on the Size of the Outbreak. Journal of Emerging Diseases and Virology [Online] 2. Available at: http://dx.doi.org/10.16966/2473-1846.123.
Lobo, S. et al. (2016). Desulfovibrio vulgarisCbiKPcobaltochelatase: evolution of a haem binding protein orchestrated by the incorporation of two histidine residues. Environmental Microbiology [Online]. Available at: http://doi.org/10.1111/1462-2920.13479.
Michaelis, M. et al. (2015). Identification of flubendazole as potential anti-neuroblastoma compound in a large cell line screen. Scientific reports [Online] 5:8202. Available at: http://www.nature.com/srep/2015/150203/srep08202/full/srep08202.html.
Kelley, L. et al. (2015). The Phyre2 web portal for protein modeling, prediction and analysis. Nature protocols [Online] 10:845-58. Available at: http://dx.doi.org/10.1038/nprot.2015.053.
Wass, M. et al. (2014). The automated function prediction SIG looks back at 2013 and prepares for 2014. Bioinformatics [Online] 30:2091-2092. Available at: http://dx.doi.org/10.1093/bioinformatics/btu117.
Pappalardo, M. and Wass, M. (2014). VarMod: modelling the functional effects of non-synonymous variants. Nucleic Acids Research [Online] 42:W331-W336. Available at: http://dx.doi.org/10.1093/nar/gku483.
Radivojac, P. et al. (2013). A large-scale evaluation of computational protein function prediction. Nature Methods [Online] 10:221-227. Available at: http://dx.doi.org/10.1038/nmeth.2340.
Wass, M., Barton, G. and Sternberg, M. (2012). CombFunc: predicting protein function using heterogeneous data sources. Nucleic Acids Research [Online] 40:W466-W470. Available at: http://dx.doi.org/10.1093/nar/gks489.
David, A. et al. (2012). Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs. Human Mutation [Online] 33:359-363. Available at: http://dx.doi.org/10.1002/humu.21656.
Wass, M. et al. (2012). Proteomic analysis of Plasmodium in the mosquito: progress and pitfalls. Parasitology [Online] 139:1131-1145. Available at: http://dx.doi.org/10.1017/S0031182012000133.
Chambers, J. et al. (2011). Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nature Genetics [Online] 43:1131-1138. Available at: http://dx.doi.org/10.1038/ng.970.
Wass, M. et al. (2011). Towards the prediction of protein interaction partners using physical docking. Molecular Systems Biology [Online] 7. Available at: http://dx.doi.org/10.1038/msb.2011.3.
Wass, M., David, A. and Sternberg, M. (2011). Challenges for the prediction of macromolecular interactions. Current Opinion in Structural Biology [Online] 21:382-90. Available at: http://dx.doi.org/10.1016/j.sbi.2011.03.013.
Chambers, J. et al. (2010). Genetic loci influencing kidney function and chronic kidney disease. Nature Genetics 42:373-5.
Chambers, J. et al. (2010). Genetic variation in SCN10A influences cardiac conduction. Nature Genetics [Online] 42:149-152. Available at: http://dx.doi.org/10.1038/ng.516.
Sinden, R. et al. (2010). The flagellum in malarial parasites. Current Opinion in Microbiology [Online] 13:491-500. Available at: http://dx.doi.org/10.1016/j.mib.2010.05.016.
Wass, M., Kelley, L. and Sternberg, M. (2010). 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Research [Online] 38:W469-W473. Available at: http://dx.doi.org/10.1093/nar/gkq406.
Wass, M. and Sternberg, M. (2009). Prediction of ligand binding sites using homologous structures and conservation at CASP8. Proteins:Structure, Function, and Genetics [Online] 77 Sup:147-151. Available at: http://dx.doi.org/10.1002/prot.22513.
Chambers, J. et al. (2009). Genome-wide association study identifies variants in TMPRSS6 associated with hemoglobin levels. Nature Genetics [Online] 41:1170-1172. Available at: http://dx.doi.org/10.1038/ng.462.
Wass, M. and Sternberg, M. (2008). ConFunc--functional annotation in the twilight zone. Bioinformatics [Online] 24:798-806. Available at: http://dx.doi.org/10.1093/bioinformatics/btn037.
Showing 25 of 26 total publications in KAR. [See all in KAR]

 

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My research is primarily based in protein bioinformatics. I have developed methods for the prediction of protein function (ConFunc and CombFunc – webserver at http://www.sbg.bio.ic.ac.uk/combfunc ) and for the prediction of small molecule binding sites in proteins (3DLigandSite - http://www.sbg.bio.ic.ac.uk/3dligandsite). Recent research has demonstrated the ability to use protein-protein docking tools to predict interactions between proteins. I am increasingly interested in using structural bioinformatics tools to analyse genetic variation and the functional effects that they may have in disease. To pursue this I have collaborated with a number of genome wide association and sequencing studies.

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Enquiries: Phone: +44 (0)1227 823743

School of Biosciences, University of Kent, Canterbury, Kent, CT2 7NJ

Last Updated: 29/09/2015