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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.

ORCID ID: 0000-0001-5428-6479

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Also view these in the Kent Academic Repository

Article
Pappalardo, M. et al. (2017). Changes associated with Ebola virus adaptation to novel species. Bioinformatics [Online]. Available at: https://doi.org/10.1093/bioinformatics/btx065.
Saintas, E. et al. (2017). Acquired resistance to oxaliplatin is not directly associated with increased resistance to DNA damage in SK-N-ASrOXALI4000, a newly established oxaliplatin-resistant sub-line of the neuroblastoma cell line SK-N-AS. PloS one [Online] 12:e0172140. Available at: http://dx.doi.org/10.1371/journal.pone.0172140.
Pappalardo, M. et al. (2017). Investigating Ebola virus pathogenicity using Molecular Dynamics. BMC Genomics.
Saintas, E. et al. (2017). Acquired resistance to oxaliplatin is not directly associated with increased resistance to DNA damage in SK-N-ASrOXALI4000, a newly established oxaliplatin-resistant sub-line of the neuroblastoma cell line SK-N-AS. PLoS ONE [Online] 12:e0172140. Available at: http://dx.doi.org/10.1371/journal.pone.0172140.
Popay, A. et al. (2017). Dexamethasone for the Prevention of Cisplatin-induced Ototoxicity. Clinical Cancer Drugs [Online] 4:59-64. Available at: https://doi.org/10.2174/2212697X04666170331171359.
Martell, H. et al. (2017). Associating mutations causing cystinuria with disease severity with the aim of providing precision medicine. BMC Genomics.
Cantoni, D. et al. (2016). Risks Posed by Reston, the Forgotten Ebolavirus. mSphere [Online] 1. Available at: http://dx.doi.org/10.1128/mSphere.00322-16.
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.
Voges, Y. et al. (2016). Effects of YM155 on survivin levels and viability in neuroblastoma cells with acquired drug resistance. Cell death & disease [Online] 7:e2410. Available at: http://dx.doi.org/10.1038/cddis.2016.257.
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.
Williams, L. et al. (2016). Mitochondrial diversity within ciliates. Mitochondrial diversity within ciliates.
Lobo, S. et al. (2016). Desulfovibrio vulgarisCbiKPcobaltochelatase: evolution of a haem binding protein orchestrated by the incorporation of two histidine residues. Environmental Microbiology [Online] 19:106-118. Available at: http://doi.org/10.1111/1462-2920.13479.
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.
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. (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.
Wong, K. et al. (2014). The Genetic Diversity of Cystinuria in a UK Population of Patients. BJU International [Online] 116:109-116. Available at: http://dx.doi.org/10.1111/bju.12894.
Zhi, D. et al. (2014). The South Asian Genome. PLoS ONE [Online] 9:e102645. Available at: http://dx.doi.org/10.1371/journal.pone.0102645.
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.
Talman, A. et al. (2014). Proteomic analysis of the Plasmodium male gamete reveals the key role for glycolysis in flagellar motility. Malaria Journal [Online] 13:315. Available at: http://dx.doi.org/10.1186/1475-2875-13-315.
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.
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.
Showing 25 of 37 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: 17/03/2017