PhD student profiles


Stuart Tetechner

Stuart Tetchner
Wellcome Trust 4-year Interdisciplinary PhD Programme, beginning in Autumn 2011

Project title
Improving multi-domain protein structure modelling using predicted coevolving residue pairs

Principal investigator: Professor David Jones, ISMB, UCL
Co-investigators: Dr Renos Savva and Dr Maya Topf, ISMB, Birkbeck

Before starting my PhD, I studied BSc Biological Sciences at the University of Reading. During my undergraduate studies I developed an interest in the prediction of protein structure and function, so I performed my dissertation on the prediction of ligand binding sites, supervised by Dr Liam McGuffin. This project involved developing and evaluating computational predictions of small molecule binding sites for the 9th "Critical Assessment of techniques for protein Structure Prediction" (CASP) experiment.

Rotation 1
Prediction of zinc binding sites in complement C3. Supervised by Dr. Andrew Martin and Prof. Steve Perkins.

Rotation 2
Genome Mining for Novel Natural Products. Supervised by Dr. Philip Lowden.

Rotation 3
Assessment of weak zinc binding to complement C3 using solution X-ray scattering. Supervised by Prof. Steve Perkins and Dr. Andrew Martin.

PhD Project

Amino acids have been observed to undergo “correlated” mutations if they interact within the native structure. The underlying concept behind correlated mutations is that if one of a pair of interacting residues is to mutate, the local environment of the interaction can be maintained by a concerted change in an interacting partner. These subtle patterns of covariation can be observed given sufficiently large amounts of sequence data, providing structural insight directly from sequence. However, accurately identifying genuine instances of covariation is complicated due to biases arising from the underlying relationship between sequences and effects stemming from multiple instances of covariance. In recent years there have been considerable advancements in statistical techniques to reduce the effect of both of these sources of bias.

My PhD project has focused on developing approaches to harness these recent advancements in statistical methods, allowing us to identify correlated mutations more accurately than ever before. In particular, my work has focused on predicting pairs which interact between domains of large proteins. We have evaluated a number of cutting-edge methods and demonstrated that our predictions improve the modelling of protein structures.


figure 1


Figure: Successfully predicted interacting pairs (shown in green) between the two domains of the Biotin carboxylase protein from E. coli.

S Tetchner, T Kosciolek, DT Jones. (2014). Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction. Bio-Algorithms and Med-Systems, 10 (4), 243-254.

DT Jones, T Singh, T Kosciolek, S Tetchner. (2014). MetaPSICOV: Combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics, btu791.

Zinc-induced Self-association of Complement C3b and Factor H - Implications for Inflammation and Age-related Macular Degeneration. (2013). R Nan, S Tetchner, E Rodriguez, PJ Pao, J Gor, I Lengyel, SJ Perkins. Journal of Biological Chemistry, 288 (26), 19197-19210.

DB Roche, MT Buenavista, SJ Tetchner, LJ McGuffin. (2011). The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic acids research, 39 (suppl 2), W171-W176.

DB Roche, SJ Tetchner, LJ McGuffin. (2011). FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins. BMC bioinformatics, 12 (1), 160.

DB Roche, SJ Tetchner, LJ McGuffin. (2010). The binding site distance test score: a robust method for the assessment of predicted protein binding sites. Bioinformatics, 26 (22), 2920-2921.




Institute of Structural and Molecular Biology, University of London
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