Systems Biology

I was a visiting-Ph.D student in the Oxford Protein Informatics Group (OPIG) under Dr. Charlotte Deane's supervison between 2008 and 2009. I had worked with Dr. Rebecca Hamer, Prof. Gesine Reinert, and Dr. Charlotte Deane. We were interested in developing the computational methods for correlation mutation of proteins. Hopefully, some novel methods can be proposed to discover the covariation patterns of proteins based on various data sources, such as the sequences, the structues, the phylogeny trees, the annotations, etc. Our aim was to integrate the informations we have by some network methodology combined with statistics tools to bring us more insight of the protein coevolution. Two outcomes from this project:

• R. Hamer(#), Qiang Luo(#), J.P. Armitage, G. Reinert, C.M. Deane(*). i-Patch: Inter-Protein Contact Prediction using Local Network Information. Proteins: Structure,Function, and Bioinformatics. October 2010, 78(13):2781--2797.
• Luo, Qiang(#)(*)，Hamer, Rebecca，Reinert, Gesine，Deane, Charlotte M.，Local Network Patterns in Protein-Protein Interfaces，PLos One，2013，8 (3). (Algorithm for PPI interface scoring by four tuples is avilable)

IPCoP (also known as i-Patch; a web tool of this algorithm is also avliable at the OPIG website)

Based on the fact that any type of amino acids can be contact sites and any type of pairs of amino acids can be contact pairs on the interfaces suggest that the intra-molecule neighbourhood information of a site on the protein surface is as important as this site itself. The propensity profiles of the contact sites, contact pairs, contact triangles, and their intra-molecule neighborhood have been calculated against the surface residues, and the degree to which a site fits these profiles measures the probability that this site is a inter-molecule contact site. A series of propensity scores, including Amino acid Propensity (APro), Pair Propensity(PPro), Triangle Propensity(TPro), have been proposed, and combined with McBASC score, the Inter-Protein Contact Prediction (IPCoP) score has been established. On our dataset with 31 proteins, the proposed scores perform much better than the other correlated mutation scores.