Kingston University London

Research bites

Our research bites give brief insights into some of the exciting research being undertaken by staff and students at Kingston.

Predicting protein-protein interactions

PhD student Reyhaneh Esmaielbeiki is developing computational frameworks that can predict protein-protein interactions (PPIs).

The interaction of proteins with other proteins, DNA, RNA and small molecules is essential for the functionality of living cells. Modifications in PPIs affect the events that take place within cells which may lead to critical diseases such as cancer. Therefore, knowledge about protein interactions can provide key information for drug design. Since experimental methods are costly and have limitations, Reyhaneh is developing a novel computational framework able to predict these interactions.

Recent work* investigated an interaction which boosts the immune system when the body is attacked by microbes and viruses. This first video shows Reyhaneh's proposed model of interaction between the two proteins (coloured in red and blue). The model is in agreement with literature findings and as such it could be used to design novel anti-microbial drugs. This work was supervised by Dr Jean-Christophe Nebel and Professor Declan Naughton.

Watch other videos on the subject:

*Esmaielbeiki, R; Naughton, D; Nebel, J C. Structure prediction of LDLR-HNP1 complex based on docking enhanced by LDLR binding 3D motif. Protein & Peptide Letters, 2011. (In press)

DNA damage

PhD student Delphine Thenet is developing techniques to study the role of chromosome structure in the repair of DNA damage in both normal and tumour cells.

The confocal microscope enables the acquisition of multiple microslices across nuclei that are subsequently combined to produce 3D visualisation and analysis.

Watch a video clip showing a 3D visualisation of confocal images of human fibroblast nuclei showing chromosomes 1 (green) and 2 (red) occupying discrete regions of the nuclei. PhD supervisor Dr Lucy Jones.

Machine learning algorithms

PhD student Victoria Bloom is working with machine learning algorithms which can be trained to recognise a wide range of actions. These have many uses in the computer gaming industry.

A new generation of games based on full body play such as dance and sports games have increased the appeal of gaming to family members of all ages.

Full body play relies on detecting players movements using sensors to provide a controller free gaming experience. The Kinect, originally developed for the Xbox 360 games console, has a wide range of released titles but they are limited to a small set of actions as many action recognition algorithms are hardcoded.

An alternative approach is to use machine learning algorithms that can be trained to recognise a wide range of actions including sporting, driving and action-adventure actions such as punching, kicking, running, jumping, dropping, firing, changing weapon, throwing and defending. The results of Victoria's machine learning algorithm are shown in a video clip as yellow stars on the punch bag. Machine learning algorithms increase the complexity and appeal of games that can be developed.