This new course will provide training in a range of complementary laboratory and bioinformatics techniques for the elucidation of protein interaction networks in disease.

Although a vast amount of genomic and genetic information is now available to biomedical researchers, the utility of these data cannot be fully exploited. Two key factors limit their functional extrapolation and strategic potential:

  1. The interaction profiles of many mutated proteins remain incomplete. As such, the potential for these proteins to affect known disease-related processes cannot be accurately predicted.
  2. The functional relevance of disease-associated mutations is hard to predict, unless the resulting effects on stability, post-translational modification and/or partner preference are experimentally defined.
For these reasons, training in the skills needed to construct and interpret high-density human disease-related protein interaction networks is vital to modern discovery-led translational research. High network density and secondary verification can only be achieved through multimodal analysis using an appropriate combination of interaction technologies.

What will you learn?

The course will cover laboratory and bioinformatics techniques for constructing disease–related protein interaction networks and the predictive interpretation and functional demonstration of genetic variation. Methods to characterise the conditional interaction profiles of different categories of human proteins will be covered.

Laboratory-based techniques will include yeast-2-hybrid, mass spectrometry and FRET-based cellular approaches to discover, quantify and investigate in vitro and in vivo interactions, as well as more specialist techniques such as quantification of protein interactions using surface plasmon resonance
(BIAcore technology).

Computational protein-network and proteomics analysis will be combined with data visualisation and data integration approaches. This will provide a platform on which data generated by the different experimental strands of the course will come together. A major focus will be how public, previously published large-scale data can be added to experimentally derived data for hypothesis testing and the planning of future experiments.

The programme will follow a thematically linked case-based learning approach, in which each technique uses a disease-related scenario to demonstrate how genetic mutations lead to conditional changes in post translational modification and/or partner interactions profiles. For example, participants will learn how to:

  • follow up yeast-2-hybrid identified interaction partners in mammalian cells using FRET to investigate correlated changes in localization and interactions of disease-related mutants
  • use surface plasmon resonance to determine the biophysical interactions properties of protein interactions and use this to determine if these parameters are altered in proteins containing disease-associated polymorphisms.
  • use affinity purification-mass spectrometry to characterise native protein complexes of wild type and mutant proteins
Together, the experimental and computational methods offered by this course will provide participants with a unique and comprehensive skill set for the identification and investigation of protein interaction networks in disease.
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(or call us: +44 (0)1223 496910)