Analysis and prediction of antibody stability

50% of the top 10 grossing drugs in 2014 were antibodies making them one of the most important classes of pharmaceuticals. Ensuring high stability is important for improving shelf-life, avoiding cold-chain storage, avoiding aggregation and crucially for reducing immunogenicity. Our goal is improving the biophysical characteristics of antibodies and remove unsuitable lead molecules early in the development pipeline.

The project will exploit high-throughput methods recently developed by UCB for generating paired antibody heavy and light chains from natural sources with small-scale biophysical analyses (e.g. thermal stability, hydrophobicity and surface charge). After spending 3 months at UCB to collect data for at least 1000 antibodies, machine-learning and data-mining approaches will be developed to analyze these data and develop a stability predictor. A return visit to UCB will allow the evaluation of mutations expected to improve stability.

ELIGIBILITY AND APPLICATION
This four-year studentship is funded jointly by the BBSRC London Interdisciplinary PhD Programme and UCB Celltech. It covers UK/EU tuition fees, an annual tax-free stipend in the region of £16,057 (exact amount confirmed each year) plus an additional tax free stipend from UCB of £4,000 p.a..

Applications will be considered from individuals with a background in life sciences or physical science (chemistry, mathematics, physics, computer science), though the equivalent of a first or upper second-class degree is essential. A postgraduate degree in a relevant subject would be desirable, as would basic molecular biology skills, experience of computer programming (e.g. C++, Python, R, but particularly Perl and C) and a demonstrable interest in development of antibody-based drugs.
For more information regarding the project, please contact Dr Andrew Martin
For more information about the programme, eligibility or the application process please contact the programme administrator.


Keywords: Antibodies, Bioinformatics, Computational Biology, Protein Structure, Machine Learning, Sequence analysis