From molecular recognition to drug design: Deeply exploring and characterizing molecular pockets to overcome drug resistance

The Cancer Research UK Cambridge Institute (CRUK CI) is a joint venture between the Charity, Cancer Research UK, and the University of Cambridge. The Institute has excellent state-of-the-art facilities and research ranges from basic cancer biology and computational biology through to translational research and clinical application. Graduate students play a pivotal role in the continuing success of our research programmes and gaining a studentship in the Institute is an excellent opportunity to start a research career in an environment committed to training outstanding cancer research scientists of the future. For further information, go to: https://www.cruk.cam.ac.uk/jobs-and-studentships/postgraduate-study

Dr Pau Creixell wishes to appoint a student to work on the project entitled: From molecular recognition to drug design: Deeply exploring and characterizing molecular pockets to overcome drug resistance.

For further information about the research group, including their most recent publications, please visit their website: https://www.cruk.cam.ac.uk/research-groups/creixell-group

Project description: Our newly established lab combines machine learning approaches with high-throughput biochemistry to study how proteins recognise their substrates, how cancer could perturb this process, and how to find highly selective drugs to overcome drug resistance in cancers.

Aberrant protein kinase-driven signaling causally contributes to cancer and, in consequence, protein kinases are major targets for cancer therapeutics. Therapeutic efforts have traditionally focused on targeting the ATP-binding pocket of protein kinases, however, these drugs often must be used in high concentrations to outcompete ATP, have low specificity, and the proteins often develop resistance. In this project, by deeply studying and characterizing the substrate binding pocket with novel peptide display technology we have developed combined with machine learning and computational modelling, we will work towards developing high-specificity drugs that avoid the common and well-known mechanisms of drug resistance.

Qualifications/skills: As a recently established lab, we are particularly interested in students who will be highly motivated and share our excitement for science. You should be independent while also capable of taking feedback and input from others. While we will consider candidates from all backgrounds, those with interests in biology, chemistry, physics and/or machine learning/artificial intelligence may have an advantage.

How to apply: All applications need to be made using the University Applicant Portal. Please visit: https://www.graduate.study.cam.ac.uk/courses/directory/cvcrpdmsc for further information about the course and to access the applicant portal.

To complete your on-line application, you need to provide the following:

Reference Request: The names and contact details of two academic referees who have agreed to act on your behalf.

Research: If you wish to be considered for more than one studentship, please enter the names of all of the supervisors you wish to consider your application in the ’Research summary’ text box. If you only wish your application to be considered by a single supervisor, then please enter their name in the ’Research Supervisor’ text box. Please describe your past ’Research experience’ in the appropriate text box.

Course Specific Questions: Your statement of interest (limit of 2,500 characters) should explain why you wish to be considered for the studentship and which qualities and experience you will bring to the role. Please also state how you learned of the studentship.

Supporting Documents: Please upload your CV (PDF file), which should include a list of the examinations taken at undergraduate level and if possible, your examination results