About the Project
With the protein structure database, i.e. Protein Data Bank, approaching 200,000 entries – the majority of them revealing detailed information of PPIs -, sufficient data is now available to train the most data hungry machine learning approaches such as Deep Learning. The aim of this research is to exploit known 3D structure interactions in protein complexes to train a Deep Learning model able to predict if two proteins, defined only by their sequence, can form a dimer. Successful completion of the project requires addressing the following scientific objectives:
- Adaptation of existing Deep Learning architectures, such as convolutional neural network (CNN) and recurrent neural network (RNN), to design a suitable classification pipeline
- Implementation of the proposed Deep Learning based architecture on the Kingston University GPU farm
- PPI data extraction and model training
- Hyper-parameter tuning and classifier evaluation on standard PPI benchmark data sets
This project does not include funding. Applicants should have, at least, an Honours Degree at 2.1 or above (or equivalent) in Computer Science or related disciplines. In addition, they should have excellent programming skills in Matlab, Python, Java and/or C++. Fundamental knowledge of bioinformatics is not essential but desirable.
Qualified applicants are strongly encouraged to contact informally the supervising academic, Prof. Nebel ([email protected]), to discuss the application. More on Prof. Nebel’s research group and activities can be found on his personal website: https://kunet.kingston.ac.uk/ku33185
 Progress and Challenges in Predicting Protein Interfaces, R. Esmaielbeiki, K. Krawczyk, B. Knapp, J.-C. Nebel and C.M. Deane, Briefings in Bioinformatics, 17(1):117-131, 2016
 Coil conversion to β-strand induced by dimerisation, J. Laibe, A. Caffrey, M. Broutin, S. Guiglion,S.B. Pierscionek & J.-C. Nebel, PROTEINS: Structure, Function, and Bioinformatics, 86(12):1221-1230, 2018
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