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Deep Learning / Bioinformatics Approach for Protein-Protein Interaction Prediction

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Since most molecular processes rely on protein–protein interactions PPIs), knowledge of those interactions is extremely valuable for biomedical research and drug design. Despite the availability of high-throughput proteomics approaches, the human protein interactome is still largely incomplete. Therefore, ‘in-silico’ prediction (i.e. computer based) has become the only practical way of revealing the full extent of the human PPI network [1]. Although the development of bioinformatics methods allowing the prediction of such interactions is a very active field of investigation [2], existing approaches tend to focus on specific classes of interactions. For example, PPIs through β-sheet interfaces have been of particular interest [3], predominantly resulting from their potential to cause aggregation.

With the protein structure database, i.e. Protein Data Bank, approaching 150,000 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 [4] 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, Bioinformatics or related disciplines. In addition, they should have excellent programming skills in Matlab, Python, Java and/or C++ and fundamental knowledge of bioinformatics.

Qualified applicants are strongly encouraged to contact informally the supervising academic, Dr Nebel (), to discuss the application. More on Dr Nebel’s research group and activities can be found on his personal website:

Funding Notes

There is no funding for this project: applications can only be accepted from self-funded candidates

How good is research at Kingston University in Computer Science and Informatics?

FTE Category A staff submitted: 10.20

Research output data provided by the Research Excellence Framework (REF)

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