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Mathematical modelling of epistasis in proteins using network analysis and statistical learning

Proteins are biopolymers composed of repetitive units (residues) from a set of 20 amino acids that form complex three-dimensional topologies. Protein topologies/structures undergo conformational changes over picosecond to millisecond timescales.

These changes are important for biological function and arise from inter and intra residue interactions and interactions with the solvent. These conformational changes can be modelled, using computer simulations and well-established software, generating millions of 3D structures that are saved as time-dependent trajectories.

To design proteins for novel functions or enhance their existing biological activity, advances in computational protocols for protein design are desired. Towards this goal, understanding the epistasis at the protein level, i.e. networks of synergistic interactions between residues that contribute to the function is the first step.

In this project, using the molecular dynamics simulations and sequence analysis data of a test protein system, the doctoral researcher will

  1. develop a mathematical formalism to analyse the complex network of interactions between the protein residues using time series network analysis, which can handle multiple densities of states;

  2. use graph convolutional networks to learn and predict effects of mutations on structural dynamics contributing to biological function 

  3. Integrate this method to select design sites into our computational protocol for the selection of design sites.

The ideal candidate will have BSc and/or MSc degree in mathematics and/or computer science. Knowledge of C/Python and R will be highly desirable.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Sarath Dantu - Associate Lecturer in Department of Computer Science 

Alessandro Pandini - My research activity focuses on the development and application of computational methods to study protein dynamics and its role in protein-ligand binding, protein-protein interactions, and protein design. I obtained my PhD in Computational Chemistry at the University of Milan-Bicocca under the supervision of Prof. Laura Bonati. As part of her research group I contributed to the unveil the molecular mechanism of toxic response mediated by binding of dioxins to the Aryl hydrocarbon Receptor. In 2008 I was awarded a Marie Curie Inter European Fellowship to work at the MRC National Institute for Medical Research (NIMR) under the supervision of Dr. Willie R. Taylor and Dr. Jens Kleinjung. From 2011 to 2014 he was a BBSRC-funded postdoctoral research assistant in the group of Prof. Franca Fraternali at King’s College London working on methods to investigate allosteric regulation, and to analyse protein-protein interaction interfaces and networks. During my career I developed and applied novel approaches combining structural bioinformatics and molecular simulation to address challenging biological questions, especially in relation to protein function, allosteric regulation and drug design. I introduced novel points of view in the definition of the limits and potential of molecular docking on theoretical models and in the use of molecular dynamics for drug design and medicinal chemistry. In particular, I developed an innovative computational method to detect local functional motions and to describe allosteric transmission in protein structures. Most recently, in collaboration with Dr. Arianna Fornili (QMUL), I contributed to the development of a novel strategy for biasing the sampling of local states to drive the global conformational transitions in proteins. In collaboration with Dr. Shahid Khan (LBNL – Berkeley Lab) and Dr. Willie Taylor, I have contributed to explain the relationships between residue coevolution and molecular dynamics in two bacterial ring assemblies. 

Related Research Group(s)

Computational Biology

Computational Biology - Developing and applying novel methodologies for computational modelling, simulation and analysis of biological systems

Intelligent Data Analysis

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.