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School of Biological and Chemical Sciences

Computational modelling of protein dynamics in heart disease

Supervisor: Dr Arianna Fornili

Project description

Applications are invited from self-funded candidates interested in completing a PhD starting in October 2019 in the group of Dr Arianna Fornili ( in the School of Biological and Chemical Sciences at Queen Mary University of London. 

The research activity of Dr Fornili’s group is focused on the computational modelling of structural and dynamical properties of proteins involved in heart contraction using Molecular Dynamics simulations. 
At the molecular level, heart contraction arises from the complex motions of proteins that compose the sarcomere, the basic repeating unit of heart muscle cells. The normal functioning of the heart relies on the correct regulation and coordination of these motions, so that amino acid mutations affecting single proteins can damage the whole sarcomeric machinery. Indeed, a large number of mutations in sarcomeric proteins have been linked to inherited cardiac diseases. 
The identification of new therapies for cardiomyopathies relies on a deep understanding of these mechanisms at the atomistic level. In particular, knowledge of the fundamental interactions that determine the protein motions could help in designing drugs that can target sarcomeric proteins and rescue their function. Using a combination of state-of-the-art Molecular Modelling, Molecular Dynamics and Bioinformatics techniques, the successful candidate will investigate how cardiomyopathy-related mutations affect the motion and stability of key components of the cardiac sarcomere. 

Facilities and training

The successful candidate will be trained in a wide range of computational techniques for the study of biomolecules, including basic and advanced Molecular Dynamics simulation techniques, Homology Modelling of protein structures, and Structural Bioinformatics tools. Moreover, the student will gain experience in Unix-based operating systems and in scripting and programming languages for biomolecular analyses. The training will also include the development of skills essential for career progression, including management of research projects, presentation and writing skills. 

Queen Mary University of London (QMUL) is a member of the Russell group and is one of the leading research-focused institutions in the UK. QMUL has been ranked 9th among multi-faculty institutions in the UK for research impact in the last Research Excellence Framework (REF) exercise. All PhD students and Post-doctoral researchers are part of the QMUL Doctoral College, which provides support with high-quality training and career development activities. 
Computational research at QMUL is supported by the shared High Performance Computing facility Apocrita, a 260+ nodes fully-managed supercomputer with more than 4,000 cores for parallel calculations. Dr Fornili’s lab also hosts state-of-the-art equipment optimised for the production and storage of biomolecular simulation data. 

Eligibility and Applying

Applications are invited from candidates with, or expecting to be awarded, at least an upper-second class degree in Chemistry, Biochemistry, Physics, Biophysics or related disciplines. Previous experience in molecular modelling/simulation and/or computer programming is desirable but not essential. International students are required to provide evidence of their proficiency in English language skills. 

For informal enquires please contact Dr. Fornili () and include your CV, a cover letter explaining how you plan to fund your project and the contact information of two references. Please note that a deadline of 31st January 2019 applies to CSC candidates to submit a formal application to QMUL. 


  • M. Tiberti, B.-D. Lechner, A. Fornili: Binding Pockets Under Mechanical Stress. Biophysical J. 114 (2018) 31A. 
  • S. Hashem, M. Tiberti, A. Fornili: Allosteric modulation of cardiac myosin dynamics by omecamtiv mecarbil, PLoS Comp. Biol. 13 (2017) e1005826. 
  • M. Tiberti, A. Pandini, F. Fraternali, A. Fornili: In silico identification of rescue sites by double force scanning, Bioinformatics (2017) doi: 10.1093/bioinformatics/btx515. 
  • A. Pandini and A. Fornili: Using Local States To Drive the Sampling of Global Conformations in Proteins. J. Chem. Theory Comput. 12 (2016) 1368. 
  • A. Fornili, E. Rostkova, F. Fraternali, M. Pfuhl: Effect of RLC N-Terminal Tails on the Structure and Dynamics of Cardiac Myosin. Biophysical J. 110 (2016) 297A. 
  • A. Fornili, E. Rostkova, F. Fraternali, M. Pfuhl: Phosphorylation modulates the dynamics of the N-terminal tail in cardiac RLC. Biophysical J. 106 (2014) 33A. 
  • A. Pandini, A. Fornili, F. Fraternali, J. Kleinjung: Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics. FASEB J. 26 (2012) 868. 
  • A. Fornili, B. Giabbai, G. Garau, M. Degano: Energy landscapes associated with macromolecular conformational changes from endpoint structures, J. Am. Chem. Soc. 132 (2010) 17570. 

See also