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Details of Grant 

EPSRC Reference: EP/N020669/1
Title: Accurate free energy calculations for biomolecular catalysis of electron transfer
Principal Investigator: Rosta, Dr E
Other Investigators:
Researcher Co-Investigators:
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Department: Chemistry
Organisation: Kings College London
Scheme: First Grant - Revised 2009
Starts: 01 July 2016 Ends: 30 September 2017 Value (£): 100,972
EPSRC Research Topic Classifications:
Catalysis & Applied Catalysis Protein chemistry
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
18 Feb 2016 EPSRC Physical Sciences Chemistry - February 2016 Announced
Summary on Grant Application Form
Long- and short-range electron transfer (ET) between proteins is vital for all living systems, and plays an essential role in photosynthesis and bio-assimilation. ET is a central process for the transfer and storage of solar energy in advanced materials as well. Our understanding of the corresponding biological electron transport can inspire new approaches for developing and advancing energy efficient technologies. However, robust, accurate, and predictive underlying theoretical and computational models are still needed to determine structure, energetics and kinetics of ET processes in materials and biological systems.

We introduced a new analysis method, DHAM, which can be used to calculate rates and free energies from biased or unbiased simulation trajectories (Rosta and Hummer, JCTC 2015). The DHAM method is a generalisation of the current state-of-the-art weighted histogram analysis method (WHAM), which is widely used to obtain accurate free energies from biased molecular simulations. We showed that WHAM-computed free energies can exhibit significant errors, e.g. when analysing simulations under weak bias - a problem overcome by using DHAM. Our method is designed to determine a global Markov chain based on a maximum likelihood approach to analyse multiple simulation trajectories. We construct the Markov transition matrix along a discretized reaction coordinate, and obtain the corresponding stationary distribution to determine the free energy profile. Importantly, our formalism provides kinetic information of biased simulations. By building on this approach, my main aim is to develop a new method to study electron transfer.

As a first application, we will study the catalytic reaction of FNR, a central enzyme in the final step of the photosynthetic electron transfer processes using the energy of light to store high-energy electrons in the form of chemical bonds in NADPH. Our novel computational methods will provide accurate free energies as well as kinetic information about the dynamics of the photosynthetic systems. Importantly, it will allow us to understand the underlying mechanism, including the elusive coupled proton transfer steps that occur together with the electron transfer reactions in FNR.

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