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Reactivity of iron-sulfur clusters studied by advanced many-electron wave-functions

Grant number: 19/26811-1
Support type:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): April 01, 2020
Effective date (End): September 30, 2020
Field of knowledge:Biological Sciences - Biophysics - Molecular Biophysics
Principal researcher:Guilherme Menegon Arantes
Grantee:Felipe Curtolo
Supervisor abroad: Sandeep Sharma
Home Institution: Instituto de Química (IQ). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Research place: University of Colorado Boulder, United States  
Associated to the scholarship:17/26109-0 - Computer simulations of reactivity and electron transfer in respiratory complex II, BP.DD

Abstract

Iron-sulfur clusters (FeS) are the most abundant metal cofactors involved in biological redox reactions. Their electronic structure with multiple low-lying and near-degenerate states is very difficult to model accurately. But, the advent of advanced electronic structure methods such as density matrix renormalization group (DMRG) and selected configuration interaction (sCI) have opened the possibility to study the reactivity of FeS clusters. Here, we propose to investigate the mechanism of the electron transfer between the binuclear FeS (2Fe-2S) and a flavin molecule. This is a model reaction of the second redox step found in the family of enzymes related to the respiratory complex II. We will use the semistochastic heat-bath configuration interaction (SHCI) method, a modern incarnation of sCI that allows calculation of correlation energies using very large active-spaces in a determinant basis with controlled approximations. This will allow the rigorous calculation of reactions involving polynuclear FeS clusters for the first time. However, the approximations inherent in the SHCI algorithm can lead to noisy energies which can adversely affect dynamics calculations. Thus, we also propose to train a deep neural network from SHCI energies and gradients at some configurations. These neural networks will then allow us to calculate smooth energies and gradients globally. We believe the results obtained here will allow the understanding of electron transfer reactions involving FeS relevant to important biochemical processes and also contribute to further development of the SHCI method. (AU)

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