A Neural Network based Approach for Molecular Simulations of Metalloproteins

Metal containing biomolecules are surprisingly common and essential for a spectrum of biological activities and physiological functions including i.a. respiration or photosynthesis. About one third of all the proteins include a metal-site, those metalloproteins typically coordinate metals by amino acid residues or organic co-factors. Metalloproteins have been investigated extensively towards understanding of their structure, function and, in particular, metal-ligand interactions which are relevant for drug design of metalloenzyme inhibitors and metallodrugs. Modelling and simulation of metalloproteins is challenging in various respects. Molecular dynamics (MD) simulations together with classical force fields do not suffice to describe the behaviour of metals and coordinated atoms. A quantum mechanical (QM) description of the systems is required to capture electronic effects. However, the efficiency of those methods is rather poor in the context of QM/MM hybrid approaches that are necessary to study large and complex biomolecules. To accelerate such hybrid systems, machine learning approaches seem to be promising. With the advances of deep learning algorithms, QM potential energy surfaces can be reproduced. Novel approaches in computational chemistry utilize neural networks (NNs) for the quantum description. With this project we propose a hybrid NN/MM-MD workflow, which we will implement in the GROMOS simulation package and apply the developed methodology to metal-sites of increasing complexity. Thus, we hope to improve the description of metal-ligand interactions in classical simulations with a specific focus on metalloproteins. The project opens the way for numerous applications and will allow for the evaluation of free-energy differences at a QM/MM level of theory, without the methodological challenges and computational costs. We expect that successful completion of the work will have considerable impact in the field of molecular simulations of metalloproteins.