Simulation techniques for the treatment of biomolecular systems

The framework of statistical simulation techniques, especially Molecular Dynamics (MD) methods are well established tools for investigations of structure and dynamics of chemical systems. One of the most crucial aspects is an accurate treatment of energies and forces which can either be treated on the basis of Molecular Mechanics (MM), i.e. empirical, parametrised potential functions, or on the basis of quantum mechanics (QM) taking into account the distributions of the electrons within the systems. QM methods lead to a more reliable description of the system but, due to the enormous computational demand of such computations the manageable number of particles is limited. MM methods are less demanding with respect to computational resources but the accuracy is in many cases not sufficient. Furthermore, the formation and cleavage of chemical bonds cannot be described by the majority of MM methods. The combination of these approaches lead to the formulation of hybride quantum mechanical/molecular mechanical (QM/MM) methodologies. The idea of these frameworks are the treatment of energies and forces in the chemically most relevant region utilising appropriate quantum chemical methods, whereas the remaining part of the system is accounted for via molecular mechanics. This approach combines the accuracy of quantum mechanics with the affordability of a molecular mechanical treatment. The quality of this framework has been demonstrated in MD simulation studies of various electrolyte systems utilising conventional QM/MM schemes as well as the QMCF (quantum mechanical charge field) formalism, which was recently developed in the Theoretical Chemistry Division of the University of Innsbruck during the applicant`s Ph.D. thesis. Although force field methods developed during the last decades have proven as versatile tools to examine biomolecular structures, the treatment of active sites including metal centers or the formation and cleavage of covalent bonds are still challenging tasks in simulation studies. The combination of the QMCF approach with force field methods appears to be a promising approach to extended the framework of biomolecular simulations with a quantum mechanical treatment of the active site based on the QMCF ansatz. Two main aspects are of importance for this project. First, the improvement of force field methods for the treatment of biomolecules by the inclusion of the new QM formalism is a major challenge. Due to the large size of biomolecules a different setup and analysis scheme is required compared to the framework for electrolyte systems consisting of small molecules. However, as the basic methodical approach is identical, the time required to obtain the necessary specific knowledge is reduced and can be estimated as approximately six month. The research group of Prof. van Gunsteren at the the Laboratory of Physical Chemistry at the ETH Zürich, Switzerland, is well-known for its experience and new methodical developments in the field of biomolecular modelling. After this visit a six month stay at the home university is required to establish all new methodologies developed and the related software at the Theoretical Chemistry Division. The second important aspect is related to the enormous computing time associated with quantum chemical computations compared to classical calculations. For this reason the energy hypersurface has to be sampled in the most efficient ways in order to reduce the number of necessary computations. This can be achieved utilising specially designed algorithms such as Replica Exchange Molecular Dynamics (RE-MD) and similar methods aimed at an efficient treatment of reaction dynamics. Dr. Wales and coworkers in the Department of Chemistry at the University of Cambridge have established many of the fundamental algorithms for applications ranging from biomolecular systems to cluster chemistry. Another six months of a postdoctoral position at this group is, therefore, desirable to become familiar with these advanced computational techniques and to combine them with the QMCF MD methodology.