The accurate prediction of the affinity between proteins and other biomolecules or small molecules is crucial in the drug design process or protein engineering. Nowadays, a number of computational methods is available to perform these predictions with sufficient quality for systems that can be correctly described by classical force fields. However, the parameterization of force fields for drugs and drug candidates (ligands) is still not straightforward and it is a common source of inaccuracies. This project is focused on the development of a novel method for free energy calculations. We will combine a new quantum mechanical/molecular mechanical (QM/MM) method (Buffer region neural network, BuRNN)[1] with alchemical free energy calculations. The resulting method should be able to perform alchemical free energy calculations at the QM level of theory.
In my poster, I will focus on the development of the BuRNN approach. The basic principle of the method and its performance on our test system (methanol in water) will be presented. The results will be compared with the traditional QM/MM schemes. In the future, we plan to further develop BuRNN to include the protein environment. This will allow us to describe protein-ligand interactions with QM accuracy.
The financial support received for the Christian Doppler Laboratory for Molecular Informatics in the Biosciences by the Austrian Federal Ministry of Labour and Economy, the National Foundation for Research, Technology and Development, the Christian Doppler Research Association, BASF SE and Boehringer Ingelheim RCV GmbH & Co KG is gratefully acknowledged.