Research

In recent years, molecular informatics has transformed from a niche discipline into a driving force of the research and development of functional small molecules such as drugs and agrochemicals. Advanced algorithms as well as powerful computer hardware are now opening unprecedented opportunities for the targeted design of safe and efficacious small molecules. However, the full potential of computational methods in the biosciences is by far not exploited yet. One of the main reasons for this situation is the fact that the most powerful technologies in molecular informatics, machine learning and simulations in particular, depend on the availability of substantial amounts of high-quality data for development and validation. Despite recently launched initiatives to boost collaborative research and learning, the vast majority of high-quality chemical, biological and structural data remain behind corporate firewalls, inaccessible for research by experts in academia. This initiative for the Christian Doppler Laboratory for Molecular Informatics in the Biosciences seeks to push the frontiers of machine learning and molecular dynamics simulations technologies for the prediction of small-molecule bioactivity by supporting three expert academic research groups of the University of Vienna and the University of Natural Resources and Life Sciences (BOKU) with big data on the chemical and biological properties of small molecules, and with significant capacities for experimental testing and method validation. The unique synergy that will be generated by this consortium stems from two important factors: First, the two industry partners of this consortium have strong interest in cheminformatics but their business areas are non-competing. Second, and from a scientific point highly important, these industry partners focus on distinct chemical spaces, opening a unique opportunity for academics to boost the capacity and applicability of in silico methods with uniquely diverse, high-quality data.

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.

The initial interaction of SARS-CoV-2 with human cells results from a protein-protein interaction between the SARS-CoV-2 Spike protein and the human ACE2 receptor. Promising novel pharmaceutics are based on solubilized versions of the ACE2 receptor, potentially blocking the virus proteins and hindering an interaction with human ACE2 present on the cells. Atomistic models of the Spike proteins and the ACE2 receptor are available, with the glycan structures reduced to the first 1-2 sugar residues. We have created a complete model of the Spike-ACE2 interaction, with full glycosylation. The model confirms that the glycans can play a significant role in the interaction. In particular, there is evidence that removal of the N90 glycan strengthens the interaction. This offers possibilities to engineer the therapeutic proteins to show stronger interactions with Spike and therefore be more effective. We will create computer models of Spike – ACE2 interactions with different variants of ACE2. This includes species-specific variations (mouse ACE2 does not interact with Spike), naturally occurring genetic modifications, and alternative therapeutic formats.