Computational chemistry has an important role to fill in the development of new pharmaceuticals. With high performance computer clusters coupled to the latest developments in algorithms and software, we are able to screen vast libraries of compounds searching for new drug candidates, create in silico models of target proteins, and explore protein-protein interactions crucial for e.g. signalling pathways in cancer cells or toxin mechanisms of action.
We aim to study several targets for cancer therapy, in order to identify small molecule inhibitors: the protein kinases IRE1 and PERK essential for the unfolded protein response and XBP1 mRNA splicing, as well as the XBP1 ligase RtcB crucial for cancer cell survival. We also explore the mechanism and identify possible small molecular binders to APAF-1 that triggers apoptosis, and the small peptide AGR2 which recent results show to be an inducer of tumorogenesis.
In our antimicrobials research, we focus on secreted targets with the aim to reduce the propensity for resistance development of the pathogen and on blocking one of the main proteins responsible for resistance towards common beta-lactam ring containing antibiotics. The targets include the secreted P. aeruginosa virulence factor exotoxin A, and the identification of ‘non-invasive’ inhibitors towards EHEC infections; and the beta-lactam hydrolyzing family of carbapenemase enzymes responsible for the main issue of antibiotics resistance.
In anti-malaria research, we target proteins for which we can also express the corresponding human orthologues in genetically engineered yeast cell lines. By combining inverse docking to filter out selective compounds, and simultaneously testing these in competitive assays, we are able to identify active compounds with high selectivity and very few adverse side effects.
We follow a well-established protocol for these studies, involving protein preparation (homology modelling if needed), systematic docking of ZINC clean drug-like library and similar, refined docking of top ranked ligands, and detailed MD simulations of resulting complexes. We have developed an inverse docking protocol to explore selectivity and possible side effects (safety) of obtained compounds. The clean drug-like compound library to be used contains over 13 million compounds, and we are able to conduct a detailed screening campaign plus ensuing MD simulations and compound refinements using approximately 1 000 000 coreh for each target; adding up to the total 1 000 000 coreh/month currently applied for.
The research group has been highly successful in using in silico methods for drug development, with several patented drugs already developed, and with a steady stream of high quality publications resulting from the use of the SNIC resources. From recent SNIC allocations, new compounds targeting histone methyl transferase Tip60, the retinioc acid degrading enzyme CYP26B1, the extracellular domain of RET kinase and the motor kinesin KIF18B have been identified and proven positive in assays.