SNIC SUPR
In Silico Drug Development
Dnr:

SNIC 2019/2-21

Type:

SNIC Large Compute

Principal Investigator:

Leif Eriksson

Affiliation:

Göteborgs universitet

Start Date:

2020-01-01

End Date:

2021-01-01

Primary Classification:

10407: Theoretical Chemistry

Secondary Classification:

30103: Medicinal Chemistry

Tertiary Classification:

10602: Biochemistry and Molecular Biology

Allocation

Abstract

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. signaling pathways in cancer cells or developing new antibiotics. We proceed with our studies of several targets for cancer therapy: 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, possible inhibition of the pro-caspase8 mimic cFLIP used by cancer cells to avoid apoptotic cascades, and the small peptide AGR2 shown to be an inducer of tumorogenesis. In antimicrobials research, we focus on virulence factors 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 exotoxin A, identification of ‘non-invasive’ inhibitors towards EHEC infections; and the beta-lactam hydrolyzing family of carbapenemase enzymes responsible for the main problem 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. 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 well-established protocols for these studies, involving protein preparation (homology modelling if needed), protein-protein docking calculations followed by replica MD simulations in the case of exploring signaling pathways. Normally, the MD simulations are of the length 500 ns each, and performed in triplicate, placing high demands for HPC resources. In the drug development projects, we perform systematic docking of large libraries, refined docking of top ranked ligands, and detailed MD simulations of resulting complexes, followed by additional hit-to-lead optimizations and further computations. The main database contains over 20 million compounds, and requires massively parallel execution. We have furthermore developed an inverse docking protocol to explore selectivity and possible side effects (safety) of obtained compounds. The size and extent of the simulations, and amount of data processed in the screening campaigns, justifies the 800 000 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 e.g., the histone acetyl 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.