The COVID-19 pandemic demonstrates that a top priority for society, now and in the future, is to be able to respond quickly to diseases with effective treatments. Among the new tools that pharmaceutical industries and researchers have in their hands nowadays, there are the extensive computer simulations capable of evaluating in-silico the interaction between possible drugs and the target proteins. The central goal of the LIGATE project is to create and validate a leading application solution for drug discovery in High-Performance Computing (HPC) systems up to the exascale level. The overall project purpose is the automation of the drug design process, which is currently performed with substantial human effort throughout the different phases of the process: preparation of input parameters, management of data sets with billions of molecules, interaction with HPC queue management systems to handle jobs, and optimization of scoring function parameters and thresholds.

Today digital revolution is having a dramatic impact on the pharmaceutical industry and the entire healthcare system. The implementation of machine learning, extreme-scale computer simulations, and big data analytics in the drug design and development process offers an excellent opportunity to lower the risk of investment and reduce the time to the patient. Within the LIGATE project 1, we aim to integrate, extend, and co-design best-in-class European components to design Computer-Aided Drug Design (CADD) solutions exploiting today's high-end supercomputers and tomorrow's Exascale resources, fostering European competitiveness in the field. The proposed LIGATE solution is a fully integrated workflow that enables to deliver the result of a virtual screening campaign for drug discovery with the highest speed along with the highest accuracy. The full automation of the solution and the possibility to run it on multiple supercomputing centers at once permit to run an extreme scale in silico drug discovery campaign in few days to respond promptly for example to a worldwide pandemic crisis.

Major histocompatibility complex class I (MHC I) reports a cell's health status by presenting antigenic peptides inside its binding groove. However, MHC I binding grooves can differ largely in their plasticity, from binding grooves that are conformationally stable by themselves to those that require a high-affinity peptide to be bound to attain conformational stability. These latter MHC I alleles are dependent on the C-terminus of the peptide that stabilizes the F-pocket region of their binding grooves. It has remained unclear to what extent a peptide-MHC I complex (pMHC I) can tolerate the (intermittent) partial dissociation of high-affinity peptides, especially of the peptide's N-terminus. Using bias exchange umbrella sampling (BEUS), a technique to achieve enhanced sampling in molecular dynamics (MD) simulations, we obtained the free-energy profiles of the N-terminal dissociation of a respective high-affinity peptide from HLA-B*35:01 and HLA-B*44:02, two alleles on opposite ends of the scale regarding binding groove plasticity. The potential of mean force (PMF) for HLA-B*35:01 was calculated for two different sets of starting structures and is compared with a PMF obtained previously with a different force field to disentangle allele differences from force field and sampling effects. For both alleles, the free-energy profiles indicate that the peptide N-terminus dissociates in a substantial fraction of the pMHC I, suggesting that their crystal structures with fully bound peptides only partially capture the dynamic conformational ensemble of pMHC I in solution, and thus in the cell.