Given the enormous social and health impact of the pandemic triggered by severe acute respiratory syndrome 2 (SARS-CoV-2), the scientific community made a huge effort to provide an immediate response to the challenges posed by Coronavirus disease 2019 (COVID-19). One of the most important proteins of the virus is an enzyme, called 3CLpro or main protease, already identified as an important pharmacological target also in SARS and Middle East respiratory syndrome virus (MERS) viruses. This protein triggers the production of a whole series of enzymes necessary for the virus to carry out its replicating and infectious activities. Therefore, it is crucial to gain a deeper understanding of 3CLpro structure and function in order to effectively target this enzyme. All-atoms molecular dynamics (MD) simulations were performed to examine the different conformational behaviors of the monomeric and dimeric form of SARS-CoV-2 3CLpro apo structure, as revealed by microsecond time scale MD simulations. Our results also shed light on the conformational dynamics of the loop regions at the entry of the catalytic site. Studying, at atomic level, the characteristics of the active site and obtaining information on how the protein can interact with its substrates will allow the design of molecules able to block the enzymatic function crucial for the virus.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the pandemic that broke out in 2020 and continues to be the cause of massive global upheaval. Coronaviruses are positive-strand RNA viruses with a genome of ~30 kb. The genome is replicated and transcribed by RNA-dependent RNA polymerase together with accessory factors. One of the latter is the protein helicase (NSP13), which is essential for viral replication. The recently solved helicase structure revealed a tertiary structure composed of five domains. Here, we investigated NSP13 from a structural point of view, comparing its RNA-free form with the RNA-engaged form by using atomistic molecular dynamics (MD) simulations at the microsecond timescale. Structural analyses revealed conformational changes that provide insights into the contribution of the different domains, identifying the residues responsible for domain–domain interactions in both observed forms. The RNA-free system appears to be more flexible than the RNA-engaged form. This result underlies the stabilizing role of the nucleic acid and the functional core role of these domains.

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.

Large-scale Virtual Screening (VS) campaigns of compound libraries can significantly speed up candidate selection in the early stages of drug discovery. The most promising drug candidates are identified by Scoring Functions (SFs), which enable VS campaigns to rank candidate compounds according to their estimated binding affinities. These SFs are typically trained on experimental data reflecting binding affinities (e.g., Dissociation Constant (K_d) values), commonly used as proxies for protein-ligand binding free energies. Because experimental reference data are often unavailable or collected using inconsistent techniques and/or procedures between laboratories, we developed two computational workflows that generate configurational ensembles of soluble protein-ligand complexes with Molecular Dynamics (MD) and compute the Absolute Binding Free Energies (ABFEs) of the sampled ligand binding poses with implicit-solvent calculations. The resulting consistent large-scale datasets of ABFEs address two complementary aspects of virtual screening: quantitative binding affinity estimation and binding pose assessment. Our Binding Affinity Prediction (BAP) workflow estimated protein-ligand binding affinities for 4000+ complexes from the PDBbind 2020 dataset. Our Pose Selector (PS) workflow computed non-convergence ABFEs from short Molecular Dynamics (MD) simulations, estimating the stability of 800,000+ related binding poses. To produce ABFE data at this scale, our free-energy workflows classify, check, and repair input structures of protein-ligand complexes in a fully automated fashion. The workflow scripts, molecular dynamics data, and ABFE labels are publicly available, creating an extendable database of reference values for the development of Scoring Functions for Large-Scale Virtual Screening campaigns.