Essential oils have shown promise as antiviral agents against several pathogenic viruses. In this work we hypothesized that essential oil components may interact with key protein targets of the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A molecular docking analysis was carried out using 171 essential oil components with SARS-CoV-2 main protease (SARS-CoV-2 Mpro), SARS-CoV-2 endoribonucleoase (SARS-CoV-2 Nsp15/NendoU), SARS-CoV-2 ADP-ribose-1″-phosphatase (SARS-CoV-2 ADRP), SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp), the binding domain of the SARS-CoV-2 spike protein (SARS-CoV-2 rS), and human angiotensin−converting enzyme (hACE2). The compound with the best normalized docking score to SARS-CoV-2 Mpro was the sesquiterpene hydrocarbon (E)-β-farnesene. The best docking ligands for SARS−CoV Nsp15/NendoU were (E,E)-α-farnesene, (E)-β-farnesene, and (E,E)−farnesol. (E,E)−Farnesol showed the most exothermic docking to SARS-CoV-2 ADRP. Unfortunately, the docking energies of (E,E)−α-farnesene, (E)-β-farnesene, and (E,E)−farnesol with SARS-CoV-2 targets were relatively weak compared to docking energies with other proteins and are, therefore, unlikely to interact with the virus targets. However, essential oil components may act synergistically, essential oils may potentiate other antiviral agents, or they may provide some relief of COVID-19 symptoms.

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

We report consensus Structure-Activity Similarity (SAS) maps that address the dependence of activity landscapes on molecular representation. As a case study, we characterized the activity landscape of 54 compounds with activities against human cathepsin B (hCatB), human cathepsin L (hCatL), and Trypanosoma brucei cathepsin B (TbCatB). Starting from an initial set of 28 descriptors we selected ten representations that capture different aspects of the chemical structures. These included four 2D (MACCS keys, GpiDAPH3, pairwise, and radial fingerprints) and six 3D (4p and piDAPH4 fingerprints with each including three conformers) representations. Multiple conformers are used for the first time in consensus activity landscape modeling. The results emphasize the feasibility of identifying consensus data points that are consistently formed in different reference spaces generated with several fingerprint models, including multiple 3D conformers. Consensus data points are not meant to eliminate data, disregarding, for example, "true" activity cliffs that are not identified by some molecular representations. Instead, consensus models are designed to prioritize the SAR analysis of activity cliffs and other consistent regions in the activity landscape that are captured by several molecular representations. Systematic description of the SARs of two targets give rise to the identification of pairs of compounds located in the same region of the activity landscape of hCatL and TbCatB suggesting similar mechanisms of action for the pairs involved. We also explored the relationship between property similarity and activity similarity and found that property similarities are suitable to characterize SARs. We also introduce the concept of structure-property-activity (SPA) similarity in SAR studies.

Background: Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) [1,2]. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.

Mortality attributable to infection with methicillin-resistant Staphylococcus aureus (MRSA) has now overtaken the death rate for AIDS in the United States, and advances in research are urgently needed to address this challenge. We report the results of the systematic identification of protein-protein interactions for the hospital-acquired strain MRSA-252. Using a high-throughput pull-down strategy combined with quantitative proteomics to distinguish specific from nonspecific interactors, we identified 13,219 interactions involving 608 MRSA proteins. Consecutive analyses revealed that this protein interaction network (PIN) exhibits scale-free organization with the characteristic presence of highly connected hub proteins. When clinical and experimental antimicrobial targets were queried in the network, they were generally found to occupy peripheral positions in the PIN with relatively few interacting partners. In contrast, the hub proteins identified in this MRSA PIN that are essential for network integrity and stability have largely been overlooked as drug targets. Thus, this empirical MRSA-252 PIN provides a rich source for identifying critical proteins essential for network stability, many of which can be considered as prospective antimicrobial drug targets.