A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

Synergistic inhibition of SARS-CoV-2 cell entry by otamixaban and covalent protease inhibitors: pre-clinical assessment of pharmacological and molecular properties.

SARS-CoV-2, the cause of the COVID-19 pandemic, exploits host cell proteins for viral entry into human lung cells. One of them, the protease TMPRSS2, is required to activate the viral spike protein (S). Even though two inhibitors, camostat and nafamostat, are known to inhibit TMPRSS2 and block cell entry of SARS-CoV-2, finding further potent therapeutic options is still an important task. In this study, we report that a late-stage drug candidate, otamixaban, inhibits SARS-CoV-2 cell entry. We show that otamixaban suppresses TMPRSS2 activity and SARS-CoV-2 infection of a human lung cell line, although with lower potency than camostat or nafamostat. In contrast, otamixaban inhibits SARS-CoV-2 infection of precision cut lung slices with the same potency as camostat. Furthermore, we report that otamixaban's potency can be significantly enhanced by (sub-) nanomolar nafamostat or camostat supplementation. Dominant molecular TMPRSS2-otamixaban interactions are assessed by extensive 109 µs of atomistic molecular dynamics simulations. Our findings suggest that combinations of otamixaban with supplemental camostat or nafamostat are a promising option for the treatment of COVID-19.

Alpha 1 Antitrypsin is an Inhibitor of the SARS-CoV-2-Priming Protease TMPRSS2.

BACKGROUND: Host proteases have been suggested to be crucial for dissemination of MERS, SARS-CoV, and SARS-CoV-2 coronaviruses, but the relative contribution of membrane versus intracellular proteases remains controversial. Transmembrane serine protease 2 (TMPRSS2) is regarded as one of the main proteases implicated in the coronavirus S protein priming, an important step for binding of the S protein to the angiotensin-converting enzyme 2 (ACE2) receptor before cell entry. METHODS: We developed a cell-based assay to identify TMPRSS2 inhibitors. Inhibitory activity was established in SARS-CoV-2 viral load systems. RESULTS: We identified the human extracellular serine protease inhibitor (serpin) alpha 1 anti-trypsin (A1AT) as a novel TMPRSS2 inhibitor. Structural modeling revealed that A1AT docked to an extracellular domain of TMPRSS2 in a conformation that is suitable for catalysis, resembling similar serine protease inhibitor complexes. Inhibitory activity of A1AT was established in a SARS-CoV-2 viral load system. Notably, plasma A1AT levels were associated with COVID-19 disease severity. CONCLUSIONS: Our data support the key role of extracellular serine proteases in SARS CoV-2 infections and indicate that treatment with serpins, particularly the FDA-approved drug A1AT, may be effective in limiting SARS-CoV-2 dissemination by affecting the surface of the host cells.

Alpha 1 Antitrypsin is an Inhibitor of the SARS-CoV-2-Priming Protease TMPRSS2.

Host proteases have been suggested to be crucial for dissemination of MERS, SARS-CoV, and SARS-CoV-2 coronaviruses, but the relative contribution of membrane versus intracellular proteases remains controversial. Transmembrane serine protease 2 (TMPRSS2) is regarded as one of the main proteases implicated in the coronavirus S protein priming, an important step for binding of the S protein to the angiotensin-converting enzyme 2 (ACE2) receptor before cell entry. The main cellular location where the SARS-CoV-2 S protein priming occurs remains debatable, therefore hampering the development of targeted treatments. Herein, we identified the human extracellular serine protease inhibitor (serpin) alpha 1 antitrypsin (A1AT) as a novel TMPRSS2 inhibitor. Structural modeling revealed that A1AT docked to an extracellular domain of TMPRSS2 in a conformation that is suitable for catalysis, resembling similar serine protease-inhibitor complexes. Inhibitory activity of A1AT was established in a SARS-CoV-2 viral load system. Notably, plasma A1AT levels were associated with COVID-19 disease severity. Our data support the key role of extracellular serine proteases in SARS-CoV-2 infections and indicate that treatment with serpins, particularly the FDA-approved drug A1AT, may be effective in limiting SARS-CoV-2 dissemination by affecting the surface of the host cells. SUMMARY: Delivery of extracellular serine protease inhibitors (serpins) such as A1AT has the capacity to reduce SARS-CoV-2 dissemination by binding and inhibiting extracellular proteases on the host cells, thus, inhibiting the first step in SARS-CoV-2 cell cycle (i.e. cell entry).

PRODIGY-crystal: a web-tool for classification of biological interfaces in protein complexes.

SUMMARY: Distinguishing biologically relevant interfaces from crystallographic ones in biological complexes is fundamental in order to associate cellular functions to the correct macromolecular assemblies. Recently, we described a detailed study reporting the differences in the type of intermolecular residue-residue contacts between biological and crystallographic interfaces. Our findings allowed us to develop a fast predictor of biological interfaces reaching an accuracy of 0.92 and competitive to the current state of the art. Here we present its web-server implementation, PRODIGY-CRYSTAL, aimed at the classification of biological and crystallographic interfaces. PRODIGY-CRYSTAL has the advantage of being fast, accurate and simple. This, together with its user-friendly interface and user support forum, ensures its broad accessibility. AVAILABILITY AND IMPLEMENTATION: PRODIGY-CRYSTAL is freely available without registration requirements at https://haddock.science.uu.nl/services/PRODIGY-CRYSTAL.

Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification.

BACKGROUND: Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems. RESULTS: In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on "pair-properties" of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA). CONCLUSION: In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier .