The transmembrane serine protease TMPRSS2 is indispensable for S protein priming of the MERS, SARS-CoV, and SARS-CoV2 coronaviruses, a process that is necessary for entry of the virus into host cells. Therefore, inhibiting TMPRSS2 holds promise as an approach toward preventing transmission of coronaviruses. Herein, we developed an in vitro system to measure TMPRSS2 activity and tested the inhibition of TMPRSS2 by several synthetic and natural protease inhibitors. Camostat mesylate and bromhexine hydrochloride (BHH) inhibited TMPRSS2 proteolytic function. In addition, we identified the small molecule 4-(2-aminomethyl)benzenesulfonyl fluoride (AEBSF) and the human, anti-inflammatory protein alpha 1 antitrypsin (A1AT) as inhibitors of TMPRSS2. AEBSF and A1AT inhibited TMPRSS2 activity in a dose-dependent manner. AEBSF and A1AT inhibited TMPRSS2 in the same range of concentrations (100-0.1 μM). We suggest that treatment with these inhibitors, particularly A1AT, which is an FDA-approved drug, might be effective in limiting SARS-CoV and SARS-CoV2 transmissibility and as a COVID-19 treatment.

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 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.  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.

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.

BackgroundStudy 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.ResultsIn 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).ConclusionIn 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.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2414-9) contains supplementary material, which is available to authorized users.