Computational and experimental validation of phthalocyanine and hypericin as effective SARS-CoV-2 fusion inhibitors

Abstract

Phthalocyanine and hypericin have been previously identified as possible SARS-CoV-2 Spike glycoprotein fusion inhibitors through a virtual screening procedure. In this paper, atomistic simulations of metal-free phthalocyanines and atomistic and coarse-grained simulations of hypericins, placed around a complete model of the Spike embedded in a viral membrane, allowed to further explore their multi-target inhibitory potential, uncovering their binding to key protein functional regions and their propensity to insert in the membrane. Following computational results, pre-treatment of a pseudovirus expressing the SARS-CoV-2 Spike protein with low compounds concentrations resulted in a strong inhibition of its entry into cells, suggesting the activity of these molecules should involve the direct targeting of the viral envelope surface. The combination of computational and in vitro results hence supports the role of hypericin and phthalocyanine as promising SARS-CoV-2 entry inhibitors, further endorsed by literature reporting the efficacy of these compounds in inhibiting SARS-CoV-2 activity and in treating hospitalized COVID-19 patients.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• hypericin
• phthalocyanine
• fusion inhibitors
• molecular simulations
• pseudovirus antiviral assay

Data availability statement

Data will be made available on request.

Author contributions

A.R. and G.C.: Methodology, Formal analysis, Investigation, Writing-Original Draft, Writing-Review and Editing; F.I.: Methodology, Investigation, Writing-Review and Editing; V.C.: Writing-Review and Editing, Supervision; M.F.: Conceptualization, Methodology, Formal analysis, Data Curation, Writing-Original Draft, Writing-Review and Editing, Supervision.

Acknowledgements

The authors thank Prof. S.J. Marrink, Dr. T.A. Wassenaar and the MD group of the University of Groningen for hosting part of this research and for their help in implementing the CG systems.

The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff (Iannone et al., 2019). CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes, see http://www.cresco.enea.it/english for information.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work was supported by the National Research Center for HP C, Big Data and Quantum Computing, within the CN1 spoke 6 “Multiscale Modelling and Engineering Applications”.