AI Promoted Virtual Screening, Structure-Based Hit Optimization, and Synthesis of Novel COVID-19 S-RBD Domain Inhibitors

Coronavirus disease 2019 (COVID-19) is caused by a new, highly pathogenic severe-acute-respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects human cells through its transmembrane spike (S) glycoprotein. The receptor-binding domain (RBD) of the S protein interacts with the angiotensin-converting enzyme II (ACE2) receptor of the host cells. Therefore, pharmacological targeting of this interaction might prevent infection or spread of the virus. Here, we performed a virtual screening to identify small molecules that block S-ACE2 interaction. Large compound libraries were filtered for drug-like properties, promiscuity and protein-protein interaction-targeting ability based on their ADME-Tox descriptors and also to exclude pan-assay interfering compounds. A properly designed AI-based virtual screening pipeline was applied to the remaining compounds, comprising approximately 10% of the starting data sets, searching for molecules that could bind to the RBD of the S protein. All molecules were sorted according to their screening score, grouped based on their structure and postfiltered for possible interaction patterns with the ACE2 receptor, yielding 31 hits. These hit molecules were further tested for their inhibitory effect on Spike RBD/ACE2 (19-615) interaction. Six compounds inhibited the S-ACE2 interaction in a dose-dependent manner while two of them also prevented infection of human cells from a pseudotyped virus whose entry is mediated by the S protein of SARS-CoV-2. Of the two compounds, the benzimidazole derivative <b>CKP-22</b> protected Vero E6 cells from infection with SARS-CoV-2, as well. Subsequent, hit-to-lead optimization of <b>CKP-22</b> was effected through the synthesis of 29 new derivatives of which compound <b>CKP-25</b> suppressed the Spike RBD/ACE2 (19-615) interaction, reduced the cytopathic effect of SARS-CoV-2 in Vero E6 cells (IC<sub>50</sub> = 3.5 μM) and reduced the viral load in cell culture supernatants. Early in vitro ADME-Tox studies showed that <b>CKP-25</b> does not possess biodegradation or liver metabolism issues, while isozyme-specific CYP450 experiments revealed that <b>CKP-25</b> was a weak inhibitor of the CYP450 system. Moreover, <b>CKP-25</b> does not elicit mutagenic effect on <i>Escherichia coli</i> WP2 uvrA strain. Thus, <b>CKP-25</b> is considered a lead compound against COVID-19 infection.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2024-11-13
DOI
https://doi.org/10.1021/acs.jcim.4c01110
Citations
7
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
2.53

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article

AI Promoted Virtual Screening, Structure-Based Hit Optimization, and Synthesis of Novel COVID-19 S-RBD Domain Inhibitors

Simona Kostova, Ioannis Karakasiliοtis, Erich E. Wanker, Vassilis G. Gorgoulis et al.
7 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
2.53
article

AI Promoted Virtual Screening, Structure-Based Hit Optimization, and Synthesis of Novel COVID-19 S-RBD Domain Inhibitors

Simona Kostova, Ioannis Karakasiliοtis, Erich E. Wanker, Vassilis G. Gorgoulis, Theodora Calogeropoulou, Maria Koufaki, Spyros Petrakis, Θεοδώρα Κάτσιλα, Απόστολος Αξενόπουλος, Stelios Mylonas, Demetris P. Papahatjis, Petros Daras, George Ε. Magoulas, Sotirios Katsamakas, Marios Dimitriou, Theano Fotopoulou, Ioannis Gkekas, Alia Cristina Tenchiu, Nafsika Rossopoulou
article en
7 citations

Abstract

Coronavirus disease 2019 (COVID-19) is caused by a new, highly pathogenic severe-acute-respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects human cells through its transmembrane spike (S) glycoprotein. The receptor-binding domain (RBD) of the S protein interacts with the angiotensin-converting enzyme II (ACE2) receptor of the host cells. Therefore, pharmacological targeting of this interaction might prevent infection or spread of the virus. Here, we performed a virtual screening to identify small molecules that block S-ACE2 interaction. Large compound libraries were filtered for drug-like properties, promiscuity and protein-protein interaction-targeting ability based on their ADME-Tox descriptors and also to exclude pan-assay interfering compounds. A properly designed AI-based virtual screening pipeline was applied to the remaining compounds, comprising approximately 10% of the starting data sets, searching for molecules that could bind to the RBD of the S protein. All molecules were sorted according to their screening score, grouped based on their structure and postfiltered for possible interaction patterns with the ACE2 receptor, yielding 31 hits. These hit molecules were further tested for their inhibitory effect on Spike RBD/ACE2 (19-615) interaction. Six compounds inhibited the S-ACE2 interaction in a dose-dependent manner while two of them also prevented infection of human cells from a pseudotyped virus whose entry is mediated by the S protein of SARS-CoV-2. Of the two compounds, the benzimidazole derivative <b>CKP-22</b> protected Vero E6 cells from infection with SARS-CoV-2, as well. Subsequent, hit-to-lead optimization of <b>CKP-22</b> was effected through the synthesis of 29 new derivatives of which compound <b>CKP-25</b> suppressed the Spike RBD/ACE2 (19-615) interaction, reduced the cytopathic effect of SARS-CoV-2 in Vero E6 cells (IC<sub>50</sub> = 3.5 μM) and reduced the viral load in cell culture supernatants. Early in vitro ADME-Tox studies showed that <b>CKP-25</b> does not possess biodegradation or liver metabolism issues, while isozyme-specific CYP450 experiments revealed that <b>CKP-25</b> was a weak inhibitor of the CYP450 system. Moreover, <b>CKP-25</b> does not elicit mutagenic effect on <i>Escherichia coli</i> WP2 uvrA strain. Thus, <b>CKP-25</b> is considered a lead compound against COVID-19 infection.

Journal of Chemical Information and ModelingVol. 64(22)
Democritus University of Thrace (GR), National and Kapodistrian University of Athens (GR), Max Delbrück Center (DE), Ninewells Hospital (GB), Information Technologies Institute (GR), National Hellenic Research Foundation (GR), Centre for Research and Technology Hellas (GR)
General Secretariat for Research and Technology, European Regional Development Fund
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
2.53
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