Author: Gulotta, Maria Rita; Lombino, Jessica; Perricone, Ugo; De Simone, Giada; Mekni, Nedra; De Rosa, Maria; Diana, Patrizia; Padova, Alessandro
Title: Targeting SARSâ€CoVâ€2 RBD interface: a supervised computational dataâ€driven approach to identify potential modulators Cord-id: 6ymt01xo Document date: 2020_7_23
ID: 6ymt01xo
Snippet: Coronavirus Disease 2019 (COVIDâ€19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARSâ€CoVâ€2 Spike (S) glycoprotein to host Angiotensinâ€converting enzyme 2 (ACE2). The interaction is mediated by the receptorâ€binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a comp
Document: Coronavirus Disease 2019 (COVIDâ€19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARSâ€CoVâ€2 Spike (S) glycoprotein to host Angiotensinâ€converting enzyme 2 (ACE2). The interaction is mediated by the receptorâ€binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARSâ€CoVâ€2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the biâ€molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.
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