In Silico Ligand-Based Screening of PDB Database for Searching Unique Motifs Against SARS-CoV-2

SARS-CoV-2, the virus responsible for coronavirus disease COVID-19, is a highly transmissible pathogen that has caused substantial global morbidity and mortality. The ongoing COVID-19 pandemic caused by this virus has had a significant impact on public health and the global economy. One approach to combating COVID-19 is the development of broadly neutralizing antibodies for prevention and treatment. In this work, we performed an in silico ligand-based screening of the PDB database to search for unique anti-SARS-CoV-2 motifs. The collected data were organized and presented in a classified SARS-CoV-2 Ligands Database, categorized based on the number of ligands and structural components of the spike glycoprotein. The database contains 1797 entries related to the structures of the spike glycoprotein (UniProt ID: P0DTC2), including both full-length molecules and their fragments (individual domains and their combinations) with various ligands, such as angiotensin-converting enzyme II and antibodies. The database's capabilities allow users to explore various datasets according to the research objectives. To search for motifs in the receptor-binding domain (RBD) most frequently involved in antibody binding sites, antibodies were classified into four classes according to their location on the RBD; for each class, special binding motifs are revealed. In the RBD binding sites, specific tyrosine-containing motifs were found. Data obtained may help speed up the creation of new antibody-based therapies, and guide the rational design of next-generation vaccines.