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

Area of Science:

• Virology
• Immunology
• Structural Biology

Background:

• Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes COVID-19, a pandemic with significant global health and economic impacts.
• Broadly neutralizing antibodies are crucial for preventing and treating COVID-19.
• Understanding virus-host interactions, particularly antibody binding sites, is key to therapeutic development.

Purpose of the Study:

• To identify unique anti-SARS-CoV-2 motifs through in silico screening.
• To create a classified database of SARS-CoV-2 ligands and spike glycoprotein structures.
• To reveal specific binding motifs in the receptor-binding domain (RBD) for antibody development.

Main Methods:

• In silico ligand-based screening of the Protein Data Bank (PDB).
• Creation and categorization of a SARS-CoV-2 Ligands Database with 1797 entries.
• Classification of antibodies based on their location on the RBD to identify binding motifs.

Main Results:

• A comprehensive database of SARS-CoV-2 spike glycoprotein structures and ligands was established.
• Specific tyrosine-containing motifs were identified within the RBD antibody binding sites.
• Antibodies were classified into four distinct classes based on RBD binding location, revealing unique motifs for each.

Conclusions:

• The identified motifs can accelerate the development of novel antibody-based therapies for COVID-19.
• Findings provide guidance for the rational design of next-generation SARS-CoV-2 vaccines.
• The SARS-CoV-2 Ligands Database serves as a valuable resource for researchers.