OptMAVEn-2.0

OptMAVEn-2.0 performs de novo design of humanized monoclonal antibody variable regions to generate and rank sequences that target specified antigen epitopes with high specificity and affinity.

Key Features:

• De novo antibody variable-region design: Generates humanized monoclonal antibody variable regions targeting specific antigen epitopes through computational design.
• Enhanced Computational Efficiency: Requires on average 74% less CPU time and 84% less disk storage compared to OptMAVEn while maintaining design quality.
• Scalability: Computational requirements scale sub-linearly with antigen size.
• Clustering and high-affinity identification: Incorporates clustering algorithms to identify and prioritize high-affinity antibody designs.
• Steric clash elimination using MAPs: Uses an updated set of clashing parts from the Modular Antibody Parts (MAPs) database to detect and eliminate intra-antibody steric clashes.
• Benchmarking: Validated on a diverse set of antigens, including applications to Zika envelope protein and hen egg white lysozyme epitopes.

Scientific Applications:

• Zika envelope protein epitope design: Applied to design and rank variable antibody fragments targeting epitopes from the Zika envelope protein.
• Hen egg white lysozyme epitope design: Applied to design and rank variable antibody fragments targeting epitopes from hen egg white lysozyme.
• Therapeutic antibody discovery: Supports research-focused discovery of monoclonal antibodies for cancer, infectious diseases, and autoimmune disorders.

Methodology:

Performs de novo computational design and ranking of antibody variable regions, applies clustering algorithms to prioritize high-affinity candidates, and uses the Modular Antibody Parts (MAPs) database to identify and eliminate intra-antibody steric clashes; designs were benchmarked on diverse antigens.