Mining antibody functionality via AI-guided structural landscape profiling

Despite substantial progress in single-cell screening techniques, antibody (Ab) repertoires still remain enigmatic. Here we show that Ab sequences can be linked to their functionality by using big data obtained from high-throughput sequencing. Using the expansive SARS-CoV-2 pandemic data, we develop an AI-based method to reveal the neutralization potential of Ab repertoires. We employ machine learning to process public 3D structural data of Ab-RBD complexes and create a comprehensive tool, RBD-AIM (https://rbdaim.2a2i.org/), for high-throughput prediction of structural Ab epitopes based on Ab sequence. Using RBD-AIM, we analyze the local big data sources to evaluate the functional biodiversity of native B cell repertoires raised after vaccination and reconstructed in a yeast display system using single-cell microfluidics. This pipeline allows for rapid isolation of neutralizing Abs that promote the survival of transgenic hACE2+ mice in lethal models of SARS-CoV-2 infection. We believe that the AI-guided sequence-functionality link can be successfully employed for further high-throughput discovery of therapeutic Abs and functional analysis of Ab repertoires. Computational methods can be used to find potent neutralizing antibodies against viruses such as SARS-CoV-2. Here the authors use an AI method to landscape antibodies, predict specificity and antibody-antigen structure, pick out potent neutralizers and show that these antibodies are protective against SARS-CoV-2 challenge in mice models.