Mining antibody functionality via AI-guided structural landscape profiling.
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Terekhov Stanislav S, Ivanisenko Nikita V, Zhang Nan, Mokrushina Yuliana A, Nolde Dmitry E, Lomakin Yakov A, Zalevsky Arthur, Ovchinnikova Leyla A, Malabuiok Diana M, Baranova Margarita N, Shashkova Tatiana, Aliper Elena, Zhang Mingxiu, Guo Kun, Duga Sergey, Akhmetyanov Nikolay, Mamontov Stepan, Smirnova Anastasia O, Mamedov Ilgar, Bobik Tatiana V, Kostin Nikita N, Chernov Aleksandr S, Eliseev Igor E, Yaroshevich Igor, Boitsov Vitali M, Stepanov Alexey V, Zhang Ding, Efremov Roman G, Smirnov Ivan V, Kardymon Olga, Zhang Hongkai, Guo Yu, Lerner Richard, Gabibov Alexander G, Kornberg Roger D
Аннотация
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.