Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization
Sisi Shan, Shitong Luo, Ziqing Yang, Junxian Hong, Yufeng Su, Fan Ding, Lili Fu, Chenyu Li, Peng Chen, Jianzhu Ma, Xuanling Shi, Qi Zhang, Bonnie Berger, Linqi Zhang, Jian Peng
Abstract
SignificanceSARS-CoV-2 continues to evolve through emerging variants, more frequently observed with higher transmissibility. Despite the wide application of vaccines and antibodies, the selection pressure on the Spike protein may lead to further evolution of variants that include mutations that can evade immune response. To catch up with the virus's evolution, we introduced a deep learning approach to redesign the complementarity-determining regions (CDRs) to target multiple virus variants and obtained an antibody that broadly neutralizes SARS-CoV-2 variants.
Topics & Concepts
Transmissibility (structural dynamics)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)NeutralizationVirologyAntibodyCoronavirus disease 2019 (COVID-19)Virus2019-20 coronavirus outbreakBiologyComputational biologyDirected Molecular EvolutionDirected evolutionPositive selectionImmune escapeImmune systemGeneticsMedicineGeneInfectious disease (medical specialty)MutantPathologyOutbreakVibration isolationDiseasePhysicsVibrationQuantum mechanicsSARS-CoV-2 and COVID-19 Researchvaccines and immunoinformatics approachesMonoclonal and Polyclonal Antibodies Research