Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico

Abstract

Since the emergence of SARS-CoV-2, the ongoing arms race between mutating viruses and human antibodies has revealed several novel strategies by which antibodies adapt to viral escape. While SARS-CoV-2 viruses exhibit high variability in epitopes targeted by neutralizing antibodies, certain epitopes remain conserved owing to their essential roles on viral fitness. Antibodies can acquire broadly neutralizing activity by targeting these vulnerable sites through affinity-based somatic evolution of immunoglobulin genes. Notably, the specificity encoded in antibody germline genes also plays a fundamental role in acquiring the breadth. In-depth genetic and structural analyses of the antibody repertoires have uncovered multiple strategies for adapting to evolving targets. The integration of large-scale antibody datasets with computational approaches increases the feasibility and efficiency of designing broadly neutralizing antibody therapeutics from ancestral antibody clones with limited initial efficacy. In this review, we discuss strategies to optimize antibody breadth for the development of broadly neutralizing antibody therapeutics and vaccine antigens.

Keywords: B-cell; SARS-CoV-2; antibody; computational design; germline gene; repertoire; vaccine.

Figure 1.

Antibody generation in vivo and in silico. Antibody repertoires can be generated either in silico or in vivo, although the former approach is still in its early stages. In the fight against SARS-CoV-2, the ideal antibodies have broadly neutralizing activities capable of targeting multiple viral variants.

Figure 2.

Strategies for the immune escape of viral proteins. (a) Physicochemical changes observed in SARS-CoV-2 RBD, including reductions in geometric, electrostatic, and hydropathic complementarity. (b) Evolution of surface hydrophobicity in SARS-CoV-2 RBD. The ACE2-binding site is centered in each panel. Hydrophobicity was calculated by the spatial aggregation propensity metric.

Figure 3.

Computer-guided B-cell repertoire mining and antibody design. Computational approaches can identify potent antibodies from deep sequenced B-cell repertoires and also refine existing antibodies. Structure-based design that starts from an antibody-antigen complex is ideal, but even an antibody sequence alone can be optimized with sequence-based machine learning methods.