Abstract
Accumulation of somatic hypermutation (SHM) is the primary mechanism to enhance the binding affinity of antibodies to antigens in vivo. However, the structural basis of the effects of many SHMs remains elusive. Here, we integrated atomistic molecular dynamics (MD) simulation and data mining to build a high-throughput structural bioinformatics pipeline to study the effects of individual and combination SHMs on antibody conformation, flexibility, stability, and affinity. By applying this pipeline, we characterized a common mechanism of modulation of heavy-light pairing orientation by frequent SHMs at framework positions 39H, 91H, 38L, and 87L through disruption of a conserved hydrogen-bond network. Q39LH alone and in combination with light chain framework 4 (FWR4L) insertions further modulated the elbow angle between variable and constant domains of many antibodies, resulting in improved binding affinity for a subset of anti-HIV-1 antibodies. Q39LH also alleviated aggregation induced by FWR4L insertion, suggesting remote epistasis between these SHMs. Altogether, this study provides tools and insights for understanding antibody affinity maturation and for engineering functionally improved antibodies.
Highlights
The affinity maturation process of antibodies or B cell receptors (BCRs) constitutes a microevolution system for antibody improvement [1]
To predict the effect of somatic hypermutation (SHM) on antibody conformation, we developed a script (MD.pl) to perform multiple steps of molecular dynamics (MD) simulation from energy minimization, heating, equilibration, to production using Amber18 (Details see Materials and Methods) (Figure 1A)
In the gene-specific substitution profiles (GSSPs), which predict positional SHM preference, we found that many residue positions at the VH-VL and elbow interfaces mutate with high frequencies and strong substitution preference (Figures S1F–I), implying that SHMs at these positions are frequently used to modulate VH-VL and elbow conformations
Summary
The affinity maturation process of antibodies or B cell receptors (BCRs) constitutes a microevolution system for antibody improvement [1]. Multiple types of somatic hypermutations (SHMs) (point mutations, insertions and deletions (indels), and sites for post-translational modifications) are incorporated in the BCR variable domain [2, 3]. Beneficial SHMs are selected iteratively to optimize the properties of BCRs including antigen-binding affinity. FWR SHMs Alter Antibody Conformation as well as the accommodation of antigen variability, flexibility, and physical stability [4,5,6,7,8,9,10]. In previous studies [11, 12], we built gene-specific substitution profiles (GSSPs) to describe genespecific hotspots and preferences of point SHMs. We found that SHMs are generated with strong preferences resulting in frequent or dominant convergent mutations, which are commonly observed amongst different antibody lineages [12, 13]. As mapping the development of functionally important antibody lineages has become commonplace, the need for a “dictionary” to interpret these developmental maps has become clear
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