Graduate Student Scripps Research Skaggs Graduate School of Chemical and Biological Sciences Yorba Linda, California, United States
Disclosure(s):
Morgan M. Gee, BS, MS: No financial relationships to disclose
Introduction/Rationale: Epitope:paratope interactions define the breadth and affinity an antibody has towards an antigen. Computational structure prediction can help to model immune complexes as a method of in silico screening for broadly neutralizing antibodies (bnAbs). However, we do not currently understand how structural predictions and confidence metrics correlate with experimental data. Here, we evaluate the accuracy of AlphaFold 3 (AF3) in predicting immune complexes of a known class of bnAbs towards a conserved epitope on influenza hemagglutinin (HA).
Methods: We used single-cell RNA sequencing to generate a library of HA reactive, paired antibody sequences. AF3 was used to computationally model antibody-HA interactions. Antibodies were expressed and characterized with BLI, ELISA, and microneutralization assays. Negative stain electron microscopy (nsEM) and single particle cryo-electron microscopy (SPA cryo-EM) were used to structurally characterize immune complexes.
Results: B cell receptor sequencing of PBMCs from 23 healthy, human donors generated 1161 H5 HA-specific, paired antibody sequences. These sequences comprised new examples within a known class of antibodies (VH1-69, of which previous examples are in the AF3 training set) likely to target the conserved central stem. We modeled these antibody sequences in complex with H1N1 and H5N1 HAs using AF3. Highly confident predictions correlated with antibodies possessing high affinity and breadth. Moreover, in silico epitope footprints strongly matched experimental nsEM data. Finally, we resolved atomistic details with high-resolution SPA cryo-EM that explain challenges with AF3 immune complex prediction.
Conclusion: AF3 can be used to screen for VH1-69 bnAbs targeting a conserved epitope on influenza HA with high confidence. This method of down selection may be applied to other classes of antibodies and to other epitopes. However, models still need to be tuned to accurately predict immune complexes of novel or less characterized antibody classes.
