Epistasis

Antibodies show evidence of significant epistasis in biophysical properties but not binding

Diego del Alamo

17 Mar 2026 — 1 min read

Antibodies undergo somatic mutation during affinity maturation in ways that show evidence of epistasis in biophysical properties like expression and polyspecificity, but not binding ^[1]^[2]. In a study by Kirby et al 2025^[1:1] on 12 anti-SARS-CoV-2 antibodies using deep mutational scanning and yeast display, no combination of two point mutations showed greater than 30% difference in binding affinity compared to the sum of two individual mutations in isolation. Likewise, Tharp et al 2026 saw linear models explain 80-90% of single-antigen binding in their data. However, the latter saw that second-order models were necessary to model expression and polyspecificity.

References

Kirby, M. B., Petersen, B. M., Faris, J. G., Kells, S. P., Sprenger, K. G., & Whitehead, T. A. (2025). Retrospective SARS-CoV-2 human antibody development trajectories are largely sparse and permissive. Proceedings of the National Academy of Sciences, 122(4). https://doi.org/10.1073/pnas.2412787122 ↩︎ ↩︎
Tharp, C. R., Catalano, C., Khalifeh, A., Ghaffari-Kashani, S., Huang, R., Kang, G., Scapin, G., & Phillips, A. M. (2026). Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis. openRxiv. https://doi.org/10.64898/2026.03.12.711465 ↩︎

Flow matching and diffusion perform comparably on biomolecular structure prediction

Flow matching and diffusion perform comparably on biomolecular structure prediction[1]. This is, to my knowledge, the only head-to-head comparison of these two approaches for protein structure modeling or design. References 1. Gong, C., Chen, X., Zhang, Y., Song, Y., Zhou, H., & Xiao, W. (2025). Protenix-Mini: Efficient Structure Predictor

Not all high-fitness sequences have plausible evolutionary paths from lower-fitness starting points via sequential introduction of mutations

I first saw this precise idea articulated by Weinreich et al. in their aptly named "Darwinian evolution can follow only very few mutational paths to fitter proteins"[1]. Many mutation combinations don't have additive effects on every aspect governing protein fitness (expression, stability, function, etc.), and

Conformational entropy could still matter in miniprotein binder design

Antibody V-regions improve their affinity for targets by both creating more high-energy interactions and reducing the conformational entropy of their antigen-binding loops[1][2]. Entropy's importance in antibody-antigen affinity seems obvious, given that loop residues largely mediate binding[3][4]. But for de novo-designed miniprotein binders, which often

Glutamate- and lysine-rich designs are susceptible to expression failure resulting from adenosine-rich sequences

High rates of glutamate and lysine introduction are a staple of structure-based sequence design by ProteinMPNN and related models, regardless of who trains them[1]. Recently, an analysis of the Bits in Bio competition showed that high glutamate/lysine content is predictive of expression failure[2]: The authors traced this

References

Read more

Flow matching and diffusion perform comparably on biomolecular structure prediction

Not all high-fitness sequences have plausible evolutionary paths from lower-fitness starting points via sequential introduction of mutations

Conformational entropy could still matter in miniprotein binder design

Glutamate- and lysine-rich designs are susceptible to expression failure resulting from adenosine-rich sequences