Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis
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Abstract
Protein evolution is constrained by multidimensional biophysical factors, in which mutations that enhance one property often compromise another. Antibodies represent an extreme case: they evolve rapidly to bind diverse antigens, yet mutations that improve affinity can disrupt folding, reduce cell-surface trafficking, or promote self-reactivity, and are typically selected against during affinity maturation. Though biophysical characterization of individual antibodies suggests that such trade-offs are pervasive, their impact on antibody evolutionary trajectories remains unclear, in part because existing high-throughput biophysical methods rely on heterologous systems that are often poorly suited for human proteins. Here, we develop a high-throughput platform to quantify multiple biophysical parameters of large libraries of full-length proteins that are natively synthesized, processed, and displayed on human cells. We apply this approach to a human antibody lineage that matures to recognize divergent SARS-CoV-2 variants by measuring the surface expression, antigen affinity, and self-reactivity for all 2^13 possible evolutionary intermediates between the unmutated and mature sequences. These measurements reveal that mutations differentially affect these biophysical properties - in some cases, improving one property at the expense of another. We leverage these data to compute the likelihood of all possible evolutionary paths, finding that very few paths can navigate these multidimensional requirements. The few accessible paths acquire mutations in a specific order that either circumvent trade-offs between biophysical properties or offset deleterious effects on one property with beneficial effects on another. By determining the structures of the ancestral and evolved antibodies, we find that these coordinated mutational effects arise from a conformational rearrangement that alleviates steric clashes and reshapes the biophysical landscape, enabling otherwise inaccessible mutational paths. Together, this work defines the multidimensional biophysical constraints and structural mechanisms that govern antibody evolution and establishes a general framework for mapping and predicting the biophysical effects of mutations in human proteins.
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Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis
This preprint gives a quantitative view of how multidimensional biophysical constraints shape antibody evolution. It highlights both the limitations of standard single-mutant deep mutational scanning. This approach is powerful for mapping marginal effects, but it can’t directly capture epistasis. The fully combinatorial DMS across all 13 mutations enables direct interrogation of epistatic interactions and trajectory structure. Integrating affinity, expression, and polyspecificity across multiple antigen contexts into a single framework makes it possible to map how a relatively small subset of mutational trajectories remains accessible under these combined constraints.
I’ve been thinking about how much the observed path constraint arises from …
Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis
This preprint gives a quantitative view of how multidimensional biophysical constraints shape antibody evolution. It highlights both the limitations of standard single-mutant deep mutational scanning. This approach is powerful for mapping marginal effects, but it can’t directly capture epistasis. The fully combinatorial DMS across all 13 mutations enables direct interrogation of epistatic interactions and trajectory structure. Integrating affinity, expression, and polyspecificity across multiple antigen contexts into a single framework makes it possible to map how a relatively small subset of mutational trajectories remains accessible under these combined constraints.
I’ve been thinking about how much the observed path constraint arises from the underlying biology versus the restriction to the 13 mutations present in the mature antibody. By restricting analysis to the 13 mutations present in the mature antibody and modeling evolution as monotonic accumulation, the system is being presented like a directed acyclic graph (DAG), where mutations are only added and all trajectories remain within a fixed subspace.
In vivo, antibody maturation may resemble a non-linear branching population process rather than a single linear trajectory. SHM may generate destabilizing mutations that are tolerated or even enabled by compensatory interactions, allowing exploration of a broader sequence space. In that context, trajectories could transiently move outside the final mutational set; mutations could arise that stabilize otherwise unfavorable intermediates, and later revert once more favorable combinations are reached. These kinds of transient “scaffolding” mutations, occurring outside the final set of 13, would not be captured in a strictly monotonic accumulation framework.
The cryo-EM results suggest that many of the strongest epistatic effects are coming from conformational changes rather than direct residue-residue interaction. That makes me think the epistasis here is really about shifting populations between different structural states, rather than specific pairwise interactions. In that sense, what looks like a discrete mutational landscape might actually be reflecting underlying continuous changes in conformation that aren’t directly modeled. However, germinal center selection is strong and iterative, and mutation rates can decrease as affinity improves, which probably limits how far trajectories actually wander. So while these kinds of excursions seem possible, it’s not clear how often they actually persist during antibody maturation or evolution more broadly.
Given that framing, I was curious how you think about a few points:
To what extent do the path constraints you observe depend on representing evolution as a DAG within the final mutational set? Do you have a sense for how allowing excursions outside this subspace or including reversions might change the accessibility landscape?
Do you think transient mutations play a role in antibody maturation and open up otherwise inaccessible paths, or does this sort of evolution mostly stay within a relatively small set of mutations?
How well do you think antibody maturation models broader protein evolution? Do you see the degree of path constraint you observe as a general property of protein evolution under biophysical trade-offs, or something shaped by the specific dynamics of germinal centers?
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