Beyond the Surface: Rethinking Protein Design from the Core Out

A landmark study redefines protein engineering, revealing robust cores and unlocking vast new possibilities for AI-driven biological design.

Ailurus Press
September 8, 2025
5 min read

Protein engineering holds the key to solving some of humanity's most pressing challenges, from developing novel therapeutics and vaccines to creating sustainable industrial enzymes. Yet, for decades, progress has been constrained by a central dogma: that a protein's core—its densely packed hydrophobic interior—is a fragile, immutable structure. This "house of cards" model suggested that even minor changes to the core would lead to catastrophic misfolding, forcing engineers to focus on conservative, surface-level modifications. A recent study is now dismantling this long-held belief, revealing a far more robust and programmable reality.

The Path to a New Paradigm

The journey of protein engineering has been one of incrementally expanding our ability to modify function. Early methods like directed evolution allowed scientists to select for desired traits but offered little insight into the underlying design principles. A significant conceptual leap came from work identifying "sectors"—networks of co-evolving amino acids that connect distant sites to a protein's active site, often mediating allosteric communication [2]. This revealed that function could be tuned from afar, but the core itself remained largely off-limits, a black box approached with extreme caution. The prevailing strategy remained one of making small, careful edits, a paradigm that inherently limited the scope of innovation [4]. The field needed a systematic way to test the core's true tolerance for change, but the sheer scale of the combinatorial sequence space made this experimentally prohibitive—until now.

A Breakthrough from the Core: Deconstructing the "House of Cards"

A landmark paper in Science by Escobedo et al., "Genetics, energetics, and allostery in proteins with randomized cores and surfaces," provides the first large-scale, systematic interrogation of this forbidden territory [1]. The research team directly confronted the "fragile core" hypothesis by taking a small, well-understood protein domain (the human FYN-SH3 domain) and performing a radical experiment: they systematically randomized its core and surface sequences and synthesized tens of thousands of variants. Using a high-throughput assay, they then measured which of these massively altered sequences could still fold into a stable, functional protein.

The results were stunning and counterintuitive. They found that a vast number of different amino acid combinations could successfully form a stable protein core. Instead of a fragile house of cards, the protein structure behaved more like a set of Lego bricks, where many different pieces could fit together to build a stable whole. This finding alone fundamentally alters our understanding of protein stability.

Even more profoundly, the study revealed why many of these stable variants were non-functional. It wasn't because of structural collapse, as previously assumed. Instead, the changes in the core disrupted the protein's function through subtle, long-range allosteric effects—indirectly altering the shape or dynamics of the distant ligand-binding site. This decouples the problem of stability from the problem of function, showing that allostery, not instability, is the primary constraint on the evolution of protein cores.

To validate this, the researchers developed simple, additive energy models based on their experimental data. Remarkably, these models, trained on a single protein, could accurately predict the stability of natural SH3 domains from organisms separated by over a billion years of evolution [1, 3]. This suggests that the fundamental "rules" of protein folding are far simpler and more universal than previously imagined.

The Future is Designed, Not Just Discovered

The implications of this work are transformative, signaling a paradigm shift in biological engineering.

  1. Expanding the Design Universe: By demonstrating the core's robustness, this research unlocks a vast, previously inaccessible sequence space for protein design. Engineers are no longer restricted to tinkering at the surface; they can now rationally design proteins from the core out, opening the door to entirely new functions and properties. This is critical for creating next-generation therapeutics that are not only effective but also designed to avoid immune responses.
  2. A New Blueprint for AI-Driven Biology: The study's methodology—combining massive parallel synthesis with high-throughput functional screening—generates the exact kind of large, structured datasets that fuel modern AI and machine learning models [5]. By revealing the simple energetic rules governing stability, it provides a powerful validation for computational approaches and paves the way for AI models that can design novel proteins with high predictive accuracy.
  3. From Incremental Edits to Scalable Engineering: The conservative, one-by-one approach to protein engineering is now poised to be replaced by a massively parallel, data-driven cycle of design, build, and test. This shift requires new tools capable of handling immense complexity and scale. To explore this vast new design space, the field will increasingly rely on platforms that enable the autonomous screening of massive libraries and AI-native DNA design services to translate complex computational possibilities into wet-lab realities.

While future work will need to explore how these principles scale to larger, multi-domain proteins and complex assemblies [6], the path forward is clear. We are moving from an era of discovering what nature has evolved to an era of designing what biology can become. By revealing the simple, elegant rules hidden within the complexity of the protein core, this research has not just added a new chapter to the textbook—it has given us a new language to write the future of biology.


References

  1. Escobedo, A., Voigt, C. A., Faure, A., & Lehner, B. (2025). Genetics, energetics, and allostery in proteins with randomized cores and surfaces. Science. https://www.science.org/doi/10.1126/science.adq3948
  2. Raman, A. S., & Ranganathan, R. (2016). Origins of Allostery and Evolvability in Proteins: A Case Study. Cell. https://chemsysbio.stanford.edu/wp-content/uploads/2019/10/Raman_Ranganathan_main_Cell2016.pdf
  3. Escobedo, A., et al. (2024). Genetics, energetics and allostery during a billion years of hydrophobic protein core evolution. bioRxiv. https://www.biorxiv.org/content/10.1101/2024.05.11.593672v1
  4. Li, H., et al. (2020). Engineering Allostery into Proteins. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC7508002/
  5. Guarnera, E., & Berezovsky, I. N. (2019). The Evolving Landscape of Protein Allostery: From Computational and Experimental Methods to Allosteric Drugs and Systems-Level Properties. Pure. https://pure.psu.edu/en/publications/the-evolving-landscape-of-protein-allostery-from-computational-an
  6. Pillai, S., et al. (2024). De novo design of allosterically switchable protein assemblies. Nature. https://www.nature.com/articles/s41586-024-07813-2

About Ailurus

Ailurus Bio is a pioneering company building bioprograms, which are genetic codes that act as living software to instruct biology. We develop foundational DNAs and libraries to turn lab-grown cells into living instruments that streamline complex procedures in biological research and production. We offer these bioprograms to scientists and developers worldwide, empowering a diverse spectrum of scientific discovery and applications. Our mission is to make biology a general-purpose technology, as easy to use and accessible as modern computers, by constructing a biocomputer architecture for all.

For more information, visit: ailurus.bio
Share this post
Authors of this post
Ailurus Press
Subscribe to our latest news
We care about your data in our privacy policy.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form. Please contact us at support@ailurus.bio