Hydrogen bonds play an indispensable role in maintaining the intricate three-dimensional architecture of proteins, particularly within β-sheet domains where inter-strand interactions confer mechanical fortitude. The natural world provides exemplary models in domains such as the muscle protein titin, which endures repetitive mechanical stretching, and the structural protein silk fibroin, prized for its tensile strength. These proteins achieve their remarkable stability through shearing hydrogen bonds that act as molecular shock absorbers under force. Mimicking and amplifying this principle at the molecular level has posed a formidable challenge given the complexity of protein folding and stability landscapes.

Addressing this challenge, the research team developed an innovative computational framework that integrates artificial intelligence-driven design strategies with all-atom molecular dynamics (MD) simulations to optimize both protein structure and sequence. This dual-pronged approach, leveraging AI’s predictive power and the atomic-level fidelity of MD, enabled systematic exploration of protein architectures with a focus on maximizing backbone hydrogen bonding capacity across force-bearing β strands. Through iterative cycles of design and simulation, the team successfully expanded the number of backbone hydrogen bonds from a modest four in early prototypes to an astonishing 33 in their final constructs.

The resultant proteins demonstrated mechanical unfolding forces exceeding 1,000 piconewtons (pN), representing a stunning 400% increase in strength relative to natural titin immunoglobulin domains, which typically endure forces of approximately 250 pN. This extraordinary enhancement is a testament to the power of strategic hydrogen bond network maximization in reinforcing protein mechanical resilience. Moreover, these designer proteins maintained their structural integrity after exposure to thermal stress at 150°C, a temperature range that typically denatures most natural proteins. This thermal robustness opens entirely new avenues for applications where proteins must function reliably under harsh environmental conditions.

Remarkably, the molecular-level advancements translated directly into tangible improvements in bulk material properties. The team fabricated hydrogels from the superstable proteins, which exhibited exceptional thermal stability, retaining structural coherence and mechanical function after exposure to elevated temperatures that would denature conventional hydrogels. This demonstration highlights the potential utility of these proteins as building blocks in biomaterials science, particularly for environments requiring durability under mechanical stress and extreme heat.

The integration of AI-guided design with molecular dynamics simulations represents a scalable and efficient paradigm for protein engineering, moving beyond traditional trial-and-error methods. By systematically expanding hydrogen bond networks within strategic β strands, this method establishes a rational blueprint for enhancing protein stability from the ground up. This approach holds promise not only for fundamental studies of protein mechanics but also for designing customized protein systems tailored to withstand extreme environmental challenges, from industrial biocatalysts used in harsh chemical processes to biomaterials deployed in aerospace applications.

Beyond the impressive mechanical and thermal resilience, the design principles outlined in this work offer a valuable framework for understanding the key determinants of protein stability. By focusing on the orchestration of hydrogen bond topology and distribution within force-bearing motifs, researchers can dissect the subtle interplay between local interactions and global structural integrity. Such insights usher in an era where protein robustness can be fine-tuned with atomic precision, guided by predictive modeling and powerful computational tools.

This accomplishment also underscores the transformative role of artificial intelligence in biological engineering. By utilizing AI algorithms to generate and refine protein sequences that optimize hydrogen bonding networks, the researchers have pioneered a new frontier where machine-guided design converges with molecular biophysics. The all-atom MD simulations provide essential validation and mechanistic understanding, ensuring that computational predictions translate into experimentally realizable, mechanically robust proteins.

The success in producing proteins with unfolding forces surpassing 1,000 pN situates these constructs among the strongest engineered proteins reported to date. This benchmark invites a reevaluation of our understanding of the mechanical limits of protein structures and suggests exciting opportunities for creating molecular machines, biosensors, and structural biomaterials with unparalleled durability.

Given the demonstrated thermal stability, these proteins hold particular promise for applications demanding longevity and resilience at elevated temperatures, such as therapeutic enzymes functioning in fever-range physiological conditions, or biomaterials for sterilizable medical implants. The capacity to engineer proteins that maintain function post-exposure to 150°C extends well beyond natural protein capabilities and paves the way for bioengineering solutions tailored to industrial conditions previously considered too extreme.

From a materials science perspective, the thermally stable hydrogel formations illustrate the potential for these designer proteins as scaffolds in tissue engineering, drug delivery, and regenerative medicine. Their robustness suggests a new class of protein-based materials that combine mechanical strength with biocompatibility and thermal endurance, offering transformative utility across biotechnology sectors.

Looking forward, this approach can be generalized, offering a versatile platform to engineer proteins with customized stability profiles by targeting backbone hydrogen bond networks tailored to application-specific mechanical demands. Future developments may incorporate other stabilizing interactions such as salt bridges or covalent crosslinks, further enhancing the toolbox for protein design.

By bridging AI-driven sequence optimization with rigorous atomic simulations, this work clarifies the principles underpinning protein mechanostability and provides a roadmap for the rational design of superstable proteins. The implications span from fundamental biophysics to applied biomaterials, positioning these superstable proteins at the forefront of synthetic biology and protein engineering.

Crucially, this study exemplifies how computational innovation can accelerate the discovery and realization of novel protein functionalities that transcend natural limitations. In the expanding landscape of protein engineering, the ability to predictably enhance stability and strength heralds a future where proteins can be custom-designed as functional materials equipped to thrive even in the most demanding environments on Earth and beyond.

Overall, the computational design of these superstable proteins marks a landmark achievement with far-reaching ramifications. It empowers scientists to explore uncharted regions of the protein fitness landscape and challenges preconceived notions of protein fragility. As these engineered proteins enter further stages of characterization and application development, they are poised to revolutionize fields from mechanobiology to industrial biotechnology.

This fusion of AI-guided design with molecular-level insights offers a definitive example of how interdisciplinary innovation fuels breakthroughs in molecular engineering. By maximizing hydrogen bonding within β strands, the researchers have not only resurrected but vastly enhanced nature’s own solutions to protein stability, achieving feats of protein resilience once thought unattainable.

Subject of Research: Computational protein engineering to design superstable proteins with enhanced mechanical and thermal stability via maximized hydrogen bonding in β-sheet structures.

Article Title: Computational design of superstable proteins through maximized hydrogen bonding.

Article References:

Zheng, B., Lu, Z., Wang, S. et al. Computational design of superstable proteins through maximized hydrogen bonding.
Nat. Chem. (2025). https://doi.org/10.1038/s41557-025-01998-3

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41557-025-01998-3

The machine learning that powers such websites will scan millions of images on the internet, analyze them and assemble facets of them into fresh, but fake, images.

Now, University of Kansas researchers are working to use a similar machine-learning process to build new proteins designed to detect water pollutants. With a new three-year, $1.5 million grant from the National Science Foundation’s Molecular Foundations for Biotechnology program, a KU researcher will use machine learning to create “deep-fake” membrane beta-barrel proteins — a class of naturally successful biosensors — designed to detect polluting metal ions in water.

“These beta barrels are super useful because they can bring things across membranes,” said principal investigator Joanna Slusky, associate professor of molecular biosciences at KU. “Barrels make good enzymes — there are so many different things that barrels can do.”

Previous research on the tube-like beta barrels has altered their binding properties for a variety of tasks. However, much of this work was arduous and completed by hand, usually resulting with minor variations of a limited number of scaffolds, or barrel structures.

“In this case, we’re using machine learning to generate large numbers of barrels,” Slusky said. “But, how about if we can both generate barrels and have them be useful? We asked ourselves, ‘What’s a biotechnology application of barrels?’ Well, one would be metal sensors that could perhaps detect metal pollutants.”

Slusky and her co-principal investigators, professors Rachel Kolodny and Margarita Osadchy of Haifa University in Israel (along with KU postdoctoral fellow Daniel Montezano), will develop a new machine-learning process that generates beta-barrels with scaffolds similar to those found in nature, but with different sequences.

“There’s a website called ‘This X Does Not Exist,’” Slusky said. “If you go to that site, you see all these AI-generated things and people don’t really exist. But a computer made an image, for instance, of a cat. But that’s not really a cat — a computer took a bunch of pictures of cats and said, ‘OK, we can just sort of generate as many cat pictures as you want now, because we figured out what is a cat.’ We need to make something real so we see it more like generating a recipe.

“The question is, how to make computers generate a recipe for proteins.”

Beta barrels are well-suited to advancement through machine learning because “natural proteins are sort of a small blip in the number of possible sequences.”

If a computer algorithm can learn the essence of what makes a protein a protein, Slusky said, it will avoid generating useless sequences.

“Most sequences would never actually be proteins— they wouldn’t have a particular fold,” she said. “They would just kind of bond with themselves in weird, nonpredictable ways over and over again. To be a protein, you need a sequence that makes one shape. When people tried to make random sequences, or even somewhat directed sequences, they found that only a very, very small percentage of them might actually be a protein.”

With machine learning creating new and viable sequences resulting in this common fold, Slusky and her colleagues hope to generate a beta-barrel especially well-suited to finding metal ions in water. This result of the work will be biosensors based on beta barrels that can identify pollutants like lead in waterways.

“If we make them the right size, this molecule will be ideal to put some particular metal in, and you can have the right substituents so that it would bind that metal,” Slusky said. “Because it’s in a membrane, it can give you some sort of conductance difference — there’s a difference between when it’s bound and when it’s not bound. If you’re able to do that, you could sense for different metals, and different concentrations of those metals. There are a lot of big steps we want to accomplish, but I’m hopeful and excited.”

The work also will help train undergraduate researchers in Slusky’s lab, as well as inform Slusky’s teaching at KU as well as outreach to high-school science students.

Until recently, protein engineering predominantly focused on static, rigid proteins incapable of substantial shape modulation. These fixed-structure proteins have been instrumental in numerous applications, from household cleaning formulations to life-saving therapeutics like synthetic insulin and monoclonal antibodies developed for cancer and autoimmune diseases. However, the inherent limitation of rigidity constrains their functional range and adaptability, especially compared to natural proteins that undergo reversible conformational shifts to regulate critical cellular processes. These “shape-switching” proteins act as molecular toggles, integral to metabolic control, cell division, and signal transduction, and represent targets for nearly a third of all FDA-approved pharmaceuticals. Replicating this dynamic switching capability in engineered proteins has remained a grand challenge—until now.

Researchers at the University of California, San Francisco (UCSF), led by Professor Tanja Kortemme, have successfully demonstrated the design of synthetic proteins capable of reversible shape changes reminiscent of their natural counterparts. This pioneering work leverages recent breakthroughs in artificial intelligence, particularly the AlphaFold2 program, which predicts protein folding structures with unprecedented accuracy. By integrating computational modeling with biochemical expertise, the team engineered a small yet versatile protein module that can “swing” and bind calcium ions, a frequent molecular trigger in biology that induces conformational shifts.

Starting with a simple natural protein scaffold, graduate student Amy Guo devised a strategy to create a discrete movable domain within the protein architecture. This domain was designed to alternate between two energetically favorable conformations: one that binds calcium and another that releases it. Generating a virtual library of thousands of potential shapes, the researchers applied AI-driven structural predictions to pinpoint two particularly stable states. Detailed atomic-level simulations allowed Guo to visualize and optimize how subtle interactions between side chains and backbone atoms govern the twisting motion necessary for calcium capture and release. These computational endeavors, accelerated by the availability of AlphaFold2 during the pandemic, represent a milestone in protein design methodology.

Corroborating the computational predictions, the collaboration included nuclear magnetic resonance (NMR) imaging performed by pharmaceutical chemist Mark Kelly at UCSF. NMR provides a high-resolution, dynamic view of protein structures in solution, enabling validation of the engineered protein’s conformational shifts as predicted. The experimental confirmation that the synthetic protein domain successfully oscillates between its two designated shapes addressed a central verification step, instilling confidence that programmable protein motion is achievable in the lab, not solely within a computer model.

The implications of this technology are vast and reach beyond fundamental biochemical research. In medicine, dynamically engineered proteins could usher in a new generation of biosensors that undergo shape changes in direct response to disease biomarkers. This molecular responsiveness could trigger early-warning signals or activate targeted therapeutic pathways, offering precision medicine tailored to individual patient physiology. Moreover, custom proteins capable of switching conformations may act as highly specific drugs that adapt their function within complex biological environments, enhancing efficacy and minimizing off-target effects.

In agriculture, the ability to develop proteins that adapt their shape could revolutionize plant resilience strategies. Proteins designed to respond dynamically to environmental stresses such as drought, pest infestation, or soil nutrient fluctuations can provide crops with novel mechanisms to withstand and adapt to climate change conditions. This biotechnological leap could lead to sustainable increases in crop yields and reduce agricultural reliance on chemical interventions, fostering eco-friendly farming practices.

Environmental applications also abound for shapeshifting proteins. Engineered variants could be deployed to degrade persistent plastics or toxic pollutants through conformationally controlled catalytic cycles, enhancing bioremediation efforts. Beyond biological systems, this protein engineering paradigm may inspire innovative material science, such as the creation of self-healing metals where embedded proteins dynamically respond to microfractures by initiating molecular repair mechanisms, pushing the frontiers of biomimicry in engineering.

The UCSF study, published in Science, was a multidisciplinary effort integrating bioengineering, computational chemistry, and structural biology. The synergy of graduate students, principal investigators, and chemists combining expertise in AI, synthetic protein design, and NMR imaging signifies a new era where digital protein design tools catalyze experimental biology innovation. The team’s work was supported by the National Institutes of Health, emphasizing the critical role of sustained funding in bridging computational advances with practical biotechnological applications.

Beyond the immediate scientific community, this research showcases the potential of AI-augmented protein engineering to forge solutions for some of society’s most pressing challenges. The ability to rationally design proteins with intrinsic flexibility offers a paradigm shift in how we approach drug discovery, sustainable agriculture, and environmental stewardship. As computational methods improve and experimental techniques evolve, the prospect of an ever-expanding “toolbox” of engineered shapeshifters beckons.

Looking ahead, further refinement of design algorithms and expansion of target proteins will be essential. Increasing complexity, such as enabling multi-state shape switching or integrating responsive motifs for other biologically relevant ions and molecules, will broaden functional versatility. Collaborative efforts blending machine learning, structural characterization, and synthetic biology are poised to accelerate this frontier, creating proteins that mimic the nuanced choreography of natural biomolecules at an engineering scale previously unimaginable.

This breakthrough not only challenges previous conceptions about the limitations of protein engineering but also heralds a future where biological function can be custom-tailored with precision akin to software programming. The applications of such molecular machines are limited only by human imagination, carrying profound implications for health, the environment, and technology at large.

Subject of Research: Engineering dynamic, shapeshifting proteins capable of reversible conformational changes

Article Title: Not explicitly stated in the provided content

News Publication Date: May 22, 2024

Web References:
https://dx.doi.org/10.1126/science.adr7094
https://profiles.ucsf.edu/tanja.kortemme
https://www.ucsf.edu/

References: Not detailed beyond DOI and associated research groups

Keywords: Proteins, Artificial intelligence, Protein engineering, Calcium, Nuclear magnetic resonance, Biosensors, Crop yields, Metabolism, Cell division, Pollution, Environmental management, Disease incidence