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Cutting-edge AI and machine learning are unlocking new insights into the complex world of protein design, revealing how solvents like water guide the assembly of complex molecular structures.seattle todayResearchers at Pacific Northwest National Laboratory have made a breakthrough discovery in protein design by leveraging AI and machine learning. A team of researchers studying the protein nanoribbons designed by Nobel laureate David Baker found that the complex order arises not only from the designed framework, but also crucially from the influence of solvents such as water during the assembly process.
why is it important
This study challenges existing protein design algorithms and highlights the importance of considering the role of solvents, especially water, when designing proteins. This discovery has far-reaching implications for areas such as biomineralization and the development of bio-based lightweight and crash-resistant materials.
detail
The researchers tracked the orientation and organization of the nanoribbons using a machine learning tool called AtomAI, and found that the nanoribbons were aligned in a single direction and organized into parallel rows. This was unexpected because the original plan was to track the arrangement of negative charges on the nanoribbons to match the regular lattice of positively charged potassium ions on the surface of the natural mineral mica. The team’s findings suggest that water on the mica surface, rather than the underlying potassium lattice, guides protein alignment.
- The study was published in Nature Communications in 2026.
players
Pacific Northwest National Laboratory
A U.S. Department of Energy research center focused on scientific discovery and technological innovation.
david baker
A Nobel Prize-winning scientist who designed the protein “nanoribbon” studied in this research.
james de yoreo
Co-lead author of this study, he is an expert in biomineralization and the adaptation of natural strategies to form inorganic materials.
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what they are saying
“The structure of the mantis shrimp shell, a natural composite of nanofibers, proteins, and minerals, is an important model for lightweight, impact-resistant, bio-inspired materials.”
— James De Yoreo, co-lead author
“Proteins designed to assemble on surfaces must explicitly include the role of solvent, and physics-based machine learning is essential to account for solvent effects when designing proteins.”
— James De Yoreo, co-lead author
what’s next
The research team plans to further investigate the role of solvents in protein design and continue to develop physics-based machine learning tools to guide the design of complex biomaterials.
Take-out
This study highlights the power of combining protein design, machine learning, and materials science to yield new insights and advance the field of bioinspired materials. By considering the critical role of solvents, researchers can develop more effective protein design algorithms and create innovative solutions inspired by nature.
