Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational model that could expedite the use of nanomaterials in biomedical applications. The study presented the first generalisable machine-learning framework capable of predicting how proteins interact with ligand-stabilised gold nanoclusters, materials widely employed in bioimaging, biosensing, and targeted drug delivery.
The adsorption of proteins onto nanomaterial surfaces is fundamental to many biological applications, including bioimaging and biosensing to targeted drug delivery. Gold nanoclusters, in particular, have attracted attention thanks to their biocompatibility and tunable optical properties. Yet existing studies that predict how proteins interact with these ligand-protected nanostructures often focus on isolated cases, leaving researchers without a unified model to guide design.
- This gap has created a clear need for general, scalable models capable of capturing the underlying rules of protein–nanocluster binding, specifies Postdoctoral Researcher Brenda Ferrari from the University of Jyväskylä.
New Machine Learning framework reveals principles behind biomolecule–gold nanocluster interactions
To address this challenge, researchers at the Nanoscience Center have developed a clustering-based machine-learning framework that identifies the chemical principles governing biomolecule adsorption on gold nanoclusters.
- Using advanced clustering analysis validated with atomistic simulations, performed with LUMI supercomputer at CSC – IT Center for Science, our research team uncovered the chemical rules governing the peptide–Au₃₈(p-MBA)₂₄ interface. The model determines which amino acids have higher or lower preference to bind to gold nanoclusters and identifies the specific chemical groups responsible for these interactions, says Ferrari.
Building a universal model for protein–gold nanocluster interactions
Designed explicitly to be general and scalable, the new framework extends beyond peptides, offering broad insights into protein–gold nanocluster interactions. This capability could significantly accelerate the screening of large numbers of proteins and support the development of more effective nanomaterials for biomedical applications.
- Our goal was to build a model that doesn’t just explain one particular system but that can be generalisable, says Ferrari. We will continue working on the limitations, but we already have a model that can be extended to broadly explain protein–gold nanocluster interactions and support the development of smarter nanomaterials for biomedical use, she continues.
The full study, with all codes and data, is published in the Aggregate journal .
Aggregate
Computational simulation/modeling
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Development of an Interaction Model of the Protein–Nanocluster Interface by Machine Learning–Assisted Clustering of Amino Acids
18-Nov-2025