| Nov 18, 2024 |
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(Nanowerk News) Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano–bio interface.
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Modeling the properties of the nano-bio interface is demanding since the important processes such as electronic charge transfer, chemical reactions or restructuring of the biomolecule surface can take place in a wide range of length and time scales, and the atomistic simulations need to be run in the appropriate aqueous environment.
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Machine learning help to study interactions at the atomic level
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Recently, researchers at the University of Jyväskylä demonstrated that it is possible to significantly speed up atomistic simulations of interactions between metal nanoparticles and blood proteins. Based on extensive molecular dynamics simulation data of gold nanoparticle – protein systems in water, graph theory and neural networks were used to create a methodology that can predict the most favorable binding sites of the nanoparticles to five common human blood proteins (serum albumin, apolipoprotein E, immunoglobulin E, immunoglobulin G and fibrinogen). The machine learning results were successfully validated by long-timescale atomistic simulations.
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“In recent months, we also published a computational study which showed that it is possible to selectively target over-expressed proteins at a cancer cell surface by functionalized gold nanoparticles carrying peptides and cancer drugs,” says professor of computational nanoscience Hannu Häkkinen.
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“With the new machine learning methodology, we can now extend our work to investigate how drug-carrying nanoparticles interact with blood proteins and how those interactions change the efficacy of the drug carriers,” Häkkinen concludes.
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The research will be continued
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The results will allow additional research to develop new computational methods for research in interaction between metal nanoparticles and biomolecules.
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“Machine learning is a very helpful tool when examining the use of nanoparticles in diagnostics and therapy applications in the field of nanomedicine. This will be one the main goals in our next project ‘Dynamic Nanocluster – Biomolecule Interfaces’ supported by the European Research Council,” says Häkkinen.
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The work was published in two articles in the international journals: Advanced Materials (“GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins”) and Bioconjugate Chemistry (“Rational Design of Targeted Gold Nanoclusters with High Affinity to Integrin αvβ3 for Combination Cancer Therapy”).
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