Quantitative design rules for protein-resistant surface coatings using machine learning
journal contributionposted on 13.07.2021, 04:25 by Tu C Le, Matthew Penna, David WinklerDavid Winkler, Irene Yarovsky
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio – nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r2 of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types.
TCL thanks RMIT University for the Vice Chancellor's Research Fellowship.
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Science & TechnologyMultidisciplinary SciencesScience & Technology - Other TopicsSELF-ASSEMBLED MONOLAYERSPOLYETHYLENE OXIDEFIBRINOGEN ADSORPTIONPOLY(ETHYLENE GLYCOL)COMBINATORIAL LIBRARIESBIODEGRADABLE POLYMERSDESCRIPTOR SELECTIONBLOOD-PLASMAQSAR MODELSPREDICTIONPolymersMuramidaseFibrinogenLinear ModelsEquipment DesignSurface PropertiesModels, ChemicalNanoparticlesImmobilized ProteinsBiofoulingDatasets as TopicMachine Learning