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Immunoglobulins are important building blocks in biology and biotechnology. With the emergence of comprehensive deep‐learning approaches, there are enormous opportunities for fast and accurate methods of classification of protein–protein interactions to arise. Herein, widely accessible image classification algorithms for species‐specific typification of a range of different immunoglobulin G (IgG) complexes are repurposed. Droplets of various immunoglobulins mixed with a B‐cell superantigen (SAg) (recombinant staphylococcal Protein A) are deposited onto hydrophobic polymer substrates and the resulting protein stains are imaged using polarized light microscopy. A comprehensive study based on 23 745 images finds that the pretrained convolutional neural network (CNN) InceptionV3 not only successfully categorizes IgGs from four different species but also predicts their binding affinity to Protein A: averaged over 36 binding pairs, the following are observed: 1) an overall accuracy of 81.4%, 2) the highest prediction accuracy for human IgG, the antibody with the highest binding affinity for Protein A, and 3) that the classification accuracy regarding the various IgG/Protein A ratios generally correlates with the binding strength of the protein–protein–complex as determined via circular dichroism spectroscopy. In addition, the CNN pretrained with IgG/Protein A stain images has been challenged with a new set of images using a different superantigen (SAg, Protein G). Despite the use of the unknown SAg, the CNN correctly classifies the images of human IgG and Protein G as indicated by a 94% accuracy over the various molar binding ratios. These findings are noteworthy because they demonstrate that appropriately pretrained CNNs can be used for the prediction of protein–protein interactions beyond the scope of the original training set. Aided by deep‐learning methods, simple stains of mixed protein solutions may serve as accurate predictors of the strength of protein–protein interactions with relevance to protein engineering, self‐aggregation, or protein stability in complex media. [ABSTRACT FROM AUTHOR]

Chemical vapor deposition (CVD) polymerization is a commonly used approach in surface chemistry, providing a substrate‐independent platform for bioactive surface functionalization strategies. This work investigates the Arbuzov reaction of halogenated polymer coatings readily available via CVD polymerization, using poly(4‐chloro‐para‐xylylene) (Parylene C) as a model substance. Postpolymerization modification of these coatings via catalyst‐free and UV‐induced Arbuzov reaction using phosphites results in phosphonate‐functionalized polymers. The combination of infrared reflection‐absorption spectroscopy (IRRAS), X‐ray photoelectron spectroscopy (XPS), and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides detailed insights into the reaction progress. Time‐dependent studies suggest that the non‐polar phosphites penetrate deep into the CVD films and react with the polymer film. In addition, ToF‐SIMS, scanning electron microscopy (SEM), and atomic force microscopy (AFM) confirm spatial control of the reaction, resulting in localized chemical and topographical surface modification, recognizable by changes in interference color, fluorescence, and wettability. Preliminary 3D fluorescence spectroscopy investigations indicate tunable near‐infrared emission of these polymer films. This work is the first step toward generating multifunctional polymer coatings based on chemically modifiable, CVD polymers with potential applications in biomaterials, sensors, or optoelectronics. [ABSTRACT FROM AUTHOR]