Synthesis of a novel carboxybetaine copolymer with different spacer lengths and inhibition of nonspecific protein adsorption on its polymer film.

Herein, we designed and synthesized a thermally stable carboxybetaine copolymer with a one- or three-carbon spacer between ammonium and carboxylate groups (CBMA1 and CBMA3) to create an anti-nonspecific adsorption surface with the ability to immobilize antibodies. A series of controlled poly(N,N-dimethylaminoethyl methacrylate) was successfully prepared using reversible addition-fragmentation chain-transfer (RAFT) polymerization and was derived to carboxybetaine copolymers of poly(CBMA1-co-CBMA3) [P(CBMA1/CBMA3)] with various CBMA1 contents, including the homopolymers of CBMA1 and CBMA3. Thermal stability of the carboxybetaine (co)polymers was higher than that of the carboxybetaine polymer with a two-carbon spacer (PCBMA2). Further, we also evaluated nonspecific protein adsorption in fetal bovine serum and antibody immobilization on the substrate coated with P(CBMA1/CBMA3) copolymers using surface plasmon resonance (SPR) analysis. As the CBMA1 content increased, nonspecific protein adsorption on the P(CBMA1/CBMA3) copolymer surface decreased. Similarly, the immobilization amount of the antibody decreased as the CBMA1 content increased. However, the figure of merit (FOM), defined as the ratio of the amount of antibody immobilization to that of nonspecific protein adsorption, depended on the CBMA3 content; FOM was higher when the CBMA3 content was 20-40% than those of CBMA1 and CBMA3 homopolymers. These findings will help enhance the sensitivity of the analysis using molecular interaction measurement devices, such as SPR and quartz crystal microbalance.

Prediction of Serum Adsorption onto Polymer Brush Films by Machine Learning.

Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films' physicochemical information and the monomers' chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film's physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films.