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.
Peptide-based self-assembled monolayers (peptide-SAMs) with specific zwitterionic amino acid sequences express an anti-biofouling property. In this work, we performed protein adsorption and cell adhesion tests using peptide-SAMs with repeating units of various zwitterionic pairs of amino acids (EK, DK, ER, and DR). The SAMs with the repeating units of EK and DK (EK and DK SAMs) manifested excellent bioinertness, whereas the SAMs with the repeating units of ER and DR (ER and DR SAMs) adhered proteins and cells. We also performed surface force measurements using atomic force microscopy to elucidate the mechanism underlying the difference in the anti-biofouling property. Our measurements revealed that water-induced repulsion with a range of about 8 nm acts between EK SAMs (immobilized on both probe and substrate) and DK SAMs, whereas such repulsion was not observed for ER and DR SAMs. The strength of the repulsion exhibited a clear correlation with the protein- and cell-resistance of the SAMs, indicating that the interfacial water in the vicinity of EK and DK SAMs is considered as a physical barrier to deter protein and cells from their adsorption or adhesion. The range of the repulsion observed for EK and DK SAMs is longer than 8 nm, indicating that the hydrogen bonding state of the interfacial water with a thickness of 4 nm is modified by EK and DK SAMs, resulting in the expression of the anti-biofouling property.
Bacterial biofilms
reduce the performance and efficiency of biomedical
and industrial devices. The initial step in forming bacterial biofilms
is the weak and reversible attachment of the bacterial cells onto
the surface. This is followed by bond maturation and secretion of
polymeric substances, which initiate irreversible biofilm formation,
resulting in stable biofilms. This implies that understanding the
initial reversible stage of the adhesion process is crucial to prevent
bacterial biofilm formation. In this study, we analyzed the adhesion
processes of
E. coli
on self-assembled
monolayers (SAMs) with different terminal groups using optical microscopy
and quartz crystal microbalance with energy dissipation (QCM-D) monitoring.
We found that a considerable number of bacterial cells adhere to hydrophobic
(methyl-terminated) and hydrophilic protein-adsorbing (amine- and
carboxy-terminated) SAMs forming dense bacterial adlayers while attaching
weakly to hydrophilic protein-resisting SAMs [oligo(ethylene glycol)
(OEG) and sulfobetaine (SB)], forming sparse but dissipative bacterial
adlayers. Moreover, we observed positive shifts in the resonant frequency
for the hydrophilic protein-resisting SAMs at high overtone numbers,
suggesting how bacterial cells cling to the surface using their appendages
as explained by the coupled-resonator model. By exploiting the differences
in the acoustic wave penetration depths at each overtone, we estimated
the distance of the bacterial cell body from different surfaces. The
estimated distances provide a possible explanation for why bacterial
cells tend to attach firmly to some surfaces and weakly to others.
This result is correlated to the strength of the bacterium–substratum
bonds at the interface. Elucidating how the bacterial cells adhere
to different surface chemistries can be a suitable guide in identifying
surfaces with a more significant probability of contamination by bacterial
biofilms and designing bacteria-resistant surfaces and coatings with
excellent bacterial antifouling characteristics.
In this review, we summarize the current situation of materials informatics in the field of biomaterials. Different from other fields working on solid state materials, the functions of biomaterials are difficult to predict by using theoretical or computational approaches. Therefore, we need to collect data experimentally for the construction of dataset. We introduce our recent achievements on the prediction of protein adsorption onto self-assembled monolayers (SAMs) and polymer blush films. In addition, we describe the procedure of the data acquisition, selection of descriptors and algorithms.
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