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Chapter 17
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Design of Biomaterials Using Informatics
Tomohiro Hayashi
17.1 Introduction
17.1.1 Materials Informatics
The term “materials informatics”—the application of information science to material
1
design—is now widely recognized [
Initiative in the United States in 2011, has permeated various sectors, including
], catalysts [3, 4], organic electronics [
battery materials [
materials [
domains. This encompasses not only the proposition of chemical structures of functional materials [
8], and autonomous material exploration facilitated by the integration with robotics.
[
In the field of biomaterials, numerous instances of informatics applications have
emerged. These applications span a wide array of areas, including drugs [
folds for regenerative medicine [
materials [
bridging bioinformatics, molecular and cell biology, organic and inorganic chemistry, polymer science, surface and interface science, colloid science, and chemical
engineering, to list a few. As for drug design, an intriguing convergence of molecular
simulation and informatics has been promoted [
significantly elevate the efficiency of drug discovery. However, challenges persist in
the design of biomaterials that interact with biomolecules, cells, and tissues [
The primary challenges in this field stem from the inability to predict interactions
between biomolecules, cells, and biomaterials based solely on the chemical structures
of the biomaterials using conventional theoretical and simulation approaches. The
2
6
]. In recent times, significant progress has been reported in further diverse
7] but also the suggestion of reaction pathways in organic synthesis
], etc. It becomes evident that this field is highly interdisciplinary,
11, 12
]. This trend, initiated by the Materials Genome
5], and opto-electric device
9], scaf-
10
], anti-fouling materials, antiviral and antibacterial
]. This integration is anticipated to
13
14–16
].
T. Hayashi (B)
Department of Materials Science and Engineering, School of Materials and Chemical Technology,
Tokyo Institute of Technology, 4259, Nagatsuta-Cho, Midori-Ku, Yokohama 226-8502, Japan
e-mail: tomo@mac.titech.ac.jp
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
H. Satoh et al. (eds.), Drug Development Supported by Informatics,
https://doi.org/10.1007/978-981-97-4828-0_17
313
314 T. Hayashi
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complexity of these interactions in an aqueous environment arises from a mixture of
van der Waals f orces, electrostatic double layers (also known as DLVO force), hydration forces, and the steric repulsion of flexible polymer chains (termed as entropic
forces) [
responses after adhering to the materials are dictated by the molecular interaction
between a protein layer comprised of adsorbed proteins and the proteins within the
cell membrane [
at the interface and the molecular processes is nearly impossible.
alone cannot suffice in the construction of a comprehensive dataset; researchers need
to rely on experimental results for database creation. To address this issue, combinatorial methods have been deployed to systematically obtain data (for instance,
responses of biomolecules, cells, and tissues to artificial biomaterials). In these
studies, the chemical parameters (such as atomic ratio, functional groups, film thickness, etc.) change based on the position of the substrates. Researchers can compile
a large dataset by conducting protein adsorption or cell adhesion tests using these
substrates.
polymer brush films [
fabricated and tested in an aqueous phase, researchers can employ liquid-handling
robots [
17, 18].
When considering cell adhesion, the situation further complicates. The cellular
19]. As a result, obtaining a detailed understanding of the interactions
Given these circumstances, computer simulations and theoretical approaches
However, these systems are predominantly limited to monolayers [20–22],
23, 24
], and metals (oxides) [25]. For biomaterials that can be
26, 27
].
17.1.2 Biomolecule Mimetics
Traditional bioinformatics methodologies have exhibited limited success in biomaterial design. In bioinformatics, researchers strive to identify common genome
sequence patterns associated with specific protein functions [
has proven successful in predicting protein function from the sequences, its application to biomaterial design has been more challenging. There have been a handful of
successful instances of biomaterial design using a bioinformatics-like approach, yet
it often proves difficult to extract useful guidelines for material design.
A significant breakthrough was achieved by White and colleagues, who
constructed anti-fouling peptide monolayers by deciphering chemical structural rules
from the three-dimensional structures of protein molecules [
composition of amino acid residues on the surfaces of protein molecules constituting
human bodies. Their analysis revealed a common presence of zwitterionic pairs of
glutamic acid (or aspartic acid) and lysine on these surfaces. Leveraging this finding,
they successfully constructed anti-fouling zwitterionic peptide-based self-assembled
monolayers [
can be utilized to unearth hidden patterns from nature for innovative applications.
30, 31]. This achievement serves as a prime example of how informatics
28]. While this approach
29
]. They analyzed the
17 Design of Biomaterials Using Informatics 315
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17.1.3 Scope of This Review
Despite the examples outlined above, the application of informatics in the field of
biomaterials still remains relatively limited when compared to other disciplines such
as battery technology, catalysis, and magnetic materials.
This review presents illustrative examples of protein adsorption predictions
derived from the chemical structures of biomaterials. Protein adsorption on biomaterial surfaces constitutes one of the most fundamental properties of biomaterials and
exhibits a profound correlation with the aggregation of biomolecules, cell adhesion,
tissue response, and immune system reactions, among others. Drawing upon past
examples, we explore the potential of employing informatics for material design and
screening processes. Furthermore, we delve into the associated technical challenges.
17.2 Application of Informatics for Biomaterial Design
17.2.1 Prediction of Protein Adsorption onto Self-Assembled
Monolayers and Application for Material Screening
Self-assembled monolayers (SAMs) are an important type of coating often used in a
]. To
32
make a SAM, you start with molecules that have a special end called a “head group.”
This head group has a strong affinity or liking for a particular surface, like gold, silver,
or silicon [
spontaneously attach themselves to the surface through their head groups, creating
a single layer (hence “monolayer”) of molecules (Fig.
molecule, called the “tail group,” can be designed to have specific properties that are
useful for different applications.
33–36]. When these molecules are exposed to the chosen surface, they
17.1). The other end of the
Fig. 17.1 (Left) Schematic illustration of self-assembled monolayers (SAMs). (Right ) Scanning
tunneling microscope (STM) image of butanethiol SAM (20 × 20 nm
the unit cell
2
). The black frame indicates
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Extensive research has been carried out utilizing SAMs as a platform for analyzing
protein adsorption on organic material surfaces, resulting in the publication of over
1000 papers. Using them, we have compiled a dataset comprising the molecular
structures of approximately 300 types of SAMs [
12]. Each structure’s data includes
the number of constituent atoms, elemental composition, and the count of chemical
bonds. The dataset also incorporates the quantity of adsorbed fibrinogen, a blood
protein vital in coagulation. With this information, we aimed to construct a regression
model capable of predicting the level of adsorption based on the molecular structure.
Various methods exist for constructing regression models, ranging from linear and
non-linear function regression to advanced deep learning techniques. However, in this
study, we chose to utilize an Artificial Neural Network (ANN) (Fig.
17.2) owing to its
impressive predictive accuracy. The ANN constructed for this study comprises three
layers: an input layer, a hidden layer (which serves as an intermediate information
processing layer), and an output layer. Each element or neuron in these layers is
connected via weights, facilitating information transfer from the input to the output
layer.
Parameters associated with the SAMs’ molecular structure, which are the explanatory variables, along with the quantity of fibrinogen adsorption, our dependent variables, serve as the data populating the input and output layers, respectively. During
the ANN training phase, using the dataset we created, the weights connecting the
neurons are optimized. This process enables the network to predict protein adsorption
based on the molecular structure.
Figure 17.3 displays the results of protein adsorption prediction using the trained
ANN. The x-axis corresponds to the experimental values, while the y-axis signifies
the predicted values. The proximity of the data points to the y = x line indicates the
level of prediction accuracy. In this instance, we achieved highly accurate predictions
with a correlation coefficient of 0.98.
It also became evident that the prediction accuracy is heavily reliant on the dataset
used. Notably, data variations, which are likely due to differences in measurement
Fig. 17.2 Schematic
representation of the
Artificial Neural Network
(ANN) employed in this
study. The ANN is
comprised of input, hidden,
and output layers, with
neurons interlinked via
weighting factors. (Redrawn
based on the figure in [
12
])
17 Design of Biomaterials Using Informatics 317
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Fig. 17.3 Predicted amounts
of fibrinogen adsorbed onto
the SAMs plotted as a
function of the experimental
values. (Redrawn based on
the figure in [
12
])
equipment and handling, significantly impacted the ANN’s prediction accuracy. As
a result, we are presently compiling data using a combinatorial platform, specifically
SAMs with a continuous chemical composition gradient. For more details, please
refer to the original paper.
The trained ANN also enables the prediction of protein adsorption to SAMs,
composed of hypothetical molecular structures, by inputting those structures.
17.4 displays the outcome of protein adsorption predictions when data not
Figure
included in the training dataset is input into the trained ANN.
Fig. 17.4 Prediction of the amounts of fibrinogen adsorbed on hypothetical SAMs outside the
dataset. They possess different terminal groups. The numbers of ethylene glycol (EG) (left two)and
methylene (right four ) units were changed
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Initially, when the number of ethylene glycol units—the terminal groups that
impart anti-protein adsorption properties to SAMs—is increased, the predicted quantity of protein adsorption drastically decreases. Additionally, modifying the length of
the molecule’s alkyl chain, which determines the thickness of SAMs’ spacer layer,
does not impact protein adsorption. This is because the properties of SAMs are
dictated by the terminal groups, not by the spacer layer.
These results demonstrate that our intuitive understanding of SAMs is replicated
in the ANN when it is trained with the appropriate data (Redrawn based on the figure
36]).
in [
17.2.2 Prediction of Protein Adsorption onto Self-Assembled
Monolayers and Application for Material Screening
Polymer films surpass SAMs in terms of physical and chemical stability, with diverse
applications such as biosensing and cell scaffolding materials [
polymer brush films, which are high-density polymers affixed to a solid substrate in a
brush-like manner by polymerizing monomers, have demonstrated high performance
as biomaterials. These include attributes like chemical and physical stability and
high resistance to protein adsorption and cell adhesion, differing from polymer films
created by spin coating or simple drop drying. Even now, extensive research is being
conducted on every aspect of these films, from their fabrication methods to their
applications.
Polymer brush thin films diverge from SAMs as the film thickness and molecular
packing density can vary substantially based on the monomer type and fabrication
conditions. As such, in this study, we used X-ray reflectivity (XRR) to measure the
thickness (0.5 ~ 30 nm) and density of each polymer brush film in air. We then adopted
20 descriptors, including structural parameters obtained from the aforementioned
experiments, the molecular structure, and MlogP (which signifies the hydrophobicity
of the polymer film), as explanatory variables. Subsequently, by analyzing Pearson’s
correlation coefficient, we performed “dimensionality reduction,” which involved
extracting parameters with high independence (low correlation between parameters).
In this study, we considered a biosensor targeting blood and used the amount of
adsorption of biomolecules, such as proteins and lipids, which are serum components,
as the objective variable.
In this study, we prepared five types of polymer brush thin films using the five types
of monomers depicted in Fig.
methacrylamide (HPMA), carboxybetaine methacrylate (CBMA), ethyleneglycol
(EG), and 2-hydroxyethyl methacrylate (HEMA), which all exhibit strong resistance
to proteins and cells, as well as 2-(dimethylamino)ethyl methacrylate (DMAEMA),
which adheres to proteins and cells. For each monomer, we formed 10 types of thin
films with varying film thicknesses and molecular densities and conducted serum
17.5 [11
]. We based these films on N -(2-hydroxypropyl)
37, 38]. Particularly,
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