Fig. 6 Application of artificial intelligence in antibody
mimetics
Notes: Each node represents a keyword; the size of the keyword is
determined by the rate of occurrence.
Furthermore, artificial intelligence technology, such as Alphafold (Fig.
6) (Tunyasuvunakool et al., 2021 ),
is applied to accurately predict the structure and conformation of an
unknown protein based on regular binding and folding patterns collected
from related database such as Protein Data Bank. In particular, the
three-dimensional structure of an antibody can already be predicted from
its gene. However, there is a big proportion of antibodies without
corresponding gene available, furthermore, dynamic conformational change
of antibodies when binding with antigens or receptors cannot be
predicted yet (Abanades et al.,
2022 ). It will greatly diminish time and cost of antibody design if
protein structure could be predicted based on the structure of the
antigens or receptors, while the conformational change and more limited
data of the antibody-antigen complex increase the challenge for this
breakthrough. However, there are still a lot of limitation of
computational approach restricting its further optimization and
application in antibody engineering. For example, computational analysis
and AI algorithms require large amounts of data to learn and make
accurate predictions. However, there is limited data available on
antibody-antigen interactions, which can limit the accuracy of
predictions made by these methods. Computational models used for
predicting the structure and function of antibody mimetics are based on
assumptions and simplifications, which may not accurately represent the
complexity of real-world systems. This can lead to inaccuracies in
predictions and suboptimal designs, Computational design methods often
rely on the availability of templates for the design of new antibody
mimetics. However, the diversity of available templates is limited,
which can limit the range of possible designs.
Table 3 Antibody
mimetics currently approved by the FDA or in clinical trials