Application of Machine Learning Algorithms to Estimate Enzyme Loading, Immobilization Yield, Activity Retention, and Reusability of Enzyme–Metal–Organic Framework Biocatalysts

The
ability to predict enzyme–metal–organic framework
(MOF) properties such as enzyme loading, immobilization yield, activity
retention, and reusability can maximize product yield and extend the
operational life of enzyme–MOF biocatalysts. However, this
is challenging due to the vast combinations of available metal and
ligand building blocks for MOF and enzymes. Therefore, several machine
learning (ML) algorithms are applied in this study using data collected
from 127 journal articles to estimate these biocatalyst parameters.
Twelve input variables, including the metal and ligand properties
of MOF, as well as the enzyme properties, were integrated and fed
into two ML algorithmsrandom forest and Gaussian process regression
(GPR)to predict model outputs. A 10-fold cross-validation
approach with grid search was applied to obtain the optimal hyperparameter
values. The random forest model (RFM) provided more accurate estimates
of the enzyme loading, immobilization yield, and reusability of the
biocatalyst than the GPR model, with relatively high R2 values of 0.85, 0.77, and 0.91, respectively. Both models
are less effective in predicting the enzyme activity retention, however,
with R2 values of 0.63 or lower. Sensitivity
analysis of the input variables revealed the most significant variables
for each corresponding output parameter, allowing further optimization
of the RFM. The final RFM was then tested with a second unseen dataset
collected from experiments. Findings confirmed the validity of the
predictive model, including a relative error of less than 25%. Our
model can aid in the synthesis of enzyme–MOF biocatalysts by
providing valuable estimates of these output parameters for different
MOF precursors and enzymes, saving experimental time and cost.