Comprehensive Evaluation and Comparison of Machine Learning Methods in QSAR Modeling of Antioxidant Tripeptides

Due to their multiple beneficial effects, antioxidant
peptides
have attracted increasing interest. Currently, the screening and identification
of bioactive peptides, including antioxidative peptides based on wet-chemistry
methods are time-consuming and highly rely on many advanced instruments
and trained personnel. Quantitative structure–activity relationship
(QSAR) analysis as an in silico method can be more
efficient and cost-effective. However, model performance of QSAR studies
on antioxidant peptides was still poor due to limited attempts in
model development approaches. The objective of this study was to compare
popular machine learning methods for antioxidant activity modeling
and screening of tripeptides and identify the critical amino acid
features that determine the antioxidant activity. 533 numerical indices
of amino acids were adopted to characterize 130 tripeptides with known
antioxidant activity from the published literature, and then 7 feature
selection strategies plus pairwise correlation were used to screen
the most important indices for antioxidant activity and model building.
14 machine learning methods were used to build models based on the
feature selection strategies, respectively. Among the 98 models, non-linear
regression methods tended to perform better, and the best model with
an R2Test of 0.847 and RMSETest of 0.627 for tripeptide antioxidants was obtained by combining
random forest for feature selection and tree-based extreme gradient
boost regression for model development. Based on the predicted antioxidant
values of 7870 unknown tripeptides, potentially high antioxidant activity
tripeptides all have a tyrosine, tryptophan, or cysteine residue at
the C-terminal position. Furthermore, the predicted antioxidant activity
of six synthesized tripeptides was confirmed through experimental
determination, and for the first time, the cysteine or tyrosine residue
at the C-terminal was found to be critical to the antioxidant activity
based on both QSAR models and experimental observations.