MetaRT: structure-embedded stacked ensemble machine learning prediction of retention time of short peptides
Predicting peptide retention time (RT) remains a significant challenge, particularly when training data is limited. In this study, we present MetaRT, a stacked-ensemble machine learning framework designed to predict the RTs from small dataset of hydrophobic peptides. Peptides composed of hydrophobic amino acids—phenylalanine (F), isoleucine (I), methionine (M), and tryptophan (W) were synthesized, and their experimental RTs were measured from the mixture entities. MetaRT utilizes a graph convolutional network (GCN) to extract structural features from the peptide sequences. The MetaRT model architecture employed multiple base learners, integrating the outputs through a meta-learner optimized via hyperparameter tuning and 3-fold cross-validation. Besides, the performance of MetaRT was compared to three ensemble methods - weight averaging, bagging, and boosting. The results demonstrated that structure-based MetaRT outperformed both base learners and the ensemble models, achieving a lower root mean square error (RMSE) of 0.08 and a maximum RT deviation of approximately 1.4 min. Compared to the prediction performance on molecular descriptors inclusion, the structure-guided model consistently performed well in terms of RMSE. Notably, MetaRT accurately predicted the RTs of sequence isomers by leveraging the structural features, with deviations ranging from 0.2 to 1.3 min. In contrast, descriptor-based model showed increased prediction error for the isomeric sequences. For peptides with lower hydrophobicity that were not included in the training data, the structure-based predictions led to the maximum deviation of 4.9 min from the experimental RTs. The entire predicted RTs were subsequently validated by linear regression analyses with the corresponding experimental values. These findings highlight the potential of MetaRT as a structure-based predictive tool for improving RT prediction accuracy, especially in data-limited scenarios. Future work will focus on enhancing the robustness of MetaRT by incorporating a wider variety of peptide classes to further refine its predictive capabilities.