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- Predicting quorum sensing peptides using stacked generalization ensemble with gradient boosting based feature selection
- Muthusaravanan Sivaramakrishnan , Rahul Suresh , Kannapiran Ponraj
- J. Microbiol. 2022;60(7):756-765. Published online June 22, 2022
- DOI: https://doi.org/10.1007/s12275-022-2044-9
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Abstract
- Bacteria exist in natural environments for most of their life as
complex, heterogeneous, and multicellular aggregates. Under
these circumstances, critical cell functions are controlled by
several signaling molecules known as quorum sensing (QS)
molecules. In Gram-positive bacteria, peptides are deployed
as QS molecules. The development of antibodies against such
QS molecules has been identified as a promising therapeutic
intervention for bacterial control. Hence, the identification of
QS peptides has received considerable attention. Availability
of a fast and reliable predictive model to effectively identify QS
peptides can help the existing high throughput experiments.
In this study, a stacked generalization ensemble model with
Gradient Boosting Machine (GBM)-based feature selection,
namely EnsembleQS was developed to predict QS peptides
with high accuracy. On selected GBM features (791D), the
EnsembleQS outperformed finely tuned baseline classifiers
and demonstrated robust performance, indicating the superiority
of the model. The accuracy of EnsembleQS is 4% higher
than those resulting from ensemble model on hybrid dataset.
When evaluating an independent data set of 40 QS peptides,
the EnsembleQS model showed an accuracy of 93.4% with
Matthew’s Correlation Coefficient (MCC) and area under the
ROC curve (AUC) values of 0.91 and 0.951, respectively. These
results
suggest that EnsembleQS will be a useful computational framework for predicting QS peptides and will efficiently support proteomics research. The source code and all datasets used in this study are publicly available at https:// github.com/proteinexplorers/EnsembleQS.
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