Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack.

Area of Science:

• Biochemistry
• Computational Biology
• Pharmacology

Background:

• Accurate identification of anti-inflammatory peptides (AIPs) is vital for developing novel inflammation treatments.
• Current prediction methods may lack the efficiency and accuracy required for large-scale screening.

Purpose of the Study:

• To develop and validate a robust computational model for predicting anti-inflammatory peptides.
• To enhance the efficiency of anti-inflammatory peptide discovery through a high-throughput approach.

Main Methods:

• A two-layer stacking ensemble model, AIPStack, was developed using random forest and extremely randomized trees as base classifiers and logistic regression as the meta-classifier.
• Peptide sequences were represented using fused hybrid features derived from amino acid composition descriptors.
• The Shapley Additive exPlanation (SHAP) method was employed to identify essential sequence features.

Main Results:

• AIPStack achieved high performance metrics on an independent test set, including an AUC of 0.819, accuracy of 0.755, and MCC of 0.510.
• The model outperformed existing anti-inflammatory peptide predictors.
• Key sequence features contributing to anti-inflammatory activity were identified.

Conclusions:

• AIPStack offers an accurate and efficient method for predicting anti-inflammatory peptides.
• The model can facilitate high-throughput screening and guide experimental design for anti-inflammatory peptide discovery.
• This work contributes to the development of novel therapeutic strategies for inflammatory diseases.