AIPPT: Predicts anti-inflammatory peptides using the most characteristic subset of bases and sequences by stacking ensemble learning strategies

Abstract

Therapeutic peptides play a vital role in developing peptide-based drugs. Recently, they have been applied as anti-inflammatory agents for a range of inflammatory conditions, including Alzheimer’s disease and rheumatoid arthritis. Laboratory-based identification of peptides with anti-inflammatory properties is a highly time-consuming and labor-intensive endeavor. To tackle this issue, researchers have developed computational methods, primarily centered on machine learning, to streamline the procedure. This paper presents AIPPT, an intelligent and computationally efficient prediction tool that introduces a novel stacking framework for the reliable identification of anti-inflammatory peptides (AIP). The study specifically employs a combination of four feature encodings, where their importance is assessed using the LightGBM method to create an optimal feature subset, which is then input to the three classifiers. The output probabilities from the three classifiers are further fed into a meta-classifier, constructing a two-layer stacking model. Subsequently, the output probabilities from the three classifiers are incorporated into a meta-classifier, establishing a two-layer stacking model. Subsequently, the output probabilities from the three classifiers are incorporated into a meta-classifier, establishing a two-layer stacking model.