A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method. (May 2023)

• Record Type: Journal Article
• Authors: Naorem, Leimarembi Devi; Sharma, Neelam; Raghava, Gajendra P.S.
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: IL-5 inducing peptides -- Alignment-based method -- Alignment-free method -- Machine learning techniques -- BLAST -- Motifs; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106864
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Interleukin-5 (IL-5) can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. The aim of this study is to develop a model for predicting IL-5 inducing antigenic regions in a protein with high precision. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB. Our primary analysis indicates that IL-5 inducing peptides are dominated by certain residues like Ile, Asn, and Tyr. It was also observed that binders of a wide range of HLA alleles can induce IL-5. Initially, alignment-based methods have been developed using similarity and motif search. These alignment-based methods provide high precision but poor coverage. In order to overcome this limitation, we explore alignment-free methods which are mainly machine learning-based models. Firstly, models have been developed using binary profiles and eXtreme Gradient Boosting-based model achieved a maximum AUC of 0.59. Secondly, composition-based models have been developed and our dipeptide-based random forest model achieved a maximum AUC of 0.74. Thirdly, random forest model developed using selected 250 dipeptides and achieved AUC 0.75 and MCC 0.29 on validation dataset; best among alignment-free models. In order to improve the performance, we developed an ensemble or hybrid method that combined alignment-based and alignment-free methods. Our hybrid method achieved AUC 0.94 with MCC 0.60 on a validation/independent dataset. The best hybrid model developed in this study has been incorporated into the user-friendly web server and a standalone package named 'IL5pred' (https://webs.iiitd.edu.in/raghava/il5pred/ ). Highlights: IL-5 is a regulatory cytokine that plays a vital role in eosinophil-mediated diseases. Alignment-based models implemented using similarity and motif search. Alignment-free models developed using machine-learning techniques. Hybrid method developed using alignment-based and alignment-free models. A web server and standalone software for predicting IL-5 inducing peptides.