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bioRxiv · 10.1101/2022.10.19.512965

Prediction and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

Devi, N. L.·Sharma, N.·Raghava, G. P. S.
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Abstract

Interleukin-5 (IL-5) is the key cytokine produced by T-helper, eosinophils, mast and basophils cells. It can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. Though numerous methods have been developed to predict HLA binders and cytokines-inducing peptides, no method was developed for predicting IL-5 inducing peptides. 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. First, 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 developed machine learning-based models for predicting IL-5 inducing peptides using a wide range of peptide features. Our random-forest model developed using selected 250 dipeptides achieved the highest performance among alignment-free methods with AUC 0.75 and MCC 0.29 on validation dataset. 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 validation/ independent dataset. The best model developed in this study has been incorporated in the web server IL5pred (https://webs.iiitd.edu.in/raghava/il5pred/). Key PointsO_LIIL-5 is a regulatory cytokine that plays a vital role in eosinophil-mediated diseases C_LIO_LIBLAST-based similarity search against IL-5 inducing peptides was employed C_LIO_LIA hybrid approach combines alignment-based and alignment-free methods C_LIO_LIAlignment-free models are based on machine learning techniques C_LIO_LIA web server IL5pred and its standalone software have been developed C_LI Authors BiographyO_LIDr. Naorem Leimarembi Devi is currently working as a DBT-Research Associate in Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LINeelam Sharma is pursuing her Ph.D. in Computational Biology from the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIProf. G.P.S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

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Devi, N. L., Sharma, N., Raghava, G. P. S.. 2022-10-21. Prediction and scanning of IL-5 inducing peptides using alignment-free and alignment-based method. https://doi.org/10.1101/2022.10.19.512965

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