A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

Abstract

A host-specific technique has been developed for annotating interferon-gamma (IFN-{gamma}) inducing peptides, it is an updated version of IFNepitope. In this study, dataset used for developing prediction method contain experimentally validated 25492 and 7983 IFN-{gamma} inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. In order to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST and achieved AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-{gamma} inducing peptides, thus server/software have been developed (https://webs.iiitd.edu.in/raghava/ifnepitope2/). HighlightsO_LIAn updated method for predicting interferon-gamma (IFN-{gamma}) inducing peptides. C_LIO_LIA wide range of features have been generated using Pfeature tool. C_LIO_LIModels were trained and tested on experimentally validated datasets. C_LIO_LIHybrid models developed by combining machine-learning and BLAST. C_LIO_LIIFNepitope2 server is available to design subunit or peptide-based vaccines. C_LI