Prediction of peptide hormones using an ensemble of machine learning and similarity‐based methods

Abstract

AbstractPeptide hormones serve as genome‐encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non‐hormonal peptide sequences. Initially, we developed similarity‐based methods utilizing BLAST and MERCI software. Although these similarity‐based methods provided a high probability of correct prediction, they had limitations, such as no hits or prediction of limited sequences. To overcome these limitations, we further developed machine and deep learning‐based models. Our logistic regression‐based model achieved a maximum AUROC of 0.93 with an accuracy of 86% on an independent/validation dataset. To harness the power of similarity‐based and machine learning‐based models, we developed an ensemble method that achieved an AUROC of 0.96 with an accuracy of 89.79% and a Matthews correlation coefficient (MCC) of 0.8 on the validation set. To facilitate researchers in predicting and designing hormone peptides, we developed a web‐based server called HOPPred. This server offers a unique feature that allows the identification of hormone‐associated motifs within hormone peptides. The server can be accessed at: https://webs.iiitd.edu.in/raghava/hoppred/.