An Explainable Identifier of iGHBPs Peptides Based on Deep PSSM Features and Learning Approaches

Abstract

Growth hormone can be effectively and non-covalently communicated with by a growth hormone binding protein (GHBP), also referred to as a soluble carrier protein.  Accurately recognizing the GHBP from a certain protein sequence is crucial for comprehending biological processes and cell growth.  In the postgenomic era, a lot of protein sequence data has been gathered, which makes it even more urgent to build an integrated computational method that can quickly and precisely identify possible GHBPs from a huge number of candidate proteins. In this work, we provide iGHBP, a growth hormone binding protein (GHBP) predictor tool.   To date, scant attention has been paid to protein descriptors, such as the amino acid index, which is a collection of 20 numerical values that indicate different physico-chemical and biological attributes of amino acid sequences and Dipeptide Composition (DPC), are used in feature extraction approaches. This study introduces a novel machine learning predictor called accurate computational identification of growth hormone binding proteins (ac-iGHBPs), utilizing an innovative gate recurrent unit (GRU) technique. We performed a cross-validation investigation to demonstrate the effectiveness of our feature selection process, and the results showed that iGHBP had an accuracy of 84.9%, 7% higher than the control very random tree predictor trained with all characteristics. Furthermore, in an objective examination on a different data set, our new iGHBP strategy performed better than the existing method.