IAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions

Sheraz Naseer,Rao Faizan Ali,Suliman Mohamed Fati,Amgad Muneer

doi:10.3390/sym13040560

Abstract

Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like pharmaceuticals, natural products, and biologically active compounds. The in-vivo, ex-vivo, and in-vitro identification of amidation sites is a costly and time-consuming but important task to study the physiochemical properties of amidated peptides. A less costly and efficient alternative is to supplement wet lab experiments with accurate computational models. Hence, an urgent need exists for efficient and accurate computational models to easily identify amidated sites in peptides. In this study, we present a new predictor, based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC), to learn efficient, task-specific, and effective representations for valine amidation site identification. Well-known DNN architectures are used in this contribution to learn peptide sequence representations and classify peptide chains. Of all the different DNN based predictors developed in this study, Convolutional neural network-based model showed the best performance surpassing all other DNN based models and reported literature contributions. The proposed model will supplement in-vivo methods and help scientists to determine valine amidation very efficiently and accurately, which in turn will enhance understanding of the valine amidation in different biological processes.

Highlights

Amidation is regarded as a change in organic molecules where, instead of the carboxyl group (–COOH), the amide group (–NH2) is incorporated in the molecule [1,2]
We propose a new predictor for determining sites of valine amide (Vamide) in proteins by integrating Chou’s Pseudo Amine Acid Composition (PseAAC) [33,34] with deep neural networks to learn deep representations resulting in better site identification
Notable evaluation metrics used in this study include receiver operating characteristics curve (ROC) curve, precisionrecall curve and point metrics, including mean average precision, accuracy, F1 measurements, and Matthew’s correlation coefficient (MCC) to find the best deep neural networks (DNN)-based

Summary

Introduction

Amidation is regarded as a change in organic molecules where, instead of the carboxyl group (–COOH), the amide group (–NH2) is incorporated in the molecule [1,2]. Amidated peptides have a longer half-life in the blood and are less susceptible to proteolysis. When a carboxyl group becomes an amide group, which may be a proton or a deproton, the peptide’s properties become less susceptible to physiological pH changes. The C-terminus of the amidated peptide is closely aligned with the GPCR transmembrane, resulting in enhanced coordination and signal transmission. Peptides’ biological activity such as vasopressin, oxytocin, and TRH is substantially decreased in the absence of a C-terminus amide moiety [5,6]. Alpha-amides in the C-terminus comprise about half of the physiologically active peptides and peptide hormones. PAM catalyzes the formation of peptide amides from precursors of

Methods

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Discussion

Conclusion