Sequence-based Identification of Arginine Amidation Sites in Proteins Using Deep Representations of Proteins and PseAAC

(E-pub Ahead of Print)

Author(s): Sheraz Naseer*, Waqar Hussain, Yaser Daanial Khan, Nouman Rasool

Journal Name: Current Bioinformatics

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Background: Among all the major Post-translational modification, amidation seems to be a small change, where a peptide ends with an amide group (-NH 2), not a carboxyl group (-COOH). Thus, to study their physicochemical properties, identification of amidation mechanism is very important. However, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner.

Objectives: Herein, we propose a novel predictor for identification of arginine amide (R-Amide) sites in proteins, by integrating the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep features.

Methods: We use well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications.

Results: Among different DNNs, CNN showed the highest scores in terms of accuracy, and all other computed measures and outperforms all the previously reported predictors.

Conclusions: Based on these results, it is concluded that the proposed model can help to identify arginine amidation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

Keywords: Amidation, Arginine Amide, DNNs, Deep features, 5-steps rule, PseAAC

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574893615666200129110450
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