Sheraz Naseer*, Waqar Hussain, Yaser Daanial Khan and Nouman Rasool
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.
Amidation, Arginine Amide, DNNs, Deep features, 5-steps rule, PseAAC
Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, National Center of Artificial Intelligence, Punjab University College of Information Technology, University of the Punjab, Lahore, Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Dr Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270