DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction

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Authors
Thapa, Niraj
Chaudhari, Meenal
McManus, Sean
Roy, Kaushik
Newman, Robert H.
Hiroto, Saigo
KC, Dukka B.
Issue Date
2020-04-23
Type
Conference paper
Language
en_US
Keywords
Succinylation , Deep learning , Convolutional neural network , Recurrent neural network , Long short-term memory , Embedding
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Abstract

Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to − 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure.

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© Authors. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Citation
Thapa, N., Chaudhari, M., McManus, S. et al. DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinformatics 21, 63 (2020)
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Springer
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1471-2105
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