Dataset Information

Identification of lysine succinylation as a new post-translational modification.

ABSTRACT: Of the 20 ribosomally coded amino acid residues, lysine is the most frequently post-translationally modified, which has important functional and regulatory consequences. Here we report the identification and verification of a previously unreported form of protein post-translational modification (PTM): lysine succinylation. The succinyllysine residue was initially identified by mass spectrometry and protein sequence alignment. The identified succinyllysine peptides derived from in vivo proteins were verified by western blot analysis, in vivo labeling with isotopic succinate, MS/MS and HPLC coelution of their synthetic counterparts. We further show that lysine succinylation is evolutionarily conserved and that this PTM responds to different physiological conditions. Our study also implies that succinyl-CoA might be a cofactor for lysine succinylation. Given the apparent high abundance of lysine succinylation and the significant structural changes induced by this PTM, it is expected that lysine succinylation has important cellular functions.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC3065206 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Identification of lysine succinylation as a new post-translational modification.

Zhang Zhihong Z Tan Minjia M Xie Zhongyu Z Dai Lunzhi L Chen Yue Y Zhao Yingming Y

Nature chemical biology 20101212 1

Of the 20 ribosomally coded amino acid residues, lysine is the most frequently post-translationally modified, which has important functional and regulatory consequences. Here we report the identification and verification of a previously unreported form of protein post-translational modification (PTM): lysine succinylation. The succinyllysine residue was initially identified by mass spectrometry and protein sequence alignment. The identified succinyllysine peptides derived from in vivo proteins w ...[more]

PMID: 21151122

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Project description:Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

Dataset Information

Identification of lysine succinylation as a new post-translational modification.

Publications

Identification of lysine succinylation as a new post-translational modification.

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