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Lysine carboxylation: unveiling a spontaneous post-translational modification.

ABSTRACT: The carboxylation of lysine residues is a post-translational modification (PTM) that plays a critical role in the catalytic mechanisms of several important enzymes. It occurs spontaneously under certain physicochemical conditions, but is difficult to detect experimentally. Its full impact is unknown. In this work, the signature microenvironment of lysine-carboxylation sites has been characterized. In addition, a computational method called Predictor of Lysine Carboxylation (PreLysCar) for the detection of lysine carboxylation in proteins with available three-dimensional structures has been developed. The likely prevalence of lysine carboxylation in the proteome was assessed through large-scale computations. The results suggest that about 1.3% of large proteins may contain a carboxylated lysine residue. This unexpected prevalence of lysine carboxylation implies an enrichment of reactions in which it may play functional roles. The results also suggest that by switching enzymes on and off under appropriate physicochemical conditions spontaneous PTMs may serve as an important and widely used efficient biological machinery for regulation.

SUBMITTER: Jimenez-Morales D

PROVIDER: S-EPMC3919261 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Lysine carboxylation: unveiling a spontaneous post-translational modification.

Jimenez-Morales David D Adamian Larisa L Shi Dashuang D Liang Jie J

Acta crystallographica. Section D, Biological crystallography 20131224 Pt 1

The carboxylation of lysine residues is a post-translational modification (PTM) that plays a critical role in the catalytic mechanisms of several important enzymes. It occurs spontaneously under certain physicochemical conditions, but is difficult to detect experimentally. Its full impact is unknown. In this work, the signature microenvironment of lysine-carboxylation sites has been characterized. In addition, a computational method called Predictor of Lysine Carboxylation (PreLysCar) for the de ...[more]

PMID: 24419378

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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

Lysine carboxylation: unveiling a spontaneous post-translational modification.

Publications

Lysine carboxylation: unveiling a spontaneous post-translational modification.

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