Dataset Information

FLAMS: Find Lysine Acylations and other Modification Sites.

ABSTRACT:

Summary

Today, hundreds of post-translational modification (PTM) sites are routinely identified at once, but the comparison of new experimental datasets to already existing ones is hampered by the current inability to search most PTM databases at the protein residue level. We present FLAMS (Find Lysine Acylations and other Modification Sites), a Python3-based command line and web-tool that enables researchers to compare their PTM sites to the contents of the CPLM, the largest dedicated protein lysine modification database, and dbPTM, the most comprehensive general PTM database, at the residue level. FLAMS can be integrated into PTM analysis pipelines, allowing researchers to quickly assess the novelty and conservation of PTM sites across species in newly generated datasets, aiding in the functional assessment of sites and the prioritization of sites for further experimental characterization.

Availability and implementation

FLAMS is implemented in Python3, and freely available under an MIT license. It can be found as a command line tool at https://github.com/hannelorelongin/FLAMS, pip and conda; and as a web service at https://www.biw.kuleuven.be/m2s/cmpg/research/CSB/tools/flams/.

SUBMITTER: Longin H

PROVIDER: S-EPMC10783949 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

FLAMS: Find Lysine Acylations and other Modification Sites.

Longin Hannelore H Broeckaert Nand N Langen Maarten M Hari Roshan R Kramarska Anna A Oikarinen Kasper K Hendrix Hanne H Lavigne Rob R van Noort Vera V

Bioinformatics (Oxford, England) 20240101 1

<h4>Summary</h4>Today, hundreds of post-translational modification (PTM) sites are routinely identified at once, but the comparison of new experimental datasets to already existing ones is hampered by the current inability to search most PTM databases at the protein residue level. We present FLAMS (Find Lysine Acylations and other Modification Sites), a Python3-based command line and web-tool that enables researchers to compare their PTM sites to the contents of the CPLM, the largest dedicated p ...[more]

PMID: 38195744

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Project description:IntroductionProtein acetylation is an extensively investigated post-translational modification (PTM). In addition to lysine acetylation, three new types of lysine acylations characterized by the presence of an acidic carboxylic group have been recently identified and validated. These included lysine malonylation (Kmal), lysine succinylation (Ksucc) and lysine glutarylation (Kglu). Pathogens belonging to the genus Mycobacterium elicit severe diseases in mammalian hosts through the modulation of energy metabolism pathways. Throughout this process, malonyl-CoA, succinyl-CoA and glutaryl-CoA are important intermediates in metabolic pathways, including the tricarboxylic acid (TCA) cycle, amino acid and lipid metabolism. These short-chain acyl-CoAs serve as substrates for corresponding acidic lysine acylation reactions. However, the landscape of these acyl-CoAs dependent acidic lysine acylomes remains unclear.MethodsWe used the high-affinity antibody enrichment combined with high-resolution LC-MS/MS analysis to systematically investigate the global proteomic characteristics of the three acidic lysine acylations in Mycobacterium smegmatis. Subsequently, we employed in vitro enzymatic assays to validate the functional impact of acylated substrates, adenylate kinase and proteasome-associated ATPase. Furthermore, we investigated the effects of overexpressing these two substrates on the in vitro growth of Mycobacterium smegmatis, its invasion of THP-1 cells, and the influence on inflammatory cytokines.ResultsWe systematically investigated the global substrate characterization of 1,703 lysine malonylated sites, 5,320 lysine succinylated sites and 269 lysine glutarylated sites in the non-pathogenic model strain Mycobacterium smegmatis. Bioinformatics analysis demonstrated a correlation between these acidic lysine acylations and the functional roles of ribosomes, in addition to their roles in various metabolic pathways. Furthermore, we investigated the impact of lysine acylations on the functional activity of adenylate kinase and proteasome-associated ATPase, as well as their roles in mycobacterial infection process.DiscussionCollectively, our study provided an important resource on substrate characterization and functional regulation of acidic lysine acylations in Mycobacterium smegmatis, giving valuable insights into their interrelation with the biology of infectious process.

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.

Project description:Recent proteomic studies discovered histone lysines are modified by acylations beyond acetylation. These acylations derive from acyl-CoA metabolites, potentially linking metabolism to transcription. Bromodomains bind lysine acylation on histones and other nuclear proteins to influence transcription. However, the extent bromodomains bind non-acetyl acylations is largely unknown. Also unclear are the effects of neighboring post-translational modifications, especially within heavily modified histone tails. Using peptide arrays, binding assays, sucrose gradients, and computational methods, we quantified 10 distinct acylations for binding to the bromodomain and extraterminal domain (BET) family. Four of these acylations (hydroxyisobutyrylation, malonylation, glutarylation, and homocitrullination) had never been tested for bromodomain binding. We found N-terminal BET bromodomains bound acetylated and propionylated peptides, consistent with previous studies. Interestingly, all other acylations inhibited binding of the BET bromodomains to peptides and nucleosomes. To understand how context tunes bromodomain binding, effects of neighboring methylation, phosphorylation, and acylation within histone H4 tails were determined. Serine 1 phosphorylation inhibited binding of the BRD4 N-terminal bromodomain to polyacetylated H4 tails by >5-fold, whereas methylation had no effect. Furthermore, binding of BRDT and BRD4 N-terminal bromodomains to H4K5acetyl was enhanced 1.4-9.5-fold by any neighboring acylation of lysine 8, indicating a secondary H4K8acyl binding site that is more permissive of non-acetyl acylations than previously appreciated. In contrast, C-terminal BET bromodomains exhibited 9.9-13.5-fold weaker binding for polyacylated than for monoacylated H4 tails, indicating the C-terminal bromodomains do not cooperatively bind multiple acylations. These results suggest acyl-CoA levels tune or block recruitment of the BET bromodomains to histones, linking metabolism to bromodomain-mediated transcription.

Dataset Information

FLAMS: Find Lysine Acylations and other Modification Sites.

Summary

Availability and implementation

Publications

FLAMS: Find Lysine Acylations and other Modification Sites.

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