Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Iannetta, Anthony A.; Hicks, Leslie M.

doi:10.1007/978-1-0716-2317-6_1

Cited by 3 publications

(1 citation statement)

References 272 publications

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“…Although traditional experimental methods have laid a good data foundation for the accumulation of Kglu data, their time-consuming and laborious shortcomings still cannot meet the needs of scienti c development. Computational-based approaches for predicting PTM sites in proteins have drawn more attention as high throughput sequencing and machine learning(ML) have advanced [5] .…”

Section: Introductionmentioning

confidence: 99%

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

Liu

Zhu

Dai

et al. 2023

Preprint

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Background Lysine glutarylation(Kglu) is one of the most important Post-translational modifications(PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiment, computational-based Kglu site prediction research are gaining more and more attention.Results In this study, we proposed a new model named GBDT_KgluSite to identify Kglu and non-Kglu sequences based on the gradient-boosting decision tree (GBDT). Here, sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. The imbalance between positive and negative samples was addressed using the NearMiss-3 approach, and extraneous data was eliminated using the Elastic Net. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively.Conclusion GBDT_KgluSite is an effectively computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in Mus musculus protein. The code and dataset of GBDT_KgluSite is available at https://github.com/flyinsky6/GBDT_KgluSite.

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Section: Introductionmentioning

confidence: 99%

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

Liu

Zhu

Dai

et al. 2023

Preprint

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Exploring the landscape of post-translational modification in drug discovery

Cao,

Yu,

Zhu

et al. 2025

Pharmacology & Therapeutics

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Post‐translational modifications of proteins in cardiovascular diseases examined by proteomic approaches

Stastna

2024

The FEBS Journal

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Over 400 different types of post‐translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)‐based proteomics. MS‐based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low‐abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning‐ and deep learning‐based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS‐based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.

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Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Cited by 3 publications

References 272 publications

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

Exploring the landscape of post-translational modification in drug discovery

Post‐translational modifications of proteins in cardiovascular diseases examined by proteomic approaches

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