Protein Processing, Post-Translational

Deep learning approaches for data-independent acquisition proteomics.

Expert review of proteomics 34918987

INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

Deep Learning Humans Peptides Protein Processing, Post-Translational Proteome Proteomics

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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.

Methods in molecular biology (Clifton, N.J.) 35696087

Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage...

Deep Learning Mass Spectrometry Protein Processing, Post-Translational Proteins Proteomics

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Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models - Development, Validation, and Interpretation.

Methods in molecular biology (Clifton, N.J.) 35696084

Protein posttranslational modifications (PTMs) are a rapidly expanding feature class of significant importance in cell biology. Due to a high burden of experimental proof, the number of functionals PTMs in the eukaryotic proteome is currently underes...

Machine Learning Neural Networks, Computer Protein Processing, Post-Translational Proteome

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Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.

Methods in molecular biology (Clifton, N.J.) 35696081

Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. He...

Amino Acid Sequence Animals Computational Biology Glycosylation Humans Mice Protein Processing, Post-Translational Proteins Support Vector Machine

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Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.

Oxidative medicine and cellular longevity 35281472

BACKGROUND: Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signature...

Female Gene Expression Regulation, Neoplastic Gene Regulatory Networks Glycosylation Humans Machine Learning Middle Aged Ovarian Neoplasms Prognosis Protein Processing, Post-Translational

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Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.

Scientific reports 34996975

In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrom...

Amino Acid Sequence Computational Biology Deep Learning Glutamic Acid Models, Molecular Protein Conformation Protein Processing, Post-Translational Proteins Reproducibility of Results Structure-Activity Relationship

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Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

Briefings in bioinformatics 35189635

Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. H...

Algorithms Computational Biology Databases, Factual Deep Learning Lysine Neural Networks, Computer Phosphorylation Protein Processing, Post-Translational Reproducibility of Results ROC Curve Software User-Computer Interface

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TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.

International journal of molecular sciences 35457080

Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational pr...

Deep Learning Neural Networks, Computer Phosphorylation Protein Processing, Post-Translational Proteins

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Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods.

IEEE journal of biomedical and health informatics 34762593

Protein s-nitrosylation (SNO) is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immu...

Cysteine Deep Learning Humans Nitric Oxide Protein Processing, Post-Translational

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A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

Scientific reports 35794165

AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino a...

Amino Acid Sequence Amino Acids Machine Learning Neural Networks, Computer Protein Processing, Post-Translational

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Protein Processing, Post-Translational

Deep learning approaches for data-independent acquisition proteomics.

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.

Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models - Development, Validation, and Interpretation.

Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.

Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.

Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods.

A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

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