AIMC Topic: Protein Processing, Post-Translational

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Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models.

Journal of chemical information and modeling Jul 11, 2022
Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, ma...
Phosphorylation Acetylation Protein Processing, Post-Translational Proteins Deep Learning
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A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

Scientific reports Jul 6, 2022
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...
Protein Processing, Post-Translational Amino Acids Amino Acid Sequence Neural Networks, Computer Machine Learning
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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 May 5, 2022
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...
Deep Learning Humans Nitric Oxide Cysteine Protein Processing, Post-Translational
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TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.

International journal of molecular sciences Apr 12, 2022
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...
Neural Networks, Computer Deep Learning Phosphorylation Protein Processing, Post-Translational Proteins
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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 Mar 4, 2022
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...
Protein Processing, Post-Translational Ovarian Neoplasms Humans Gene Expression Regulation, Neoplastic Prognosis Glycosylation Female Middle Aged Machine Learning Gene Regulatory Networks
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Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.

Scientific reports Jan 7, 2022
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...
Structure-Activity Relationship Models, Molecular Protein Processing, Post-Translational Proteins Glutamic Acid Amino Acid Sequence Protein Conformation Deep Learning Computational Biology Reproducibility of Results
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Deep learning approaches for data-independent acquisition proteomics.

Expert review of proteomics Dec 28, 2021
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.
Protein Processing, Post-Translational Proteomics Proteome Peptides Deep Learning Humans
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DeepLC can predict retention times for peptides that carry as-yet unseen modifications.

Nature methods Oct 28, 2021
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction mod...
Algorithms Peptide Mapping Deep Learning Humans Proteins Proteome Peptide Fragments Protein Processing, Post-Translational Datasets as Topic
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Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques.

Biochimie Oct 7, 2021
Lysine formylation is a newly discovered and mostly interested type of post-translational modification (PTM) that is generally found on core and linker histone proteins of prokaryote and eukaryote and plays various important roles on the regulation o...
Protein Processing, Post-Translational Lysine Acylation Histones Animals Support Vector Machine Humans
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A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites.

Scientific reports Jun 15, 2021
Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphoryl...
Chlamydomonas reinhardtii Chromatography, Liquid Phosphorylation Deep Learning Serine Tandem Mass Spectrometry Proteome Phosphoproteins Threonine Protein Processing, Post-Translational
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