AIMC Topic: Protein Processing, Post-Translational

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A machine learning-based framework for modeling transcription elongation.

Proceedings of the National Academy of Sciences of the United States of America Feb 9, 2021
RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gen...
Humans Base Sequence Machine Learning Genome, Human Epigenesis, Genetic Histones DNA Methylation HeLa Cells Protein Processing, Post-Translational HEK293 Cells Models, Biological RNA Splicing RNA Splice Sites Transcription Elongation, Genetic Binding Sites RNA Polymerase II Nucleotide Motifs
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Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein k...
Neural Networks, Computer Proteome Protein Processing, Post-Translational Phosphoric Monoester Hydrolases Phosphorylation Protein Kinases Substrate Specificity
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DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder.

Protein and peptide letters Jan 1, 2021
BACKGROUND: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcription...
Machine Learning Deep Learning Proteins Computational Biology Databases, Protein Protein Processing, Post-Translational Sulfenic Acids
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DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins.

Molecular omics Oct 12, 2020
Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine ...
Methylation Arginine Reproducibility of Results Deep Learning ROC Curve Protein Processing, Post-Translational Proteins Databases, Protein Neural Networks, Computer Algorithms
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MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization.

Nucleic acids research Jul 2, 2020
MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which res...
Sequence Analysis, Protein Protein Conformation Computer Graphics Software Proteins Deep Learning Models, Molecular Protein Processing, Post-Translational Internet
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The neXtProt knowledgebase in 2020: data, tools and usability improvements.

Nucleic acids research Jan 8, 2020
The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describ...
Peptides User-Computer Interface Sequence Analysis, RNA Mass Spectrometry Protein Processing, Post-Translational Proteins Humans Software Databases, Protein Internet Protein Kinases Knowledge Bases Trypsin
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Prediction of S-nitrosylation sites by integrating support vector machines and random forest.

Molecular omics Dec 2, 2019
Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes. It is associated with redox-based cellular signaling to protect against oxidative stress. The identification of...
Cysteine Support Vector Machine Protein Processing, Post-Translational Software Algorithms Computational Biology
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Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

Briefings in bioinformatics Nov 27, 2019
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 id...
Machine Learning Algorithms Computational Biology Protein Processing, Post-Translational Feasibility Studies Lysine
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SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties.

Bioinformatics (Oxford, England) Oct 15, 2019
MOTIVATION: Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization...
Humans Protein Processing, Post-Translational Animals Glycoproteins Mice Glycosylation Support Vector Machine
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DeepPhos: prediction of protein phosphorylation sites with deep learning.

Bioinformatics (Oxford, England) Aug 15, 2019
MOTIVATION: Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but m...
Deep Learning Neural Networks, Computer Software Protein Processing, Post-Translational Phosphorylation
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