AIMC Topic: Threonine

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DOGpred: A Novel Deep Learning Framework for Accurate Identification of Human O-linked Threonine Glycosylation Sites.

Journal of molecular biology Feb 1, 2025
O-linked glycosylation is a crucial post-translational modification that regulates protein function and biological processes. Dysregulation of this process is associated with various diseases, underscoring the need to accurately identify O-linked gly...
Humans Neural Networks, Computer Protein Processing, Post-Translational Databases, Protein Threonine Computational Biology Glycosylation Deep Learning
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DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Proteins Sep 6, 2024
Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulati...
Threonine Phosphoproteins Serine Computational Biology Neural Networks, Computer Protein Processing, Post-Translational Phosphorylation Software Databases, Protein Bacterial Proteins Deep Learning
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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...
Deep Learning Proteome Chromatography, Liquid Phosphoproteins Tandem Mass Spectrometry Serine Protein Processing, Post-Translational Phosphorylation Threonine Chlamydomonas reinhardtii
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Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins Aug 22, 2019
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...
Sequence Analysis, Protein Threonine Protein Conformation Species Specificity Binding Sites Serine Tyrosine Machine Learning Databases, Protein Algorithms Phosphorylation Humans Animals Models, Molecular Computational Biology Protein Processing, Post-Translational Reproducibility of Results Proteins
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Broiler responses to digestible threonine at different ages: a neural networks approach.

Journal of animal physiology and animal nutrition Aug 6, 2015
Three experiments were conducted with broiler chickens to evaluate the effects of digestible threonine (DThr) and crude protein (CP) on their performance at three different phases of age: 1-14, 15-28 and 29-42 days. The measured traits included the f...
Animal Nutritional Physiological Phenomena Female Threonine Animals Male Neural Networks, Computer Chickens Models, Biological Animal Feed Aging Diet
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Using support vector machines to identify protein phosphorylation sites in viruses.

Journal of molecular graphics & modelling Dec 24, 2014
Phosphorylation of viral proteins plays important roles in enhancing replication and inhibition of normal host-cell functions. Given its importance in biology, a unique opportunity has arisen to identify viral protein phosphorylation sites. However, ...
Threonine Phosphorylation Molecular Sequence Data Tyrosine Phosphoproteins Support Vector Machine Serine Viral Proteins Amino Acid Sequence Databases, Protein Protein Structure, Tertiary
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iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines.

Molecular bioSystems Jul 25, 2017
Protein phosphorylation plays a potential role in regulating protein conformation and functions. As a result, identifying an uncharacterized protein sequence as a phosphorylated protein is a very meaningful problem and an urgent issue for both basic ...
Support Vector Machine Protein Processing, Post-Translational Phosphorylation Internet Proteins Tyrosine Humans Serine Software Benchmarking Threonine Computational Biology Datasets as Topic
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