Protein Processing, Post-Translational

THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model.

Bioinformatics (Oxford, England) Nov 1, 2023

MOTIVATION: Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the pa...

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

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PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk.

Briefings in bioinformatics Mar 19, 2023

Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk...

Protein Domains Signal Transduction Protein Processing, Post-Translational Neural Networks, Computer Proteome

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FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.

Briefings in bioinformatics Nov 19, 2022

Glutarylation is a post-translational modification which plays an irreplaceable role in various functions of the cell. Therefore, it is very important to accurately identify the glutarylation substrates and its corresponding glutarylation sites. In r...

Amino Acids Support Vector Machine Supervised Machine Learning Protein Processing, Post-Translational Algorithms Computational Biology

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PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection.

Briefings in bioinformatics Sep 20, 2022

A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular me...

Computational Biology Support Vector Machine Humans Algorithms Amino Acid Sequence Lysine Protein Processing, Post-Translational Proteins

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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 Mar 10, 2022

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...

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

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BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.

Bioinformatics (Oxford, England) Jan 12, 2022

MOTIVATION: As one of the most important post-translational modifications (PTMs), protein lysine crotonylation (Kcr) has attracted wide attention, which involves in important physiological activities, such as cell differentiation and metabolism. Howe...

Machine Learning Natural Language Processing Language Lysine Protein Processing, Post-Translational

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

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022

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...

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

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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.) Jan 1, 2022

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...

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

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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.) Jan 1, 2022

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...

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

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PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information.

Bioinformatics (Oxford, England) Dec 11, 2021

MOTIVATION: Phosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but...

Proteins Amino Acid Sequence Neural Networks, Computer Phosphorylation Protein Processing, Post-Translational

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Protein Processing, Post-Translational

THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model.

PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk.

FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.

PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection.

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

BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.

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.

PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information.

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