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Protein Processing, Post-Translational

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Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction.

PeerJ
Ubiquitination is an important post-translational modification of proteins that regulates many cellular activities. Traditional experimental methods for identification are costly and time-consuming, so many researchers have proposed computational met...

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

Journal of chemical information and modeling
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...

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

Briefings in bioinformatics
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...

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

Briefings in bioinformatics
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...

Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine.

Analytical biochemistry
Protein 3-hydroxyl-3-methylglutarylation (HMGylation) is newly discovered lysine acylation modification in mitochondrion. The accurate identification of HMGylation sites is the premise and key to further explore the molecular mechanisms of HMGylation...

MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases.

Cell reports methods
We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enabl...

Multiplex Identification of Post-Translational Modifications at Point-of-Care by Deep Learning-Assisted Hydrogel Sensors.

Angewandte Chemie (International ed. in English)
Multiplex detection of protein post-translational modifications (PTMs), especially at point-of-care, is of great significance in cancer diagnosis. Herein, we report a machine learning-assisted photonic crystal hydrogel (PCH) sensor for multiplex dete...

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

Briefings in bioinformatics
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...

Large-Scale Modeling of Sparse Protein Kinase Activity Data.

Journal of chemical information and modeling
Protein kinases are a protein family that plays an important role in several complex diseases such as cancer and cardiovascular and immunological diseases. Protein kinases have conserved ATP binding sites, which when targeted can lead to similar acti...

LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model.

Journal of proteome research
Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harne...