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Protein Processing, Post-Translational

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A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.

Genomics, proteomics & bioinformatics
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that...

Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme.

Biochimica et biophysica acta. Reviews on cancer
Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PT...

Machine learning-based approaches for ubiquitination site prediction in human proteins.

BMC bioinformatics
Protein ubiquitination is a critical post-translational modification (PTMs) involved in numerous cellular processes. Identifying ubiquitination sites (Ubi-sites) on proteins offers valuable insights into their function and regulatory mechanisms. Due ...

Label-free identification of protein aggregates using deep learning.

Nature communications
Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington's disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescen...

DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features.

Journal of chemical information and modeling
Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of...

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

Bioinformatics (Oxford, England)
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...

Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore.

ACS nano
Protein post-translational modifications (PTMs) play a crucial role in countless biological processes, profoundly modulating protein properties on both spatial and temporal scales. Protein PTMs have also emerged as reliable biomarkers for several dis...

O-GlcNAcPRED-DL: Prediction of Protein O-GlcNAcylation Sites Based on an Ensemble Model of Deep Learning.

Journal of proteome research
O-linked β--acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins, regulating a plethora of physiological and pathological events. As a dynamic process, O-GlcNAc functions i...

Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.

Database : the journal of biological databases and curation
The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety...

PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.

Interdisciplinary sciences, computational life sciences
The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological f...