AIMC Topic: Protein Processing, Post-Translational

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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...

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...

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...

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...

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...

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...

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...