AIMC Topic: Proteome
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PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms.
Nature communications
Dec 11, 2024
Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards p...
Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.
Journal of agricultural and food chemistry
Nov 13, 2024
Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Cali...
Physics-Based Machine Learning Trains Hamiltonians and Decodes the Sequence-Conformation Relation in the Disordered Proteome.
Journal of chemical theory and computation
Nov 6, 2024
Intrinsically disordered proteins and regions (IDPs) are involved in vital biological processes. To understand the IDP function, often controlled by conformation, we need to find the link between sequence and conformation. We decode this link by inte...
Protein interactions in human pathogens revealed through deep learning.
Nature microbiology
Sep 18, 2024
Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lit...
Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Mucus Proteome.
Marine drugs
Aug 26, 2024
Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and traditionally challenging infections. However, traditional drug discovery methods face challenges such a...
Deep Learning Powers Protein Identification from Precursor MS Information.
Journal of proteome research
Aug 21, 2024
Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain unselected for MS/MS fragmentation, especially in the cases of low abundance samples and wide abundan...
Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.
Journal of proteome research
Aug 16, 2024
Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learn...
High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula).
Food research international (Ottawa, Ont.)
Aug 8, 2024
Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to p...
pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.
Life science alliance
Jul 30, 2024
High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologie...
Host-derived protein profiles of human neonatal meconium across gestational ages.
Nature communications
Jul 17, 2024
Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted ...
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