AIMC Topic: Peptides
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Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.
Nature communications
May 28, 2021
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...
Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.
International journal of molecular sciences
May 26, 2021
Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop ...
Deep learning neural network tools for proteomics.
Cell reports methods
May 17, 2021
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategie...
pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning.
Journal of proteome research
Apr 6, 2021
In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accura...
AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides.
Journal of chemical information and modeling
Mar 31, 2021
Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applicatio...
Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.
Proteins
Mar 18, 2021
Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better predict...
Machine learning to determine optimal conditions for controlling the size of elastin-based particles.
Scientific reports
Mar 18, 2021
This paper evaluates the aggregation behavior of a potential drug and gene delivery system that combines branched polyethyleneimine (PEI), a positively-charged polyelectrolyte, and elastin-like polypeptide (ELP), a recombinant polymer that exhibits l...
Screening of a novel free fatty acid receptor 1 (FFAR1) agonist peptide by phage display and machine learning based-amino acid substitution.
Biochemical and biophysical research communications
Mar 9, 2021
Free fatty acid receptor 1 (FFAR1 or GPR40) has attracted attention for the treatment of type 2 diabetes mellitus, and various small-molecule agonists have been developed. However, most FFAR1 agonists as well as endogenous ligands, such as linoleic a...
Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.
Journal of the American Chemical Society
Mar 8, 2021
Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-li...
Fluorescence Polarization
Nuclear Magnetic Resonance, Biomolecular
NF-E2-Related Factor 2
Cyclization
Kelch-Like ECH-Associated Protein 1
Mutagenesis, Site-Directed
Amino Acid Motifs
Recombinant Proteins
Machine Learning
Humans
Structure-Activity Relationship
Peptides
Protein Binding
Crystallography, X-Ray
Hydrogen Bonding
Deep learning the collisional cross sections of the peptide universe from a million experimental values.
Nature communications
Feb 19, 2021
The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million dat...
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