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Peptides

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DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.

Analytical chemistry
Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the p...

A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides.

Molecules (Basel, Switzerland)
Food protein-derived antihypertensive peptides are a representative type of bioactive peptides. Several models based on partial least squares regression have been constructed to delineate the relationship between the structure and activity of the pep...

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.

Immunity
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomple...

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery.

Expert opinion on drug discovery
INTRODUCTION: Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread of artificial intelligence and machine learning complemented with experime...

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing.

Genes
Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design ...

CysPresso: a classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides.

BMC bioinformatics
BACKGROUND: Cysteine-dense peptides (CDPs) are an attractive pharmaceutical scaffold that display extreme biochemical properties, low immunogenicity, and the ability to bind targets with high affinity and selectivity. While many CDPs have potential a...

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking.

Journal of chemical information and modeling
The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide intera...

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Analytical chemistry
Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, w...

Discovery of the Lanthipeptide Curvocidin and Structural Insights into its Trifunctional Synthetase CuvL.

Angewandte Chemie (International ed. in English)
Lanthipeptides are ribosomally-synthesized natural products from bacteria featuring stable thioether-crosslinks and various bioactivities. Herein, we report on a new clade of tricyclic class-IV lanthipeptides with curvocidin from Thermomonospora curv...

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches.

International journal of molecular sciences
Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protei...