AIMC Topic: Peptides

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Machine learning assisted design of highly active peptides for drug discovery.

PLoS computational biology
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning ap...

Prediction and analysis of quorum sensing peptides based on sequence features.

PloS one
Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. ...

Developing an artificial intelligence-generated peptide targeting platelet-type von Willebrand disease.

Blood advances
Platelet-type von Willebrand disease (PT-VWD) refers to a rare bleeding disorder caused by gain-of-function mutations in platelet glycoprotein Ibα (GPIbα). These mutations lead to a hyperactive protein-protein interaction (PPI) with von Willebrand fa...

AIRPred: A Deep Learning Model Predictor for Peptide Intensity Ratios in Cross-Linking Mass Spectrometry Improves Cross-Link Spectrum Matching.

Analytical chemistry
Cross-linking mass spectrometry (XL-MS) is a powerful tool in structural proteomics, offering insights into protein conformations, interactions and dynamics by linking spatially proximal residues. However, current cross-linked spectrum match (CSM) sc...

Sequence-driven species identification of ZooMS collagen peptide mass fingerprints.

Journal of proteomics
Developments in biomolecular species identification of animal tissues have been ongoing for decades, with collagen peptide mass fingerprinting becoming increasingly used in recent years. However, establishing confidence in the species biomarkers with...

DPP-IV inhibitory peptides from highland barley via machine learning and multi-scale validation.

Food chemistry
Highland barley has shown potential in regulating blood glucose and may serve as a natural source of dipeptidyl peptidase-IV (DPP-IV) inhibitors. In this study, machine learning (Gradient Boosting Decision Trees) and virtual screening were employed t...

Virtual screening of umami peptides during sufu ripening based on machine learning and molecular docking to umami receptor T1R1/T1R3.

Food chemistry
Umami peptides might significantly contribute to the taste of sufu. However, the inefficiencies of traditional identification methods had great limitations. This study explored a new approach for umami peptides characterization in sufu. Combining pep...

EnsemPred-ACP: Combining machine and deep learning to improve anticancer peptide prediction.

Computers in biology and medicine
Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to healthy cells. However, the accurate computational prediction of ACP remains challenging because of...

DeepMS: super-fast peptide identification using end-to-end deep learning method.

Journal of molecular biology
Mass spectrometry (MS) has emerged as a powerful omics analysis technique, particularly in proteomics, where the initial step involves identifying MS spectra as peptide sequences. However, this process often requires substantial computational resourc...

Heterojunction nanofluidic memristors based on peptide chain valves for neuromorphic applications.

Biosensors & bioelectronics
Memristors exhibit significant potential for neuromorphic computing due to their unique properties. This study introduces a heterojunction nanofluidic memristor (HJNFM) and explores its applications in simulating synapses and constructing neural netw...