AIMC Topic: Peptides
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Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.
Journal of chemical information and modeling
Feb 1, 2023
Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, . It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous ...
Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.
PLoS computational biology
Jan 20, 2023
Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains ...
RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2022
The abuse of traditional antibiotics has led to an increase in the resistance of bacteria and viruses. Similar to the function of antibacterial peptides, bacteriocins are more common as a kind of peptides produced by bacteria that have bactericidal o...
Prediction Models for Identifying Ion Channel-Modulating Peptides via Knowledge Transfer Approaches.
IEEE journal of biomedical and health informatics
Dec 7, 2022
Ion channels, which can be modulated by peptides, are promising drug targets for neurological, metabolic, and cardiovascular disorders. Because it is expensive and labor-intensive to experimentally screen ion channel-modulating peptides (IMPs), in-si...
AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics.
Nature communications
Nov 24, 2022
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid ...
Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach.
Nanoscale
Nov 17, 2022
Designing nano-engineered particles capable of the delivery of therapeutic and diagnostic agents to a specific target remains a significant challenge. Understanding how interactions between particles and cells are impacted by the physicochemical prop...
Prediction, Discovery, and Characterization of Plant- and Food-Derived Health-Beneficial Bioactive Peptides.
Nutrients
Nov 14, 2022
Nature may have the answer to many of our questions about human, animal, and environmental health. Natural bioactives, especially when harvested from sustainable plant and food sources, provide a plethora of molecular solutions to nutritionally actio...
Deep learning drives efficient discovery of novel antihypertensive peptides from soybean protein isolate.
Food chemistry
Oct 20, 2022
As a potential and effective substitute for the drugs of antihypertension, the food-derived antihypertensive peptides have arisen great interest in scholars recently. However, the traditional screening methods for antihypertensive peptides are at con...
Combining mass spectrometry and machine learning to discover bioactive peptides.
Nature communications
Oct 20, 2022
Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the lit...
PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability.
International journal of molecular sciences
Oct 16, 2022
Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful f...
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