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Peptides

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Prediction Models for Identifying Ion Channel-Modulating Peptides via Knowledge Transfer Approaches.

IEEE journal of biomedical and health informatics
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
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
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
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
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
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
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...

Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections.

IEEE journal of biomedical and health informatics
Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning e...

Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation.

Journal of computational chemistry
Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applicatio...

AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction.

Methods (San Diego, Calif.)
In recent years, anticancer peptides have emerged as a new viable option in cancer therapy, with the ability to overcome the considerable side effects and poor outcomes of standard cancer therapies. Accurate anticancer peptide identification can faci...