AIMC Topic: Peptides

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From prediction to design: Revealing the mechanisms of umami peptides using interpretable deep learning, quantum chemical simulations, and module substitution.

Food chemistry
This study screened and designed umami peptides using deep learning model and module substitution strategies. The predictive model, which integrates pre-training, enhanced feature, and contrastive learning module, achieved an accuracy of 0.94, outper...

SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq.

IEEE journal of biomedical and health informatics
In the Internet of Medical Things (IoMT), de novo peptide sequencing prediction is one of the most important techniques for the fields of disease prediction, diagnosis, and treatment. Recently, deep-learning-based peptide sequencing prediction has be...

Production of bioactive peptides by high-voltage pulsed electric field: Protein extraction, mechanism, research status and collaborative application.

Food chemistry
Bioactive peptides exhibit a variety of potential applications in the fields of medicine, food and cosmetics. However, studies have shown that the traditional preparation is characterized by low efficiency, substantial pollution, limited activities a...

Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.

Molecular diversity
Antiviral peptides (AVPs) represent a novel and promising therapeutic alternative to conventional antiviral treatments, due to their broad-spectrum activity, high specificity, and low toxicity. The emergence of zoonotic viruses such as Zika, Ebola, a...

Learning the rules of peptide self-assembly through data mining with large language models.

Science advances
Peptides are ubiquitous and important biomolecules that self-assemble into diverse structures. Although extensive research has explored the effects of chemical composition and exterior conditions on self-assembly, a systematic study consolidating the...

Mechanistic Study of Protein Interaction with Natto Inhibitory Peptides Targeting Xanthine Oxidase: Insights from Machine Learning and Molecular Dynamics Simulations.

Journal of chemical information and modeling
Bioactive peptides from food sources offer a safe and biocompatible approach to enzyme inhibition, with potential applications in managing metabolic disorders such as hyperuricemia and gout, conditions linked to excessive xanthine oxidase activity. U...

De novo design of self-assembling peptides with antimicrobial activity guided by deep learning.

Nature materials
Bioinspired materials based on self-assembling peptides are promising for tackling various challenges in biomedical engineering. While contemporary data-driven approaches have led to the discovery of self-assembling peptides with various structures a...

Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.

Interdisciplinary sciences, computational life sciences
The emergence of methicillin-resistant Staphylococcus aureus (MRSA) as a recognized cause of community-acquired and hospital infections has brought about a need for the efficient and accurate identification of peptides with anti-MRSA properties in dr...

SWAPS: A Modular Deep-Learning Empowered Peptide Identity Propagation Framework Beyond Match-Between-Run.

Journal of proteome research
Mass spectrometry (MS)-based proteomics relies heavily on MS/MS (MS2) data, which do not fully exploit the available MS1 information. Traditional peptide identity propagation (PIP) methods, such as match-between-runs (MBR), are limited to similar run...