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Peptides

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Empowering peptidomics: utilizing computational tools and approaches.

Bioanalysis
Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biolog...

Prediction of Cholecystokinin-Secretory Peptides Using Bidirectional Long Short-term Memory Model Based on Transfer Learning and Hierarchical Attention Network Mechanism.

Biomolecules
Cholecystokinin (CCK) can make the human body feel full and has neurotrophic and anti-inflammatory effects. It is beneficial in treating obesity, Parkinson's disease, pancreatic cancer, and cholangiocarcinoma. Traditional biological experiments are c...

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information.

IEEE journal of biomedical and health informatics
The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for o...

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy.

Journal of chemical information and modeling
Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reli...

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.

Molecular diversity
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this p...

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination.

International journal of biological macromolecules
Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds pro...

AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model.

Journal of chemical information and modeling
The prediction of peptide amyloidogenesis is a challenging problem in the field of protein folding. Large language models, such as the ProtBERT model, have recently emerged as powerful tools in analyzing protein sequences for applications, such as pr...

Multi-dimensional deep learning drives efficient discovery of novel neuroprotective peptides from walnut protein isolates.

Food & function
Neurodegenerative diseases, such as Alzheimer's and Parkinson's, are multi-factor induced neurological disorders that require management from multiple pathologies. The peptides from natural proteins with diverse physiological activity can be candidat...

MSBooster: improving peptide identification rates using deep learning-based features.

Nature communications
Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFrag...

PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction.

Journal of medicinal chemistry
The combination of library-based screening and artificial intelligence (AI) has been accelerating the discovery and optimization of hit ligands. However, the potential of AI to assist in de novo macrocyclic peptide ligand discovery has yet to be full...