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Peptides

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Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning.

Journal of chemical information and modeling
Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, ...

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.

Scientific reports
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and...

IF-AIP: A machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy.

Computers in biology and medicine
BACKGROUND: The most commonly used therapy currently for inflammatory and autoimmune diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects. Recently, some anti-inflammatory peptides (AIPs) have been found to be a ...

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

Frontiers in immunology
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic reg...

Use of tree-based machine learning methods to screen affinitive peptides based on docking data.

Molecular informatics
Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four differen...

The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.

Acta crystallographica. Section D, Structural biology
Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedu...

Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries.

Journal of proteome research
Spectral libraries are useful resources in proteomic data analysis. Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences. This enables predicted spectral libraries to be compiled, and ...

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Food research international (Ottawa, Ont.)
Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypert...

Identification of Water-Soluble Polymers through Machine Learning of Fluorescence Signals from Multiple Peptide Sensors.

ACS applied bio materials
Recently, there has been growing concern about the discharge of water-soluble polymers (especially synthetic polymers) into the environment. Therefore, the identification of water-soluble polymers in water samples is becoming increasingly crucial. In...

Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Self-assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self-assembling properties of peptides within the complete sequence space due to the enormous sequence quanti...