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Peptides

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Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 ...

MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.

BMC genomics
BACKGROUND: Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database sea...

Antimicrobial peptide identification using multi-scale convolutional network.

BMC bioinformatics
BACKGROUND: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commo...

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.

International journal of molecular sciences
Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the p...

An antifouling peptide-based biosensor for determination of Streptococcus pneumonia markers in human serum.

Biosensors & bioelectronics
We report a peptide-based sensor that involves a multivalent interaction with L-ascorbate 6-phosphate lactonase (UlaG), a protein marker of Streptococcus pneumonia. By integrating the antifouling feature of the sensor, we significantly improved the s...

Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network.

Molecular informatics
Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In t...

MiPepid: MicroPeptide identification tool using machine learning.

BMC bioinformatics
BACKGROUND: Micropeptides are small proteins with length < = 100 amino acids. Short open reading frames that could produces micropeptides were traditionally ignored due to technical difficulties, as few small peptides had been experimentally confirme...

Peptide arrays incubated with three collections of human sera from patients infected with mosquito-borne viruses.

F1000Research
Global outbreaks caused by emerging or re-emerging arthropod-borne viruses (arboviruses) are becoming increasingly more common. These pathogens include the mosquito-borne viruses belonging to the and genera. These viruses often cause non-specific ...

DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.

Frontiers in immunology
Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In th...

Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid Peptide Sequencing Framework.

Molecular & cellular proteomics : MCP
Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpec...