AIMC Topic: Peptides

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ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from ther...

SPEQ: quality assessment of peptide tandem mass spectra with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may...

Accelerating bioactive peptide discovery via mutual information-based meta-learning.

Briefings in bioinformatics
Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting i...

Identifying multi-functional bioactive peptide functions using multi-label deep learning.

Briefings in bioinformatics
The bioactive peptide has wide functions, such as lowering blood glucose levels and reducing inflammation. Meanwhile, computational methods such as machine learning are becoming more and more important for peptide functions prediction. Most of the pr...

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.

Briefings in bioinformatics
Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COV...

Machine Learning Prediction of Antimicrobial Peptides.

Methods in molecular biology (Clifton, N.J.)
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in c...

Diversified Applications of Self-assembled Nanocluster Delivery Systems- A State-ofthe- art Review.

Current pharmaceutical design
BACKGROUND: For the nanoparticulate system and the transportation of cellular elements for the fabrication of microelectronic devices, self-assembled nanoclusters arrange the components into an organized structure. Nanoclusters reduce transcytosis an...

CNN-PepPred: an open-source tool to create convolutional NN models for the discovery of patterns in peptide sets-application to peptide-MHC class II binding prediction.

Bioinformatics (Oxford, England)
SUMMARY: The ability to unveil binding patterns in peptide sets has important applications in several biomedical areas, including the development of vaccines. We present an open-source tool, CNN-PepPred, that uses convolutional neural networks to dis...

PreTP-EL: prediction of therapeutic peptides based on ensemble learning.

Briefings in bioinformatics
Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different cha...

PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture.

Briefings in bioinformatics
The prediction of peptide secondary structures is fundamentally important to reveal the functional mechanisms of peptides with potential applications as therapeutic molecules. In this study, we propose a multi-view deep learning method named Peptide ...