AIMC Topic: Peptides

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In Silico tool for predicting, designing and scanning IL-2 inducing peptides.

Scientific reports
Interleukin-2 (IL-2) based immunotherapy has been approved for treating certain types of cancer, as IL-2 plays a crucial role in regulating the immune system. In this study, we developed a method for predicting IL-2-inducing peptides. Our method was ...

A hybrid framework of generative deep learning for antiviral peptide discovery.

Scientific reports
Antiviral peptides (AVPs) hold great potential for combating viral infections, yet their discovery and development remain challenging. In this study, we present a hybrid model combining Wasserstein Generative Adversarial Networks with Gradient Penalt...

BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction.

BMC biology
BACKGROUND: Diabetes is a global metabolic disease that urgently calls for the development of new and effective therapeutic agents. Anti-diabetic peptides (ADPs) have emerged as a research hotspot due to their therapeutic potential and natural safety...

NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides.

BMC biology
BACKGROUND: Accurate identification of anti-inflammatory peptides (AIPs) is crucial for drug development and inflammatory disease treatment. However, the short length and limited informational content of peptide sequences make precise computational r...

Machine learning-guided anti-photoaging peptides from Chinese giant salamander skin: Efficient preparation and mechanistic insights.

Food chemistry
Collagen peptides are ubiquitously applied in food systems for their versatile bioactivities but face constraints from labor-intensive enzymatic screening and zoonotic risks from terrestrial sources. This study developed machine learning (ML) models ...

Cysteine pattern barcoding-based dataset filtration enhances the machine learning-assisted interpretation of Conus venom peptide therapeutics.

PloS one
Crude cone snail venom is a rich source of bioactive compounds with significant therapeutic potential. In this study, we conducted a comprehensive analysis of 5,985 cone snail peptides across 82 Conus species to identify unique cysteine (Cys) pattern...

StackPIP: An Effective Computational Framework for Accurate and Balanced Identification of Proinflammatory Peptides.

Journal of chemical information and modeling
Proinflammatory peptides (PIPs) play a crucial role in immune response modulation by orchestrating cytokine release and leukocyte recruitment. Accurate identification of PIPs is essential for understanding inflammation-related diseases and developing...

Integrating Protein Language Models and Geometric Deep Learning for Peptide Toxicity Prediction.

Journal of chemical information and modeling
Peptide toxicity prediction is a critical task in biomedical research, influencing drug safety and therapeutic development. Traditional methods, relying on sequence similarity or handcrafted features, struggle to capture the complex relationship betw...

Application of MALDI-TOF MS-based peptidome profiling for the identification of Bacillus cereus, Staphylococcus aureus, and Escherichia coli in single and mixed inoculum.

Food chemistry
The detection of mixed-species bacterial samples plays a vital role in ensuring food safety, yet research in this area remains notably limited. This study investigates the integration of MALDI-TOF MS-derived peptidome profiles with artificial intelli...

Deep learning-derived optimal annotation strategies to power the systematic mapping of peptide space.

Food chemistry
Rapid and reliable peptide identification techniques are essential for proteomics. High-resolution tandem mass spectrometry acquires a large amount of data through data-dependent acquisition (DDA) and data-independent acquisition (DIA), but tradition...