AIMC Topic: Peptides

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iACP-DPNet: a dual-pooling causal dilated convolutional network for interpretable anticancer peptide identification.

Functional & integrative genomics
Anticancer peptides (ACPs) are acknowledged for their potential in cancer therapy, attributed to their safety, low side effects, and high target specificity. However, the discovery of ACPs is slowed by the high cost and labor-intensive nature of expe...

A genetic algorithm-based ensemble model for efficiently identifying interleukin 6 inducing peptides.

Scientific reports
Interleukin-6 (IL-6) is a cytokine with diverse biological activities that contribute to a variety of physiologic and immune responses. IL-6-inducing peptides are the short protein fragments that are critical for playing a contributing role in biolog...

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201.

PloS one
Class Ι major histocompatibility complexes (MHC-Ι), encoded by the highly polymorphic HLA-A, HLA-B, and HLA-C genes in humans, are expressed on all nucleated cells. Both self and foreign proteins are processed to peptides of 8-10 amino acids, loaded ...

From precision synthesis to cross-industry applications: The future of emerging peptide technologies.

Pharmacological research
Peptides, derived primarily from natural bioactive sources, play essential roles in human physiological processes such as hormone regulation and nerve signal transmission. Recent advances in phage display technology have revolutionized peptide screen...

generation of peptide binders with desired properties by deep generative models reinforced through enrichment of focused sets for iterative fine-tuning.

Chemical communications (Cambridge, England)
Recurrent neural networks underwent reinforcement procedures for generation of peptide binders with desired properties. Docking and scoring of peptides from these models allowed enrichment of focused sets with validated sequences for iterative fine-...

Identification of Polymeric Nanoparticles Using Strategic Peptide Sensor Configurations and Machine Learning.

ACS sensors
Environmental pollution by miniaturized plastics such as micro- and nanoplastics continues to escalate, posing serious risks to ecosystems and human health. Therefore, there is an urgent need to detect or identify the plastics. Although the technique...

Self-assembling peptide hydrogels: design, mechanisms, characterization, and biomedical applications.

Soft matter
Self-assembled peptide hydrogels have emerged as a research frontier in biomedical engineering due to their exceptional water-retention capacity and spatiotemporal drug release kinetics. Researchers can fabricate biomaterials with customizable struct...

Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.

Drug design, development and therapy
OBJECTIVE: Alzheimer's disease poses a significant threat to human health. Currenttherapeutic medicines, while alleviate symptoms, fail to reverse the disease progression or reduce its harmful effects, and exhibit toxicity and side effects such as ga...

AVP-HNCL: Innovative Contrastive Learning with a Queue-Based Negative Sampling Strategy for Dual-Phase Antiviral Peptide Prediction.

Journal of chemical information and modeling
Viral infections have long been a core focus in the field of public health. Antiviral peptides (AVPs), due to their unique mechanisms of action and significant inhibitory effects against a wide range of viruses, exhibit tremendous potential in protec...

Discovery of novel umami peptides and their bitterness masking effects from yellowfin tuna (Thunnus albacares) via peptidomics, multisensory evaluation, and molecular docking approaches.

Food chemistry
In this study, we identified and screened nine novel umami peptides derived from yellowfin tuna (Thunnus albacares) utilizing peptidomics combined with various machine learning based umami screening methodologies, including UMPred-FRL, TPDM, Umami-MR...