AIMC Topic: Peptides

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Virtual Hydrolysis-Based Screening of Wheat-Derived DPP-IV Inhibitory Peptides: A Mechanistic Analysis Integrating Cell Experiments and Molecular Dynamics Simulations.

Journal of agricultural and food chemistry
Dipeptidyl peptidase-IV (DPP-IV) inhibitors play a critical role in the treatment of diabetes and metabolic diseases. This study combines computational simulations with experimental validation to identify peptides with potential DPP-IV inhibitory act...

PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scores.

Protein science : a publication of the Protein Society
Within the central nervous system, the myelin sheath is composed of elements known as myelin autoantigens that are mistakenly targeted by the immune system in multiple sclerosis (MS). This autoimmune attack leads to the destruction of myelin, resulti...

A large language model for predicting neurotoxic peptides and neurotoxins.

Protein science : a publication of the Protein Society
The accurate prediction of neurotoxicity in peptides and proteins is essential for the safety evaluation of therapeutic proteins and genetically modified (GM) organisms. Existing tools, including our earlier method NTxPred, typically use a single pre...

Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: T cells play a vital role in the immune system by recognizing and eliminating infected or cancerous cells, thus driving adaptive immune responses. Their activation is triggered by the binding of T cell receptors (TCRs) to ep...

pACPs-DNN: Predicting anticancer peptides using novel peptide transformation into evolutionary and structure matrix-based images with self-attention deep learning model.

Computational biology and chemistry
Globally, cancer remains a major health challenge due to its high mortality rates. Traditional experimental approaches and therapies are resource-intensive and often cause significant side effects. Anticancer peptides (ACPs) have emerged as alternati...

Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration.

Food chemistry
This review explores the health benefits of bioactive peptides (BAPs) from fermented milk proteins, emphasizing the transformative role of artificial intelligence (AI) and machine learning (ML) in advancing this field. BAPs exhibit diverse biological...

iAVP-RFVOT: Identify Antiviral Peptides by Random Forest Voting Machine Learning with Unified Manifold Learning Embedded Features.

Biochemistry
Viruses are transmitted through multiple routes and can cause a wide range of diseases. Antiviral peptides (AVPs) have emerged as a cost-effective and low-side-effect strategy for combating viral infections. However, identifying antiviral peptides ex...

Assessing Substrate Scope of the Cyclodehydratase LynD by mRNA Display-Enabled Machine Learning Models.

Biochemistry
Many members of the broad family of enzymes, known as YcaOs have been shown to install azoline heterocycles post-translationally into peptide substrates. These moieties can help rigidify structures and contribute to the potent bioactivities of the ev...

Structure-based artificial intelligence-aided design of MYC-targeting degradation drugs for cancer therapy.

Biochemical and biophysical research communications
The MYC protein is an oncoprotein that plays a crucial role in various cancers. Although its significance has been well recognized in research, the development of drugs targeting MYC remains relatively slow. In this study, we developed a novel MYC pe...

PLPTP: A Motif-based Interpretable Deep Learning Framework Based on Protein Language Models for Peptide Toxicity Prediction.

Journal of molecular biology
Peptide toxicity prediction holds significant importance in drug development and biotechnology, as accurately identifying toxic peptide sequences is crucial for designing safer peptide-based drugs. This study proposes a deep learning-based model for ...