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Peptides

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Feature selection enhances peptide binding predictions for TCR-specific interactions.

Frontiers in immunology
INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for a...

Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.

Briefings in bioinformatics
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I m...

TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

Bioinformatics (Oxford, England)
MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding ...

AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides.

Viruses
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2...

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.

IET systems biology
Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy of naturally derived peptides in reducing blood pressure. Hypertension is one of the r...

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.

Analytical chemistry
Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database sear...

IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network.

Biomolecules
Interleukin-6 (IL-6) is a potent glycoprotein that plays a crucial role in regulating innate and adaptive immunity, as well as metabolism. The expression and release of IL-6 are closely correlated with the severity of various diseases. IL-6-inducing ...

Artificial intelligence in peptide-based drug design.

Drug discovery today
Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promi...

PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.

Journal of proteome research
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, d...

From Sea Cucumbers to Soft Robots: A Photothermal-Responsive Hydrogel Actuator with Shape Memory.

ACS applied materials & interfaces
Soft robotics has undergone considerable progress driven by materials that can effectively transduce external stimuli into mechanical actuation. Here, we report the development of a photothermal-responsive hydrogel actuator with shape memory capabili...