AIMC Topic: Peptides

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AI, docking, and molecular dynamics to track the binding of structural peptides to different keratin models.

International journal of biological macromolecules
The present work shows a computational approach to assess the interactions of different nature-inspired peptides with hair keratin models. An updated keratin model was validated, and comparisons with previous models were traced, thereby highlighting ...

Novel Computational Approaches in the Discovery and Identification of Bioactive Peptides: A Bioinformatics Perspective.

Journal of agricultural and food chemistry
Bioactive peptides are protein molecules known for their specific biological functions, offering promising applications across various fields including medicine, food, and cosmetics. Traditional approaches to the investigation of bioactive peptides t...

Immunopeptidomics-guided discovery and characterization of neoantigens for personalized cancer immunotherapy.

Science advances
Neoantigens have emerged as ideal targets for personalized cancer immunotherapy. We depict the pan-cancer peptide atlas by comprehensively collecting immunopeptidomics from 531 samples across 14 cancer and 29 normal tissues, and identify 389,165 cano...

Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis.

PloS one
Understanding the interaction between T Cell Receptors (TCRs) and peptide-bound Major Histocompatibility Complexes (pMHCs) is crucial for comprehending immune responses and developing targeted immunotherapies. While recent machine learning (ML) model...

Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder.

Nature communications
Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protei...

Dual-Site Targeting by Peptide Inhibitors of the N-Terminal Domain of Hsp90: Mechanism and Design.

Journal of chemical information and modeling
Heat shock protein 90 (Hsp90) is a pivotal molecular chaperone crucial in the maturation of client proteins, positioning it as a significant target for cancer therapy. However, the design of effective Hsp90 inhibitors presents substantial challenges ...

Machine learning models for predicting configuration of modified knuckle epitope peptides of BMP-2 protein using mesoscale simulation data.

Physical chemistry chemical physics : PCCP
The high doses of bone morphogenetic proteins (BMPs) cause undesired side effects in skeletal tissue regeneration. An alternative approach is to use the bioactive knuckle epitope domain of BMP-2 (BMP2-KEP) with an open-arm structure as part of the pr...

Dynamics and Machine Learning Reveal the Link between Tripeptide Sequences and Evaporation-Driven Material Properties.

Nano letters
Previous research showed that a peptide composed of three tyrosines (YYY) can turn into organic glass and cause strong adhesion between substrates via evaporation. However, the mechanisms of these processes remain unclear, and the exploration of appl...

Topology-Enhanced Machine Learning Model (Top-ML) for Anticancer Peptide Prediction.

Journal of chemical information and modeling
Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have been developed to systematically screen potential candidates. However, th...

Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.

Proteomics
This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state di...