Evaluation of Structure Prediction and Molecular Docking Tools for Therapeutic Peptides in Clinical Use and Trials Targeting Coronary Artery Disease

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(2), P. 462 - 462

Published: Jan. 8, 2025

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure tools, including AlphaFold 3, I-TASSER 5.1, PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) template-based (I-TASSER 5.1) fragment-based (PEP-FOLD), selected their proven capabilities in predicting reliable structures. Molecular was conducted using four (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro HawDock 2.0) assess binding affinities interactions. A 100 ns dynamics (MD) simulation performed evaluate stability peptide–receptor complexes, along with Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) calculations determine free energies. The results demonstrated that Apelin, a peptide, exhibited superior across all platforms, making it promising candidate CAD therapy. Apelin’s interactions key receptors involved cardiovascular health notably stronger more stable compared other tested. findings underscore importance integrating advanced computational design evaluation, offering valuable insights future applications CAD. Future work should focus on vivo validation combination therapies fully explore clinical potential these peptides.

Language: Английский

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Artificial intelligence in food bioactive peptides screening: Recent advances and future prospects

Trends in Food Science & Technology, Journal Year: 2024, Volume and Issue: unknown, P. 104845 - 104845

Published: Dec. 1, 2024

Language: Английский

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A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs

Journal of Biological Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 108225 - 108225

Published: Jan. 1, 2025

Language: Английский

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Deep Learning for Predicting Biomolecular Binding Sites of Proteins

Research, Journal Year: 2025, Volume and Issue: 8

Published: Jan. 1, 2025

The rapid evolution of deep learning has markedly enhanced protein–biomolecule binding site prediction, offering insights essential for drug discovery, mutation analysis, and molecular biology. Advancements in both sequence-based structure-based methods demonstrate their distinct strengths limitations. Sequence-based approaches offer efficiency adaptability, while techniques provide spatial precision but require high-quality structural data. Emerging trends hybrid models that combine multimodal data, such as integrating sequence information, along with innovations geometric learning, present promising directions improving prediction accuracy. This perspective summarizes challenges computational demands dynamic modeling proposes strategies future research. ultimate goal is the development computationally efficient flexible capable capturing complexity real-world biomolecular interactions, thereby broadening scope applicability predictions across a wide range biomedical contexts.

Language: Английский

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Quantum Oncology

Quantum Reports, Journal Year: 2025, Volume and Issue: 7(1), P. 9 - 9

Published: Feb. 18, 2025

Quantum core technologies (computing, sensing, imaging, communication) hold immense promise for revolutionizing cancer care. This paper explores their distinct capabilities in early-stage diagnosis, improved clinical workflows, drug discovery, and personalized treatment. By overcoming challenges such as infrastructure ethical considerations, these processes can unlock faster diagnoses, optimize therapies, enhance patient outcomes.

Language: Английский

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Understanding and Classification of Innate Immune Response through Weighted Edge Representation Learning with Dual Hypergraph Transformation

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104443 - 104443

Published: Feb. 1, 2025

Language: Английский

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Chemically Engineered Peptide Efficiently Blocks Malaria Parasite Entry into Red Blood Cells

Biochemistry, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Chemical peptide engineering, enabled by residue insertion, backbone cyclization, and incorporation of an additional disulfide bond, led to a unique cyclic that efficiently inhibits the invasion red blood cells malaria parasites. The engineered exhibits 20-fold enhanced affinity toward its receptor (PfAMA1) compared native ligand (PfRON2), as determined surface plasmon resonance. In-vitro parasite growth inhibition assay revealed augmented potency peptide. structure PfAMA1-cyclic complex, predicted deep learning-based prediction tool ColabFold-AlphaFold2 with protein-cyclic complex offset, provided valuable insights into observed activity analogs. Rational editing side chain described here proved be effective strategy for designing peptide-based inhibitors interfere disease-related protein-protein interactions.

Language: Английский

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Main methods and tools for peptide development based on protein-protein interactions (PPIs).

Biochemical and Biophysical Research Communications, Journal Year: 2025, Volume and Issue: unknown, P. 151623 - 151623

Published: March 1, 2025

Language: Английский

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Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Drug classification and target identification are crucial yet challenging steps in drug discovery. Existing methods often suffer from inefficiencies, overfitting, limited scalability. Traditional approaches like support vector machines XGBoost struggle to handle large, complex pharmaceutical datasets effectively. Deep learning models, while powerful, face challenges with interpretability, computational complexity, generalization unseen data. This study addresses these limitations by introducing a novel framework: optSAE+HSAPSO. framework integrates stacked autoencoder (SAE) for robust feature extraction hierarchically self-adaptive particle swarm optimization (HSAPSO) algorithm adaptive parameter optimization. combination delivers superior performance across various metrics. Experimental evaluations on DrugBank Swiss-Prot demonstrate that optSAE+HSAPSO achieves high accuracy of 95.52%. Notably, it exhibits significantly reduced complexity (0.010 seconds per sample) exceptional stability (±0.003). Compared state-of-the-art methods, the offers higher accuracy, faster convergence, greater resilience variability. Furthermore, ROC convergence analyses confirm its robustness capability, maintaining consistent both validation datasets. By leveraging advanced techniques, efficiently handles large sets diverse data, making scalable adaptable solution real-world discovery applications. However, method's is dependent quality training fine-tuning may be necessary high-dimensional Despite limitations, demonstrates transformative potential, reducing overhead improving reliability. work advances field informatics presenting reliable efficient identification. These findings open promising avenues future research, including extending other domains such as disease diagnostics or genetic data classification, ultimately accelerating development process.

Language: Английский

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Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Biomolecules, Journal Year: 2025, Volume and Issue: 15(4), P. 524 - 524

Published: April 3, 2025

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties interactions with biological targets. Many models predicting peptide function or structure rely on intrinsic properties, including influence amino acid composition, sequence, chain length, which impact stability, folding, aggregation, target interaction. Homology predicts structures based known templates. Peptide-protein can be explored using molecular docking techniques, but there are challenges related to inherent flexibility addressed by more computationally intensive approaches that consider movement over time, called dynamics (MD). Virtual screening many usually against single target, enables rapid identification potential peptides from large libraries, typically approaches. The integration artificial intelligence (AI) has transformed leveraging amounts data. AlphaFold general protein prediction deep learning greatly improved predictions conformations interactions, addition estimates model accuracy at each residue guide interpretation. Peptide being further enhanced Protein Language Models (PLMs), deep-learning-derived statistical learn computer representations useful identify fundamental patterns proteins. Recent methodological developments discussed context canonical as well those modifications cyclisations. In designing therapeutics, main outstanding challenge for these methods incorporation diverse non-canonical acids

Language: Английский

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