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, Год журнала: 2025, Номер 26(2), С. 462 - 462

Опубликована: Янв. 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.

Язык: Английский

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Therapeutic Peptide Development Revolutionized: Harnessing the Power of Artificial Intelligence for Drug Discovery

Heliyon, Год журнала: 2024, Номер 10(22), С. e40265 - e40265

Опубликована: Ноя. 1, 2024

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and expansion effective medicinal compounds. The novel functional properties peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals biotechnology advance drug development rapidly reduce costs. Short-chain inhibit a wide range pathogens great potential for targeting diseases. To address challenges synthesis sustainability, methods, namely machine learning, must be integrated into their production. Learning can use complicated computations select active toxic compounds metabolic activity. Through this comprehensive review, we investigated method as tool finding peptide-based drugs providing more accurate analysis through introduction predictable databases selection development.

Язык: Английский

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Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs

Biomolecules, Год журнала: 2024, Номер 14(10), С. 1303 - 1303

Опубликована: Окт. 15, 2024

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent artificial intelligence (AI), there has been a transformative shift in generation new peptide drugs. AI offers range computational tools algorithms that enables researchers accelerate therapeutic pipeline. This review explores current landscape applications discovery, highlighting its potential, challenges, ethical considerations. Additionally, it presents case studies future prospectives demonstrate impact on

Язык: Английский

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Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics

Chemical Reviews, Год журнала: 2024, Номер 124(22), С. 13020 - 13093

Опубликована: Ноя. 14, 2024

The development of potent, specific, and pharmacologically viable chemical probes therapeutics is a central focus biology therapeutic development. However, significant portion predicted disease-causal proteins have proven resistant to targeting by traditional small molecule biologic modalities. Many these so-called "undruggable" targets feature extended, dynamic protein-protein protein-nucleic acid interfaces that are their roles in normal diseased signaling pathways. Here, we discuss the synthetically stabilized peptide protein mimetics as an ever-expanding powerful region space tackle undruggable targets. These molecules aim combine synthetic tunability pharmacologic properties typically associated with binding footprints, affinities specificities biologics. In this review, historical emerging platforms approaches design, screen, select optimize "designer" peptidomimetics We examine inspiration design different classes designer peptidomimetics: (i) macrocyclic peptides, (ii) side chain (iii) non-natural peptidomimetics, (iv) proteomimetics, notable examples application challenging biomolecules. Finally, summarize key learnings remaining challenges for become useful historically

Язык: Английский

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Comparative analysis of non-fermented and Saccharomyces boulardii-fermented whey: Peptidomic profiling, in silico bioactive peptide analysis, and in vivo evaluation of serum proteins and immune response

International Dairy Journal, Год журнала: 2025, Номер unknown, С. 106222 - 106222

Опубликована: Фев. 1, 2025

Язык: Английский

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Development of a Peptide Aptamer-Based TRFIA for the Quantitive Detection of SARS-CoV-2 Nucleocapsid Protein

Journal of Fluorescence, Год журнала: 2025, Номер unknown

Опубликована: Фев. 22, 2025

Язык: Английский

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Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Biomolecules, Год журнала: 2025, Номер 15(4), С. 524 - 524

Опубликована: Апрель 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

Язык: Английский

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A Computational Approach for Designing a Peptide-Based Acetyl-CoA Synthetase 2 Inhibitor: A New Horizon for Anticancer Development

Cell Biochemistry and Biophysics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 27, 2025

Abstract Acetyl-CoA Synthetase 2 (ACSS2) has emerged as a new target for anticancer development owing to its high expression in various tumours and enhancement of malignancy. Stressing the growing interest peptide-derived drugs featuring better selectivity efficacy, computational protocol was applied design peptide inhibitor ACSS2. Herein, 3600 sequences derived from ACSS2 nucleotide motif were generated by classifying 20 amino acids into six physiochemical groups. De novo modeling maintained essential binding interactions, refined library 16 peptides using Support Vector Machine filters ensure proper bioavailability, toxicity, therapeutic relevance. Structural folding predictions, along with molecular docking, identified top candidate, Pep16, which demonstrated significantly higher affinity (91.1 ± 1.6 kcal/mol) compared known (53.7 0.7 kcal/mol). Further dynamics simulations free energy calculations revealed that Pep16 enhances conformational variability, occupies larger interface, achieved firm binding. MM/GBSA analysis highlighted key electrostatic interactions specific residues, including ARG 373, 526, 628, 631, LYS 632. Overall, appears lock pocket compact, rigid conformation, potentially blocking ATP catalytic activity, may serve novel inhibitor. Though, we urge further research confirm compare potential existing inhibitors. We also believe this systematic methodology would represent an indispensable tool prospective peptide-based drug discovery.

Язык: Английский

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De Novo Rational design of peptide-based covalent inhibitors via mapping of complementary binding site residues – technical protocol and case study on KRASG12C and BTK481C

Deleted Journal, Год журнала: 2025, Номер 2(1)

Опубликована: Май 1, 2025

Язык: Английский

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De Novo Rational Design of Peptide‐Based Protein–Protein Inhibitors (Pep‐PPIs) Approach by Mapping the Interaction Motifs of the PP Interface and Physicochemical Filtration: A Case on p25‐Cdk5‐Mediated Neurodegenerative Diseases

Journal of Cellular Biochemistry, Год журнала: 2024, Номер 125(9)

Опубликована: Авг. 15, 2024

ABSTRACT Protein–protein interactions, or PPIs, are a part of every biological activity and have been linked to number diseases, including cancer, infectious neurological disorders. As such, targeting PPIs is considered strategic vital approach in the development new medications. Nonetheless, wide flat contact interface makes it difficult find small‐molecule PP inhibitors. An alternative strategy would be use PPI interaction motifs as building blocks for design peptide‐based Herein, we designed 12‐mer peptide inhibitors target p25‐inducing‐cyclin‐dependent kinase (Cdk5) hyperregulation, that has shown perpetuate neuroinflammation, which one major causal implications neurodegenerative disorders such Alzheimer's disease, Parkinson's frontotemporal dementia. We generated library 5 062 500 combination sequences (PCS) derived from motif Cdk5/p25 interface. The 20 amino acids were differentiated into six groups, namely, hydrophobic (aliphatic), aromatic, basic, acidic, unique, polar uncharged, on basis their physiochemical properties. To preserve necessary ideal binding, de novo modeling all possible sequence substitutions was considered. A set filters, backed by Support Vector Machine (SVM) algorithm, then used create shortlisted custom met specific bioavailability, toxicity, therapeutic relevance, leading refined 15 PCS. greedy algorithm coarse‐grained force field predict structure folding before subsequent studies. Molecular docking performed estimate relative binding affinities, out top hits, Pep15 subjected molecular dynamics simulations free‐energy calculations comparison known inhibitor with experimental data (template peptide). Interestingly, identified through our protocol, Pep15, found show significantly higher affinity than reference template (−48.10 ± 0.23 kcal/mol −17.53 0.27 kcal/mol, respectively). In peptide, possess more compact buried surface area, tighter landscape, reduced conformational variability, enhanced structural kinetic stability complex. Notably, both minimal impact architectural integrity secondary structure. propose novel potentially disruptive drug Cdk5/p25‐mediated phenotypes require further clinical investigation. systematic protocol findings this report serve valuable tool identification critical reactive residues, designing analogs, potent

Язык: Английский

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