New insights into protein–protein interaction modulators in drug discovery and therapeutic advance

Signal Transduction and Targeted Therapy, Год журнала: 2024, Номер 9(1)

Опубликована: Дек. 5, 2024

Abstract Protein-protein interactions (PPIs) are fundamental to cellular signaling and transduction which marks them as attractive therapeutic drug development targets. What were once considered be undruggable targets have become increasingly feasible due the progress that has been made over last two decades rapid technological advances. This work explores influence of innovations on PPI research development. Additionally, diverse strategies for discovering, modulating, characterizing PPIs their corresponding modulators examined with aim presenting a streamlined pipeline advancing PPI-targeted therapeutics. By showcasing carefully selected case studies in modulator discovery development, we illustrate efficacy various identifying, optimizing, overcoming challenges associated design. The valuable lessons insights gained from identification, optimization, approval discussed demonstrating transitioned beyond early-stage now represent prime opportunity significant potential. examples encompass those developed cancer, inflammation immunomodulation, well antiviral applications. perspective aims establish foundation effective targeting modulation using pave way future

Язык: Английский

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Exploring Recent Updates on Molecular Docking: Types, Method, Application, Limitation & Future Prospects

International Journal Of Pharmaceutical Research And Allied Sciences, Год журнала: 2024, Номер 13(2), С. 24 - 40

Опубликована: Янв. 1, 2024

Язык: Английский

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A comprehensive survey of scoring functions for protein docking models

BMC Bioinformatics, Год журнала: 2025, Номер 26(1)

Опубликована: Янв. 22, 2025

Abstract Background While protein-protein docking is fundamental to our understanding of how proteins interact, scoring complex conformations a critical component successful programs. Without accurate and efficient functions differentiate between native non-native binding complexes, the accuracy current tools cannot be guaranteed. Although many innovative have been proposed, good function for remains elusive. Deep learning models offer alternatives using explicit empirical or mathematical complexes. Results In this study, we perform comprehensive survey state-of-the-art by considering most popular highly performant approaches, both classical deep learning-based, The methods were also compared based on their runtime as it directly impacts use in large-scale applications. Conclusions We evaluate strengths weaknesses learning-based approaches across seven public datasets aid researchers progress made field.

Язык: Английский

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Protein Function Analysis through Machine Learning

Biomolecules, Год журнала: 2022, Номер 12(9), С. 1246 - 1246

Опубликована: Сен. 6, 2022

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new approaches have incorporated into many areas dealing with function. We examine how integrated a wide range models improve prediction accuracy gain better understanding The applications discussed are structure prediction, engineering using sequence modifications achieve stability druggability characteristics, molecular docking terms protein-ligand binding, including allosteric effects, protein-protein interactions protein-centric drug discovery. To quantify mechanisms underlying function, holistic approach that takes structure, flexibility, stability, dynamics account is required, as these aspects become inseparable through their interdependence. Another key component conformational dynamics, which often manifest kinetics. Computational use generate representative ensembles differences included this review. Future opportunities highlighted each topics.

Язык: Английский

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Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery

Expert Opinion on Drug Discovery, Год журнала: 2023, Номер 18(7), С. 737 - 752

Опубликована: Май 29, 2023

Introduction Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread artificial intelligence and machine learning complemented with experimental methods is likely to change perspectives protein-protein modulator research. Noteworthy, some novel low molecular weight (LMW) short peptide modulators PPIs already in clinical trials treatment relevant diseases.Areas covered This review focuses on main properties interfaces key concepts pertaining modulation PPIs. authors survey recently reported state-of-the-art dealing rational design PPI highlight role several computer-based approaches.Expert opinion Interfering specifically large protein still an open challenge. initial concerns about unfavorable physicochemical many these nowadays less acute molecules lying beyond rule 5, orally available successful trials. As cost biologics interfering very high, it would seem reasonable put more effort, both academia private sectors, actively developing compounds peptides perform this task.

Язык: Английский

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The potential of selenium to induce salt stress tolerance in Brassica rapa: Evaluation of biochemical, physiological and molecular phenomenon

Plant Stress, Год журнала: 2023, Номер 11, С. 100331 - 100331

Опубликована: Дек. 28, 2023

The efficacy of selenium (Se) to ameliorate salinity stress in Brassica rapa was elucidated using an in-vitro and in-silico hybrid approach. Selenium applied as a seed priming agent on the seeds grown salt-spiked soils. treatment facilitated all growth indicators physiological parameters, including chlorophyll synthesis, sugar contents, gas exchange attributes, etc. advantageous effects Se were accredited alleviation through reducing malondialdehyde (MDA), proline, electrolyte leakage, hydrogen peroxide (H2O2) level. Similarly, modulations expression genes encoding antioxidative enzymes enhanced reactive oxygen species (ROS) scavenging capacity along with decrease sodium (Na+) ions, reciprocally enhancing potassium (K+) ions resulting higher K+/Na+ ratio. Additionally, based silico studies, significant difference surface overlap stress-responsive proteins, DREB, SOS3, STXBP1 B. rapa, observed, indicating involvement foiling interaction NaCl enzymes. In case CAT macromolecule, ligands exhibited -1.9-fold lower docking score compared selenite -4.9-fold, more overlapped interactive than chloride. binding energies analysis recorded -3.3 kcal/mol energy for best pose while it -3.5 kcal.mol-1 SOS3 1 STXBP1, respectively. Ligand revealed frequent existence bonds besides hydrophobic interactions, salt bridges, pi–pi interactions representing high structural functional stability. Results current study unveiled bioinformatics system unveil biochemical, molecular phenomenon behind selenium-driven resilience rapa.

Язык: Английский

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Sturgeon (Acipenser schrenckii) spinal cord peptides: Antioxidative and acetylcholinesterase inhibitory efficacy and mechanisms

Food Chemistry, Год журнала: 2024, Номер 461, С. 140834 - 140834

Опубликована: Авг. 12, 2024

Язык: Английский

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From Traditional Methods to Deep Learning Approaches: Advances in Protein–Protein Docking

Wiley Interdisciplinary Reviews Computational Molecular Science, Год журнала: 2025, Номер 15(2)

Опубликована: Март 1, 2025

ABSTRACT Protein–protein interactions play a crucial role in human biological processes, and deciphering their structural information interaction patterns is essential for drug development. The high costs of experimental structure determination have brought computational protein–protein docking methods into the spotlight. Traditional algorithms, which hinge on sampling‐scoring framework, heavily rely extensive sampling candidate poses customized scoring functions based geometric chemical compatibility between proteins. However, these face challenges related to efficiency stability. advent deep learning (DL) has ushered data‐driven that demonstrate significant advantages, particularly boosting docking. We systematically review historical development from traditional approaches DL techniques provide insights emerging technologies this field. Moreover, we summarize commonly used datasets evaluation metrics expect can offer valuable guidance more efficient algorithms.

Язык: Английский

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Discovery of Bioactive Peptides Through Peptide Scanning

Journal of Peptide Science, Год журнала: 2025, Номер 31(6)

Опубликована: Май 10, 2025

ABSTRACT Therapeutic peptides targeted at various diseases are becoming increasingly relevant for the pharmaceutical industry. Several of these drugs were originally designed by mimicking a segment protein interest. As such, mimicry represents promising strategy both in immunology, identification B‐ and T‐cell epitopes, as well modulation activity, including disruption protein–protein interactions (PPIs) interference with biological or pathological cellular functions. methods have been developed to pinpoint (binding) epitopes regions responsible activity. One such strategies is scanning selected domains synthetic overlapping peptides. mechanism action mimetic peptide can be similar that whole protein, this method offers powerful tool investigation function, along providing solid basis development therapeutic candidates. This review gives general overview different applications methodology, describing comparison preparation use solid‐phase libraries (peptide arrays) isolated highlighting their strengths most common applications.

Язык: Английский

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Protein Binding Prediction by Computational Methods

Опубликована: Янв. 1, 2025

Язык: Английский

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