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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A Review on the Stability Challenges of Advanced Biologic Therapeutics

Pharmaceutics, Journal Year: 2025, Volume and Issue: 17(5), P. 550 - 550

Published: April 23, 2025

Advanced biotherapeutic systems such as gene therapy, mRNA lipid nanoparticles, antibody–drug conjugates, fusion proteins, and cell therapy have proven to be promising platforms for delivering targeted biologic therapeutics. Preserving the intrinsic stability of these advanced therapeutics is essential maintain their innate structure, functionality, shelf life. Nevertheless, various challenges obstacles arise during formulation development throughout storage period due complex nature sensitivity stress factors. Key concerns include physical degradation chemical instability factors fluctuations in pH temperature, which results conformational colloidal instabilities biologics, adversely affecting quality therapeutic efficacy. This review emphasizes key issues associated with approaches identify overcome them. In brittleness viral vectors encapsulation limits stability, requiring use stabilizers, excipients, lyophilization. Keeping cells viable whole process, from culture final formulation, still a major difficulty. therapeutics, stabilization strategies optimization nucleotides compositions are used address both nanoparticles. Monoclonal antibodies colloidally conformationally unstable. Hence, buffers stabilizers useful stability. Although proteins monoclonal share structural similarities, they show similar pattern instability. Antibody–drug conjugates possess conjugation linker outlines biotherapeutics provides insights into challenges.

Language: Английский

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Enhancing the Protein Stability of an Anticancer VHH‐Fc Heavy Chain Antibody through Computational Modeling and Variant Design

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Abstract VHHs (also known as nanobodies) are important therapeutic antibodies. To prolong their half‐life in bloodstream, usually fused to the Fc fragment of full‐length However, stability is often main challenge for commercialization, and methods improve still lacking. Here, an silico pipeline developed analyzing anticancer VHH‐Fc fusion antibody (VFA01) designing its stable variants. Computational modeling used analyze VFA01 structure evaluate conformational stability, disulfide bond reduction state, aggregation degradation tendency. By building mechanistic models degradation, hotspot residues affecting stability: C130, F57, Y106, L120, W111 identified. Based on them, a series variants designed obtained variant M11 (C130S/W111F/F57K) whose significantly enhanced compared VFA01: there no visible particles solution, change rate DLS average hydrodynamic size, SEC HMW%, CE‐SDS purity improved by 6.2‐, 3.4‐, 1.5‐fold, respectively. Both antigen‐binding activity production yield also about 1.5‐fold. The results show that our computational very promising approach improving protein

Language: Английский

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EVALUATING THE STORAGE STABILITY OF A Plasmodium vivax CIRCUMSPOROZOITE PROTEIN VACCINE CANDIDATE

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

Published: Feb. 1, 2025

Language: Английский

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Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti‐Inflammatory and Gene Therapy Applications

Proteins Structure Function and Bioinformatics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 22, 2025

ABSTRACT Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning‐based approaches developed to address this problem have achieved significant success. However, these often do not adequately emphasize the properties of proteins. In study, we heuristic optimization method enhance key functionalities such as solubility, flexibility, stability, while preserving structural integrity This aims reduce demands by enabling both structurally sound. approach particularly valuable for synthetic production with anti‐inflammatory those used in gene therapy. The designed were initially evaluated ability preserve structures using recovery confidence metrics, followed assessments AlphaFold tool. Additionally, protein sequences mutated genetic algorithm compared our method. results demonstrate generated exhibit much greater similarity native structures. code available https://github.com/aysenursoyturk/HMHO .

Language: Английский

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Adverse Impacts of PEGylated Protein Therapeutics: A Targeted Literature Review

BioDrugs, Journal Year: 2024, Volume and Issue: 38(6), P. 795 - 819

Published: Oct. 17, 2024

The beneficial effects of polyethylene glycol (PEG)-conjugated therapeutics, such as increased half-life, solubility, stability, and decreased immunogenicity, have been well described. There concerns, however, about adverse outcomes with their use, but understanding those is still relatively limited. present study aimed to characterize associated PEGylation protein-based therapeutics on pharmacologic properties, safety. A targeted review English language articles published from 1990 September 29, 2023, was conducted. Of the 29 studies included in this review, 18 reported safety hematologic complications, hepatic toxicity, injection site reactions, arthralgia, nausea, infections, grade 3 or 4 events (AEs), AE-related discontinuations dose modifications. Fifteen immunogenicity-related outcomes, prevalence pre-existing antibodies PEG, treatment-emergent antibody response, hypersensitivity reactions PEGylated drugs. Seven pharmacological clearance reduced activity response This aims contribute a balanced view therapies by summarizing lack benefit literature. We identified several characterizing effects, immunogenicity use therapeutics. Our findings suggest that using may require careful monitoring for including screening induced therapy, adjusting dosing

Language: Английский

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Investigation on the Combined Effect of Hydroxypropyl Beta-Cyclodextrin (HPβCD) and Polysorbate in Monoclonal Antibody Formulation

Pharmaceuticals, Journal Year: 2024, Volume and Issue: 17(4), P. 528 - 528

Published: April 19, 2024

Monoclonal antibodies require careful formulation due to their inherent stability limitations. Polysorbates are commonly used stabilize mAbs, but they prone degradation, which results in unwanted impurities. KLEPTOSE

Language: Английский

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Stability of Protein Pharmaceuticals: Recent Advances

Pharmaceutical Research, Journal Year: 2024, Volume and Issue: 41(7), P. 1301 - 1367

Published: June 27, 2024

Language: Английский

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Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(33)

Published: Aug. 7, 2024

Protein phase transitions (PPTs) from the soluble state to a dense liquid (forming droplets via liquid–liquid separation) or solid aggregates (such as amyloids) play key roles in pathological processes associated with age-related diseases such Alzheimer’s disease. Several computational frameworks are capable of separately predicting formation amyloid based on protein sequences, yet none have tackled prediction both within unified framework. Recently, large language models (LLMs) exhibited great success structure prediction; however, they not been used for PPTs. Here, we fine-tune LLM PPTs and demonstrate its usage evaluating how sequence variants affect PPTs, an operation useful design. In addition, show superior performance compared suitable classical benchmarks. Due “black-box” nature LLM, also employ random forest model along biophysical features facilitate interpretation. Finally, focusing disease-related proteins, that greater aggregation is reduced gene expression disease, suggesting natural defense mechanism.

Language: Английский

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Application of lyophilization in pharmaceutical injectable formulations: An industry and regulatory perspective

Journal of Drug Delivery Science and Technology, Journal Year: 2024, Volume and Issue: 100, P. 106089 - 106089

Published: Aug. 23, 2024

Language: Английский

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