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Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared

The early characterization of drug membrane permeability is an important step in pharmaceutical developments to limit possible late failures in preclinical studies. This is particularly crucial for therapeutic peptides whose size generally prevents them from passively entering cells. However, a sequ...

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Detalles Bibliográficos
Autores principales: Mazzanti, Liuba, Ha-Duong, Tâp
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003141/
https://www.ncbi.nlm.nih.gov/pubmed/36902455
http://dx.doi.org/10.3390/ijms24055021
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author Mazzanti, Liuba
Ha-Duong, Tâp
author_facet Mazzanti, Liuba
Ha-Duong, Tâp
author_sort Mazzanti, Liuba
collection PubMed
description The early characterization of drug membrane permeability is an important step in pharmaceutical developments to limit possible late failures in preclinical studies. This is particularly crucial for therapeutic peptides whose size generally prevents them from passively entering cells. However, a sequence-structure-dynamics-permeability relationship for peptides still needs further insight to help efficient therapeutic peptide design. In this perspective, we conducted here a computational study for estimating the permeability coefficient of a benchmark peptide by considering and comparing two different physical models: on the one hand, the inhomogeneous solubility–diffusion model, which requires umbrella–sampling simulations, and on the other hand, a chemical kinetics model which necessitates multiple unconstrained simulations. Notably, we assessed the accuracy of the two approaches in relation to their computational cost.
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spelling pubmed-100031412023-03-11 Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared Mazzanti, Liuba Ha-Duong, Tâp Int J Mol Sci Article The early characterization of drug membrane permeability is an important step in pharmaceutical developments to limit possible late failures in preclinical studies. This is particularly crucial for therapeutic peptides whose size generally prevents them from passively entering cells. However, a sequence-structure-dynamics-permeability relationship for peptides still needs further insight to help efficient therapeutic peptide design. In this perspective, we conducted here a computational study for estimating the permeability coefficient of a benchmark peptide by considering and comparing two different physical models: on the one hand, the inhomogeneous solubility–diffusion model, which requires umbrella–sampling simulations, and on the other hand, a chemical kinetics model which necessitates multiple unconstrained simulations. Notably, we assessed the accuracy of the two approaches in relation to their computational cost. MDPI 2023-03-06 /pmc/articles/PMC10003141/ /pubmed/36902455 http://dx.doi.org/10.3390/ijms24055021 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mazzanti, Liuba
Ha-Duong, Tâp
Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared
title Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared
title_full Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared
title_fullStr Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared
title_full_unstemmed Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared
title_short Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared
title_sort understanding passive membrane permeation of peptides: physical models and sampling methods compared
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003141/
https://www.ncbi.nlm.nih.gov/pubmed/36902455
http://dx.doi.org/10.3390/ijms24055021
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