Computation meets experiment: identification of highly efficient fibrillating peptides

Self-assembling peptides are of huge interest for biological, medical and nanotechnological applications. The enormous chemical variety that is available from the 20 amino acids offers potentially unlimited peptide sequences, but it is currently an issue to predict their supramolecular behavior in a...

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Autores principales: Sori, Lorenzo, Pizzi, Andrea, Bergamaschi, Greta, Gori, Alessandro, Gautieri, Alfonso, Demitri, Nicola, Soncini, Monica, Metrangolo, Pierangelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2023
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10424810/
https://www.ncbi.nlm.nih.gov/pubmed/38014394
http://dx.doi.org/10.1039/d3ce00495c
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author Sori, Lorenzo
Pizzi, Andrea
Bergamaschi, Greta
Gori, Alessandro
Gautieri, Alfonso
Demitri, Nicola
Soncini, Monica
Metrangolo, Pierangelo
author_facet Sori, Lorenzo
Pizzi, Andrea
Bergamaschi, Greta
Gori, Alessandro
Gautieri, Alfonso
Demitri, Nicola
Soncini, Monica
Metrangolo, Pierangelo
author_sort Sori, Lorenzo
collection PubMed
description Self-assembling peptides are of huge interest for biological, medical and nanotechnological applications. The enormous chemical variety that is available from the 20 amino acids offers potentially unlimited peptide sequences, but it is currently an issue to predict their supramolecular behavior in a reliable and cheap way. Herein we report a computational method to screen and forecast the aqueous self-assembly propensity of amyloidogenic pentapeptides. This method was found also as an interesting tool to predict peptide crystallinity, which may be of interest for the development of peptide based drugs.
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spelling pubmed-104248102023-08-15 Computation meets experiment: identification of highly efficient fibrillating peptides Sori, Lorenzo Pizzi, Andrea Bergamaschi, Greta Gori, Alessandro Gautieri, Alfonso Demitri, Nicola Soncini, Monica Metrangolo, Pierangelo CrystEngComm Chemistry Self-assembling peptides are of huge interest for biological, medical and nanotechnological applications. The enormous chemical variety that is available from the 20 amino acids offers potentially unlimited peptide sequences, but it is currently an issue to predict their supramolecular behavior in a reliable and cheap way. Herein we report a computational method to screen and forecast the aqueous self-assembly propensity of amyloidogenic pentapeptides. This method was found also as an interesting tool to predict peptide crystallinity, which may be of interest for the development of peptide based drugs. The Royal Society of Chemistry 2023-07-04 /pmc/articles/PMC10424810/ /pubmed/38014394 http://dx.doi.org/10.1039/d3ce00495c Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Sori, Lorenzo
Pizzi, Andrea
Bergamaschi, Greta
Gori, Alessandro
Gautieri, Alfonso
Demitri, Nicola
Soncini, Monica
Metrangolo, Pierangelo
Computation meets experiment: identification of highly efficient fibrillating peptides
title Computation meets experiment: identification of highly efficient fibrillating peptides
title_full Computation meets experiment: identification of highly efficient fibrillating peptides
title_fullStr Computation meets experiment: identification of highly efficient fibrillating peptides
title_full_unstemmed Computation meets experiment: identification of highly efficient fibrillating peptides
title_short Computation meets experiment: identification of highly efficient fibrillating peptides
title_sort computation meets experiment: identification of highly efficient fibrillating peptides
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10424810/
https://www.ncbi.nlm.nih.gov/pubmed/38014394
http://dx.doi.org/10.1039/d3ce00495c
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