Prediction of peptides retention behavior in reversed-phase liquid chromatography based on their hydrophobicity
Author(s)
Al Musaimi, Othman
Valenzo, Oscar M Mercado
Williams, Daryl R
Type
Journal Article
Abstract
Hydrophobicity is an important physicochemical property of peptides and proteins. It is responsible for their conformational changes, stability, as well as various chemical intramolecular and intermolecular interactions. Enormous efforts have been invested to study the extent of hydrophobicity and how it could influence various biological processes, in addition to its crucial role in the separation and purification endeavor as well.
Here, we have reviewed various studies that were carried out to determine the hydrophobicity starting from (i) simple amino acids solubility behavior, (ii) experimental approach that was undertaken in the reversed-phase liquid chromatography mode, and ending with (iii) some examples of more advanced computational and machine learning models.
Here, we have reviewed various studies that were carried out to determine the hydrophobicity starting from (i) simple amino acids solubility behavior, (ii) experimental approach that was undertaken in the reversed-phase liquid chromatography mode, and ending with (iii) some examples of more advanced computational and machine learning models.
Date Issued
2023-01
Date Acceptance
2022-10-31
Citation
Journal of Separation Science, 2023, 46 (2), pp.1-17
ISSN
1615-9306
Publisher
Wiley
Start Page
1
End Page
17
Journal / Book Title
Journal of Separation Science
Volume
46
Issue
2
Copyright Statement
© 2022 The Authors. Journal of Separation Science published by Wiley-VCH GmbH
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000882943300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Physical Sciences
Chemistry, Analytical
Chemistry
hydrophobicity
machine learning
peptides
retention behavior
AMINO-ACIDS
TIME PREDICTION
SOLUBILITY
PROTEIN
HPLC
PURIFICATION
SEPARATION
PI
Publication Status
Published
Date Publish Online
2022-11-09