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A new set of amino acid descriptors and its application in peptide QSARs
Author(s) -
Mei Hu,
Liao Zhi H.,
Zhou Yuan,
Li Shengshi Z.
Publication year - 2005
Publication title -
peptide science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 125
eISSN - 1097-0282
pISSN - 0006-3525
DOI - 10.1002/bip.20296
Subject(s) - chemistry , partial least squares regression , quantitative structure–activity relationship , steric effects , preprint , principal component analysis , peptide , amino acid , cross validation , stereochemistry , artificial intelligence , machine learning , computer science , biochemistry , world wide web
In this work, a new set of amino acid descriptors, i.e., VHSE (principal components score V ectors of H ydrophobic, S teric, and E lectronic properties), is derived from principal components analysis (PCA) on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively, which are included in total 50 physicochemical variables of 20 coded amino acids. Using the stepwise multiple regression (SMR) method combined with partial least squares (PLS), the VHSE scales are then applied to QSAR studies of bitter‐tasting dipeptides (BTD), angiotensin‐converting enzyme (ACE) inhibitors, and bradykinin‐potentiating pentapeptides (BPP). To validate the predictive power of resulting models, external validation are also performed. A comparison of the results to those obtained with z scores and other two‐dimensional (2D) or three‐dimensional(3D) descriptors shows that the VHSE scales are comparable for parameterizing the structural variability of the peptide series. ©2005 Wiley Periodicals, Inc. Biopoly 80: 775–786, 2005 This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com