NDLI: A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field

Content Provider	Springer Nature Link
Author	Zhou, Peng Zhou, Yuan Wu, Shirong Li, Bo Tian, Feifei Li, Zhiliang
Copyright Year	2006
Abstract	A noval molecular structural expression method, three-dimensional vector of atomic interaction field (3D-VAIF), has been newly developed based on electrostatic and steric interaction between different types of atoms. Feature descriptors of single amino acid, i.e. principal component scores of structural information for amino acids (SSIA), are obtained through calculation of structural information of 20 coded amino acids using principal component analysis (PCA) method, and the strict tests are performed on the property of SSIA by three quantitative structure-activity relationships (QSARs)/quantitative sequence-activity models (QSAMs) models of 58 ngiotensin-converting enzymes (ACE), 48 bitter tasting thresholds (BTT) and 31 bradykinin potentiating pentapeptides (BPP). Cumulative multiple correlation coefficients (R cum 2 ) are 0.789, 0.856 and 0.838; and corresponding cross-validated correlation coefficients (Q LOO 2 ) are 0.773, 0.837 and 0.815, respectively. Good results indicate that SSIA are better than traditional descriptors of amino acid in quantitative sequence-activity relationships of peptide analogues.
Starting Page	524
Ending Page	529
Page Count	6
File Format	PDF
ISSN	10016538
Journal	Chinese Science Bulletin
Volume Number	51
Issue Number	5
e-ISSN	18619541
Language	English
Publisher	Science in China Press
Publisher Date	2006-01-01
Publisher Place	Beijing
Access Restriction	Subscribed
Subject Keyword	three-dimensional vector of atomic interaction field (3D-VAIF) principal component scores of structural information for amino acids (SSIA) quantitative structure-activity relationship/quantitative sequence activity model quantum chemistry principal component analysis stepwise multiple regression partial least squares regression Chemistry/Food Science Geosciences Life Sciences Physics Engineering Science
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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