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Quantitative protein descriptors for secondary structure characterization and protein classification
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- Lindström, Anton (author)
- Umeå universitet,Kemiska institutionen
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- Pettersson, Fredrik (author)
- The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK
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- Linusson, Anna (author)
- Umeå universitet,Kemiska institutionen
- (creator_code:org_t)
- Elsevier BV, 2009
- 2009
- English.
- In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 95:1, s. 74-85
- Journal article (peer-reviewed)
Abstract
Subject headings
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- In this study protein chains were characterized based on alignment-independent protein descriptors using three types of structural and sequence data; (i) C-α atom Euclidean distances, (ii) protein backbone ψ and φ angles and (iii) amino acid physicochemical properties (zz-scales). The descriptors were analyzed using principal component analysis (PCA) and further elucidated using the multivariate methods partial least-squares projections to latent structures discriminant-analysis (PLS-DA) and hierarchical-PLS-DA. The descriptors were applied to three protein chain datasets: (i) 82 chains classified, according to the structural classification of proteins (SCOP) scheme, as either all-α or all-β; (ii) 96 chains classified as either α + β or α/β and (iii) 6590 chains of all aforementioned classes selected from the PDB-select database. Results showed that the descriptors related to the secondary structure of the chains. The C-α Euclidean distances, and as expected, the protein backbone angles were found to be most important for the characterization and classification of chains. Assignment of SCOP classes using PLS-DA based on all descriptor types was satisfactory for all-α and all-β chains with more than 93% correct classifications of a large external test set, while the protein chains of types α/β and α + β was harder to discriminate between, resulting in 74% and 54% correct classifications, respectively.
Subject headings
- NATURVETENSKAP -- Kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences (hsv//eng)
Keyword
- Multivariate analysis
- Protein descriptor
- SCOP
- Auto covariance
- Auto cross-covariance
Publication and Content Type
- ref (subject category)
- art (subject category)
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