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Quantitative protei...
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Lindström, AntonUmeå universitet,Kemiska institutionen
(författare)
Quantitative protein descriptors for secondary structure characterization and protein classification
- Artikel/kapitelEngelska2009
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Elsevier BV,2009
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LIBRIS-ID:oai:DiVA.org:umu-3651
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https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-3651URI
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https://doi.org/10.1016/j.chemolab.2008.08.006DOI
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Språk:engelska
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Sammanfattning på:engelska
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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.
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Pettersson, FredrikThe Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK
(författare)
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Linusson, AnnaUmeå universitet,Kemiska institutionen(Swepub:umu)analin99
(författare)
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Umeå universitetKemiska institutionen
(creator_code:org_t)
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Ingår i:Chemometrics and Intelligent Laboratory Systems: Elsevier BV95:1, s. 74-850169-74391873-3239
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