التفاصيل البيبلوغرافية
| العنوان: |
Quantitative protein descriptors for secondary structure characterization and protein classification |
| المؤلفون: |
Lindström, Anton1, Pettersson, Fredrik2, Linusson, Anna1 anna.linusson@chem.umu.se |
| المصدر: |
Chemometrics & Intelligent Laboratory Systems. Jan2009, Vol. 95 Issue 1, p74-85. 12p. |
| مصطلحات موضوعية: |
*STRUCTURAL frames, *DISTANCES, *ORGANIC acids, *PHYSICAL & theoretical chemistry |
| مستخلص: |
Abstract: 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. [Copyright &y& Elsevier] |
| قاعدة البيانات: |
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