Principal component analysis Protein

S Flayward, A Kitao, N Go. Flarmomc and anharmomc aspects m the dynamics of BPTI A normal mode analysis and principal component analysis. Protein Sci 3 936-943, 1994. 

PACS PCA PDB PEEM PESTM PET PrP Picture Archiving and Communication Systems Principal Component Analysis Protein Data Bank Photoemission Electron Microscopy STM Photoemission Spectroscopy Positron Emission Tomography Prion Protein... 

Y. Mu, P. H. Nguyen, and G. Stock (2004) Energy landscape of a small peptide revealed by dihedral angle principal component analysis. Proteins Structure, Function, and Bioinformatics 58(1), pp. 45-52... 

The important underlying components of protein motion during a simulation can be extracted by a Principal Component Analysis (PGA). It stands for a diagonalization of the variance-covariance matrix R of the mass-weighted internal displacements during a molecular dynamics simulation. 

Grubmiiller described a method to induce conformational transitions in proteins and derived rate constants for these ([Grubmiiller 1994]). The method employs subsequent modifications of the original potential function based on a principal component analysis of a short MD simulation. It is discussed in more detail in the chapter of Eichinger et al. in this volume. 

M. A. Balsera, W. Wriggers, Y. Oono, and K. Schulten. Principal component analysis and long time protein dynamics. J. Phys. Chem., 100 2567-2572, 1996. 

FIGURE 23.5 Effect of feeding captive male ring-necked pheasant (Ph. colchicus) young a high- or low-protein feed for the first three weeks of life on the expression of wattle coloration (mean+SE) at 20 (open circles) and 40 (filled circles) weeks of age. Coloration was determined using a principal components analysis (PCA) of tristimulus scores (hue, saturation, and brightness) obtained with a Colortron II reflectance spectrophotometer. 

Because protein ROA spectra contain bands characteristic of loops and turns in addition to bands characteristic of secondary structure, they should provide information on the overall three-dimensional solution structure. We are developing a pattern recognition program, based on principal component analysis (PCA), to identify protein folds from ROA spectral band patterns (Blanch etal., 2002b). The method is similar to one developed for the determination of the structure of proteins from VCD (Pancoska etal., 1991) and UVCD (Venyaminov and Yang, 1996) spectra, but is expected to provide enhanced discrimination between different structural types since protein ROA spectra contain many more structure-sensitive bands than do either VCD or UVCD. From the ROA spectral data, the PCA program calculates a set of subspectra that serve as basis functions, the algebraic combination of which with appropriate expansion coefficients can be used to reconstruct any member of the... 

O. D. Sanni, M. S. Wagner, D. G. Briggs, D. G. Castner and J. C. Vickerman, Classification of adsorbed protein static ToF SIMS spectra by principal component analysis and neural networks, Surface and Interface Analysis, 33, 715 728 (2002). 

Fig. 19.9 Left nmrDraw-integrated display of the result of a Principal Component Analysis (PCA) for about 100 2D spectra. Numbers in yellow correspond to spectra in which a testing compound does not lead to shift changes of the protein. Numbers in cyan represent spectra whose PCA...

The reason for the correlation between the localization and the amino acid composition was sought by Andrade et al. (1998). They examined the amino acid composition of proteins with known localization and three-dimensional structure in three ways total composition, surface composition, and interior composition. The principal component analysis showed the best correlation between the surface composition and the localization. Therefore, they concluded that the correlation is the result of evolutionary adaptation of proteins to the surrounding environment. 

In a paper that addresses both these topics, Gordon et al.11 explain how they followed a com mixture fermented by Fusarium moniliforme spores. They followed the concentrations of starch, lipids, and protein throughout the reaction. The amounts of Fusarium and even com were also measured. A multiple linear regression (MLR) method was satisfactory, with standard errors of prediction (SEP) for the constituents being 0.37% for starch, 4.57% for lipid, 4.62% for protein, 2.38% for Fusarium, and 0.16% for com. It may be inferred from the data that PLS or PCA (principal components analysis) may have given more accurate results. 

Diamond (1966) has applied a filtering procedure in the refinement of protein structures, in which poorly determined linear combinations are not varied. In charge density analysis, the principal component analysis has been tested in a refinement of theoretical structure factors on diborane, B2H6, with a formalism including both one-center and two-center overlap terms (Jones et al. 1972). Not unexpectedly, it was found that the sum of the populations of the 2s and spherically averaged 2p shells on the boron atoms constitutes a well-determined eigenparameter, while the difference is very poorly determined. Correlation between one- and two-center terms was also evident in the analysis. 

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