Principal components analysis of protein structure ensembles calculated using NMR data.
One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations.
Howe
P W
PW
Analytical Sciences, Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire, UK. peter.howe@syngenta.com
D046911:Macromolecular Substances; D008958:Models, Molecular; D019906:Nuclear Magnetic Resonance, Biomolecular; D010455:Peptides; D025341:Principal Component Analysis; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry
C441459:EF40 peptide; D046911:Macromolecular Substances; D010455:Peptides; D011506:Proteins