AboutDownloadsDocumentsForumsSource CodeIssues
Lee-Wei Yang, Eran Eyal, Ivet Bahar and Akio Kitao. Principal Component Analysis of Native Ensembles of Biomolecular Structures (PCA_NEST): Insights into Functional Dynamics. Bioinformatics 25:606-614 (2009)
Abstract    View

Motivation: To efficiently analyze the ‘native ensemble of conformations’ accessible to proteins near their folded state and to extract essential information from observed distributions of conformations, reliable mathematical methods and computational tools are needed. Result: Examination of 24 pairs of structures determined by both NMR and X-ray reveals that the differences in the dynamics of the same protein resolved by the two techniques can be tracked to the most robust low frequency modes elucidated by principal component analysis (PCA) of NMR models. The active sites of enzymes are found to be highly constrained in these PCA modes. Furthermore, the residues predicted to be highly immobile are shown to be evolutionarily conserved, lending support to a PCA-based identification of potential functional sites. An online tool, PCA_NEST, is designed to derive the principal modes of conformational changes from structural ensembles resolved by experiments or generated by computations. Supplementary information: Supplementary data are available at Bioinformatics online.

Provide an easy-to-use application for extracting dominant motions from multiple experimental (or theoretical) structures of a given protein (or other biomolecules).

The concept of ‘native ensemble of conformations’ is such that the experimentally determined (by X-ray, NMR) protein ensembles suggest a handful of intrinsically favored conformational transition pathways that are as 'native' as can be described by first-principle-based physics models. Structural biologists and theoretical chemists are welcome to submit multiple conformers of a given protein to our online server, PCA_NEST. PCA_NEST best-aligns those iteratively until the mean structure in subsequent cycle reaches convergence. It then performs Principal Component Analysis on the best-aligned ensemble to obtain sets of dominant motions (the principal components) of the protein. We have shown previously that low modes derived from elastic network models (ENMs) coincide with PCA_NEST-derived dominant motions suggesting that once perceived structural 'errors' can be described by simple-model-inferred vibrational slow modes.


At the moment we give the web site links to PCA_NEST. We may provide libraries, tools, codes for best-alignment, trajectory file readers and covariance matrix builder/solver in the future.

See all Downloads