PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-3 (3)
 

Clipboard (0)
None
Journals
Authors
Year of Publication
Document Types
2.  Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics 
Bioinformatics  2009;25(5):606-614.
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.
Availability: http://ignm.ccbb.pitt.edu/oPCA_Online.htm
Contact: lwy1@iam.u-tokyo.ac.jp
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btp023
PMCID: PMC2647834  PMID: 19147661
3.  iGNM: a database of protein functional motions based on Gaussian Network Model 
Bioinformatics (Oxford, England)  2005;21(13):2978-2987.
Motivation
The knowledge of protein structure is not sufficient for understanding and controlling its function. Function is a dynamic property. Although protein structural information has been rapidly accumulating in databases, little effort has been invested to date toward systematically characterizing protein dynamics. The recent success of analytical methods based on elastic network models, and in particular the Gaussian Network Model (GNM), permits us to perform a high-throughput analysis of the collective dynamics of proteins.
Results
We computed the GNM dynamics for 20 058 structures from the Protein Data Bank, and generated information on the equilibrium dynamics at the level of individual residues. The results are stored on a web-based system called i GNM and configured so as to permit the users to visualize or download the results through a standard web browser using a simple search engine. Static and animated images for describing the conformational mobility of proteins over a broad range of normal modes are accessible, along with an online calculation engine available for newly deposited structures. A case study of the dynamics of 20 non-homologous hydrolases is presented to illustrate the utility of the iGNM database for identifying key residues that control the cooperative motions and revealing the connection between collective dynamics and catalytic activity.
Availability
http://ignm.ccbb.pitt.edu/
doi:10.1093/bioinformatics/bti469
PMCID: PMC1752228  PMID: 15860562

Results 1-3 (3)