This Article Full Text Full Text (PDF) Supplemental All Versions of this Article: biophysj.105.076836v1 90/12/4327    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Zheng, W. Articles by Brooks, B. R. Search for Related Content PubMed PubMed Citation Articles by Zheng, W. Articles by Brooks, B. R.

Modeling Protein Conformational Changes by Iterative Fitting of Distance Constraints Using Reoriented Normal Modes

Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892

Correspondence: Address reprint requests to Wenjun Zheng, zhengwj{at}helix.nih.gov.

Recently we have developed a normal-modes-based algorithm that predicts the direction of protein conformational changes given the initial state crystal structure together with a small number of pairwise distance constraints for the end state. Here we significantly extend this method to accurately model both the direction and amplitude of protein conformational changes. The new protocol implements a multisteps search in the conformational space that is driven by iteratively minimizing the error of fitting the given distance constraints and simultaneously enforcing the restraint of low elastic energy. At each step, an incremental structural displacement is computed as a linear combination of the lowest 10 normal modes derived from an elastic network model, whose eigenvectors are reorientated to correct for the distortions caused by the structural displacements in the previous steps. We test this method on a list of 16 pairs of protein structures for which relatively large conformational changes are observed (root mean square deviation >3 Å), using up to 10 pairwise distance constraints selected by a fluctuation analysis of the initial state structures. This method has achieved a near-optimal performance in almost all cases, and in many cases the final structural models lie within root mean square deviation of 1 2 Å from the native end state structures.

This article has been cited by other articles:

				G. Cui and K. M. Merz Jr. The Intrinsic Dynamics and Function of Nickel-Binding Regulatory Protein: Insights from Elastic Network Analysis Biophys. J., May 15, 2008; 94(10): 3769 - 3778. [Abstract] [Full Text] [PDF]

				W. Zheng A Unification of the Elastic Network Model and the Gaussian Network Model for Optimal Description of Protein Conformational Motions and Fluctuations Biophys. J., May 15, 2008; 94(10): 3853 - 3857. [Abstract] [Full Text] [PDF]