Dataset Information

Improvement and analysis of computational methods for prediction of residual dipolar couplings.

ABSTRACT: We describe a new, computationally efficient method for computing the molecular alignment tensor based on the molecular shape. The increase in speed is achieved by re-expressing the problem as one of numerical integration, rather than a simple uniform sampling (as in the PALES method), and by using a convex hull rather than a detailed representation of the surface of a molecule. This method is applicable to bicelles, PEG/hexanol, and other alignment media that can be modeled by steric restrictions introduced by a planar barrier. This method is used to further explore and compare various representations of protein shape by an equivalent ellipsoid. We also examine the accuracy of the alignment tensor and residual dipolar couplings (RDC) prediction using various ab initio methods. We separately quantify the inaccuracy in RDC prediction caused by the inaccuracy in the orientation and in the magnitude of the alignment tensor, concluding that orientation accuracy is much more important in accurate prediction of RDCs.

SUBMITTER: Berlin K

PROVIDER: S-EPMC2763024 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improvement and analysis of computational methods for prediction of residual dipolar couplings.

Berlin Konstantin K O'Leary Dianne P DP Fushman David D

Journal of magnetic resonance (San Diego, Calif. : 1997) 20090805 1

We describe a new, computationally efficient method for computing the molecular alignment tensor based on the molecular shape. The increase in speed is achieved by re-expressing the problem as one of numerical integration, rather than a simple uniform sampling (as in the PALES method), and by using a convex hull rather than a detailed representation of the surface of a molecule. This method is applicable to bicelles, PEG/hexanol, and other alignment media that can be modeled by steric restrictio ...[more]

PMID: 19700353

Similar Datasets

Project description:Disulfide bridges in proteins are formed by the oxidation of pairs of cysteine residues. These cross-links play a critical role in stabilizing the 3D-structure of small disulfide rich polypeptides such as hormones and venom toxins. The arrangement of the multiple disulfide bonds directs the peptide fold into distinct structural motifs that have evolved for resistance against biochemical and physical insults. These structural scaffolds have, therefore, proven to be very attractive in bioengineering efforts to develop novel biologics with applications in health and agriculture. Structural characterization of small disulfide rich peptides (DRPs) presents unique challenges when using commonly applied biophysical methods. NMR is the most commonly used method for studying such molecules, where the relatively small size of these molecules results in highly precise structural ensembles defined by a large number of distance and dihedral angle restraints per amino acid. However, in NMR the sulfur atoms that are involved in three of the five dihedral angles in a disulfide bond cannot be readily measured. Given the central role of disulfide bonds in the structure of these molecules, it is unclear what the inherent resolution of such NMR structures is when using traditional NMR methods. Here, we use an extensive set of long-range residual dipolar couplings (RDCs) to assess the resolution of the NMR structure of a disulfide-rich peptide. We find that structures based primarily on NOEs, yield ensembles that are equivalent to a crystallographic resolution of 2-3 Å in resolution, and that incorporation of RDCs reduces this to ~1-1.5 Å resolution. At this resolution the sidechain of ordered amino acids can be defined accurately, allowing the geometry of the cysteine bridges to be better defined, and allowing for disulfide-bond connectivities to be determined with high confidence. The observed improvements in resolution when using RDCs is remarkable considering the small size of these peptides.

Dataset Information

Improvement and analysis of computational methods for prediction of residual dipolar couplings.

Publications

Improvement and analysis of computational methods for prediction of residual dipolar couplings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets