Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts

One of the major open challenges in structural biology is to achieve effective descriptions of disordered states of proteins. This problem is difficult because these states are conformationally highly heterogeneous and cannot be represented as single structures, and therefore it is necessary to characterize their conformational properties in terms of probability distributions. Here we show that it is possible to obtain highly quantitative information about particularly important types of probability distributions, the populations of secondary structure elements (\u3b1-helix, \u3b2-strand, random coil, and polyproline II), by using the information provided by backbone chemical shifts. The application of this approach to mammalian prions indicates that for these proteins a key role in molecular recognition is played by disordered regions characterized by highly conserved polyproline II populations. We also determine the secondary structure populations of a range of other disordered proteins that are medically relevant, including p53, \u3b1-synuclein, and the A\u3b2 peptide, as well as an oligomeric form of \u3b1B-crystallin. Because chemical shifts are the nuclear magnetic resonance parameters that can be measured under the widest variety of conditions, our approach can be used to obtain detailed information about secondary structure populations for a vast range of different protein states

Improving 3D structure prediction from chemical shift data

We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CSRosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50–100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (\2 A° RMSD from the reference)

e-NMR gLite grid enabled infrastructure

The e-NMR project is an European e-infrastructure that aims at providing the bio-NMR community with a software platform integrating and streamlining computational approaches necessary for NMR data analysis. The infrastructure is grid enabled with fteen gLite based partners sharing computational resources. A main focus of the consortium is to provide protocoled services through easy-to-use web interfaces, while retaining su cient exibility to handle speci c requests by expert users. Various programs relevant for structural biology scientists are grid ported and already available through the e-NMR web portal, including HADDOCK, XPLORNIH, CYANA and CS-ROSETTA among others. A general overview of the project current status toward EGEE/EGI integration, as well as brief guidelines on how to become an e-NMR site/user will be considered. With more than 170 registered users, enmr.eu is currently the second largest virtual organization in the life sciences. The state of the project can be found on the web page http://www.enmr.eu

CASD-NMR: critical assessment of automated structure determination by NMR.

Contains fulltext : 75259.pdf (publisher's version ) (Closed access