Literature DB >> 11890823

De novo determination of protein backbone structure from residual dipolar couplings using Rosetta.

Carol A Rohl1, David Baker.   

Abstract

As genome-sequencing projects rapidly increase the database of protein sequences, the gap between known sequences and known structures continues to grow exponentially, increasing the demand to accelerate structure determination methods. Residual dipolar couplings (RDCs) are an attractive source of experimental restraints for NMR structure determination, particularly rapid, high-throughput methods, because they yield both local and long-range orientational information and can be easily measured and assigned once the backbone resonances of a protein have been assigned. While very extensive RDC data sets have been used to determine the structure of ubiquitin, it is unclear to what extent such methods will generalize to larger proteins with less complete data sets. Here we incorporate experimental RDC restraints into Rosetta, an ab initio structure prediction method, and demonstrate that the combined algorithm provides a general method for de novo determination of a variety of protein folds from RDC data. Backbone structures for multiple proteins up to approximately 125 residues in length and spanning a range of topological complexities are rapidly and reproducibly generated using data sets that are insufficient in isolation to uniquely determine the protein fold de novo, although ambiguities and errors are observed for proteins with symmetry about an axis of the alignment tensor. The models generated are not high-resolution structures completely defined by experimental data but are sufficiently accurate to accelerate traditional high-resolution NMR structure determination and provide structure-based functional insights.

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Year:  2002        PMID: 11890823     DOI: 10.1021/ja016880e

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  74 in total

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Journal:  Proteins       Date:  2011-12-13

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4.  Automated protein fold determination using a minimal NMR constraint strategy.

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5.  PROSHIFT: protein chemical shift prediction using artificial neural networks.

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Journal:  J Biomol NMR       Date:  2003-05       Impact factor: 2.835

6.  Protein structure prediction using sparse dipolar coupling data.

Authors:  Youxing Qu; Jun-tao Guo; Victor Olman; Ying Xu
Journal:  Nucleic Acids Res       Date:  2004-01-26       Impact factor: 16.971

7.  Improving the accuracy of NMR structures of large proteins using pseudocontact shifts as long-range restraints.

Authors:  Vadim Gaponenko; Siddhartha P Sarma; Amanda S Altieri; David A Horita; Jess Li; R Andrew Byrd
Journal:  J Biomol NMR       Date:  2004-03       Impact factor: 2.835

8.  Accurate and automated classification of protein secondary structure with PsiCSI.

Authors:  Ling-Hong Hung; Ram Samudrala
Journal:  Protein Sci       Date:  2003-02       Impact factor: 6.725

9.  Determination of protein global folds using backbone residual dipolar coupling and long-range NOE restraints.

Authors:  Alexander W Giesen; Steve W Homans; Jonathan Miles Brown
Journal:  J Biomol NMR       Date:  2003-01       Impact factor: 2.835

10.  Role for NMR in structural genomics.

Authors:  Michael A Kennedy; Gaetano T Montelione; Cheryl H Arrowsmith; John L Markley
Journal:  J Struct Funct Genomics       Date:  2002
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