Literature DB >> 18780343

Performance of density functional models to reproduce observed (13)C(alpha) chemical shifts of proteins in solution.

Jorge A Vila1, Héctor A Baldoni, Harold A Scheraga.   

Abstract

The purpose of this work is to test several density functional models (namely, OPBE, O3LYP, OPW91, BPW91, OB98, BPBE, B971, OLYP, PBE1PBE, and B3LYP) to determine their accuracy and speed for computing (13)C(alpha) chemical shifts in proteins. The test is applied to 10 NMR-derived conformations of the 76-residue alpha/beta protein ubiquitin (protein data bank id 1D3Z). With each functional, the (13)C(alpha) shielding was computed for 760 amino acid residues by using a combination of approaches that includes, but is not limited to, treating each amino acid X in the sequence as a terminally blocked tripeptide with the sequence Ac-GXG-NMe in the conformation of the regularized experimental protein structure. As computation of the (13)C(alpha) chemical shifts, not their shielding, is the main goal of this work, a computation of the (13)C(alpha) shielding of the reference, namely, tetramethylsilane, is investigated here and an effective and a computed tetramethylsilane shielding value for each of the functionals is provided. Despite observed small differences among all functionals tested, the results indicate that four of them, namely, OPBE, OPW91, OB98, and OLYP, provide the most accurate functionals with which to reproduce observed (13)C(alpha) chemical shifts of proteins in solution, and are among the faster ones. This study also provides evidence for the applicability of these functionals to proteins of any size or class, and for the validation of our previous results and conclusions, obtained from calculations with the slower B3LYP functional. (c) 2008 Wiley Periodicals, Inc.

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Year:  2009        PMID: 18780343      PMCID: PMC2779021          DOI: 10.1002/jcc.21105

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  20 in total

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  8 in total

1.  Sequential nearest-neighbor effects on computed 13Calpha chemical shifts.

Authors:  Jorge A Vila; Pedro Serrano; Kurt Wüthrich; Harold A Scheraga
Journal:  J Biomol NMR       Date:  2010-07-20       Impact factor: 2.835

2.  Assessing the fractions of tautomeric forms of the imidazole ring of histidine in proteins as a function of pH.

Authors:  Jorge A Vila; Yelena A Arnautova; Yury Vorobjev; Harold A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  2011-03-21       Impact factor: 11.205

3.  What can we learn by computing 13Calpha chemical shifts for X-ray protein models?

Authors:  Yelena A Arnautova; Jorge A Vila; Osvaldo A Martin; Harold A Scheraga
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2009-06-20

4.  Factors affecting the computation of the 13C shielding in disaccharides.

Authors:  Pablo G Garay; Osvaldo A Martin; Harold A Scheraga; Jorge A Vila
Journal:  J Comput Chem       Date:  2014-07-28       Impact factor: 3.376

Review 5.  My 65 years in protein chemistry.

Authors:  Harold A Scheraga
Journal:  Q Rev Biophys       Date:  2015-04-08       Impact factor: 5.318

6.  Fermentation and Cost-Effective 13C/15N Labeling of the Nonribosomal Peptide Gramicidin S for Nuclear Magnetic Resonance Structure Analysis.

Authors:  Marina Berditsch; Sergii Afonin; Anna Steineker; Nataliia Orel; Igor Jakovkin; Christian Weber; Anne S Ulrich
Journal:  Appl Environ Microbiol       Date:  2015-03-20       Impact factor: 4.792

7.  Assessing the accuracy of protein structures by quantum mechanical computations of 13C(alpha) chemical shifts.

Authors:  Jorge A Vila; Harold A Scheraga
Journal:  Acc Chem Res       Date:  2009-10-20       Impact factor: 22.384

8.  ProCS15: a DFT-based chemical shift predictor for backbone and Cβ atoms in proteins.

Authors:  Anders S Larsen; Lars A Bratholm; Anders S Christensen; Maher Channir; Jan H Jensen
Journal:  PeerJ       Date:  2015-10-20       Impact factor: 2.984

  8 in total

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