Literature DB >> 24082119

Physics-based method to validate and repair flaws in protein structures.

Osvaldo A Martin1, Yelena A Arnautova, Alejandro A Icazatti, Harold A Scheraga, Jorge A Vila.   

Abstract

A method that makes use of information provided by the combination of (13)C(α) and (13)C(β) chemical shifts, computed at the density functional level of theory, enables one to (i) validate, at the residue level, conformations of proteins and detect backbone or side-chain flaws by taking into account an ensemble average of chemical shifts over all of the conformations used to represent a protein, with a sensitivity of ∼90%; and (ii) provide a set of (χ1/χ2) torsional angles that leads to optimal agreement between the observed and computed (13)C(α) and (13)C(β) chemical shifts. The method has been incorporated into the CheShift-2 protein validation Web server. To test the reliability of the provided set of (χ1/χ2) torsional angles, the side chains of all reported conformations of five NMR-determined protein models were refined by a simple routine, without using NOE-based distance restraints. The refinement of each of these five proteins leads to optimal agreement between the observed and computed (13)C(α) and (13)C(β) chemical shifts for ∼94% of the flaws, on average, without introducing a significantly large number of violations of the NOE-based distance restraints for a distance range ≤ 0.5 , in which the largest number of distance violations occurs. The results of this work suggest that use of the provided set of (χ1/χ2) torsional angles together with other observables, such as NOEs, should lead to a fast and accurate refinement of the side-chain conformations of protein models.

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Year:  2013        PMID: 24082119      PMCID: PMC3801053          DOI: 10.1073/pnas.1315525110

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  31 in total

1.  Protein structure validation using side-chain chemical shifts.

Authors:  Aleksandr B Sahakyan; Andrea Cavalli; Wim F Vranken; Michele Vendruscolo
Journal:  J Phys Chem B       Date:  2012-04-15       Impact factor: 2.991

2.  Using NMR chemical shifts as structural restraints in molecular dynamics simulations of proteins.

Authors:  Paul Robustelli; Kai Kohlhoff; Andrea Cavalli; Michele Vendruscolo
Journal:  Structure       Date:  2010-08-11       Impact factor: 5.006

3.  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

4.  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

5.  Quantum-mechanics-derived 13Calpha chemical shift server (CheShift) for protein structure validation.

Authors:  Jorge A Vila; Yelena A Arnautova; Osvaldo A Martin; Harold A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  2009-09-08       Impact factor: 11.205

6.  Structure refinement of protein model decoys requires accurate side-chain placement.

Authors:  Mark A Olson; Michael S Lee
Journal:  Proteins       Date:  2012-11-12

7.  SHIFTX2: significantly improved protein chemical shift prediction.

Authors:  Beomsoo Han; Yifeng Liu; Simon W Ginzinger; David S Wishart
Journal:  J Biomol NMR       Date:  2011-03-30       Impact factor: 2.835

8.  Blind testing of routine, fully automated determination of protein structures from NMR data.

Authors:  Antonio Rosato; James M Aramini; Cheryl Arrowsmith; Anurag Bagaria; David Baker; Andrea Cavalli; Jurgen F Doreleijers; Alexander Eletsky; Andrea Giachetti; Paul Guerry; Aleksandras Gutmanas; Peter Güntert; Yunfen He; Torsten Herrmann; Yuanpeng J Huang; Victor Jaravine; Hendrik R A Jonker; Michael A Kennedy; Oliver F Lange; Gaohua Liu; Thérèse E Malliavin; Rajeswari Mani; Binchen Mao; Gaetano T Montelione; Michael Nilges; Paolo Rossi; Gijs van der Schot; Harald Schwalbe; Thomas A Szyperski; Michele Vendruscolo; Robert Vernon; Wim F Vranken; Sjoerd de Vries; Geerten W Vuister; Bin Wu; Yunhuang Yang; Alexandre M J J Bonvin
Journal:  Structure       Date:  2012-02-08       Impact factor: 5.006

9.  Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks.

Authors:  Yang Shen; Ad Bax
Journal:  J Biomol NMR       Date:  2013-06-02       Impact factor: 2.835

10.  CheShift-2: graphic validation of protein structures.

Authors:  Osvaldo A Martin; Jorge A Vila; Harold A Scheraga
Journal:  Bioinformatics       Date:  2012-04-11       Impact factor: 6.937

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  5 in total

Review 1.  My 65 years in protein chemistry.

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

2.  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

3.  Protein structure refinement using a quantum mechanics-based chemical shielding predictor.

Authors:  Lars A Bratholm; Jan H Jensen
Journal:  Chem Sci       Date:  2016-12-01       Impact factor: 9.825

4.  Detection of methylation, acetylation and glycosylation of protein residues by monitoring (13)C chemical-shift changes: A quantum-chemical study.

Authors:  Pablo G Garay; Osvaldo A Martin; Harold A Scheraga; Jorge A Vila
Journal:  PeerJ       Date:  2016-07-21       Impact factor: 2.984

5.  Classification of RNA backbone conformations into rotamers using 13C' chemical shifts: exploring how far we can go.

Authors:  Alejandro A Icazatti; Juan M Loyola; Igal Szleifer; Jorge A Vila; Osvaldo A Martin
Journal:  PeerJ       Date:  2019-10-21       Impact factor: 2.984

  5 in total

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