Literature DB >> 26091586

PPM_One: a static protein structure based chemical shift predictor.

Dawei Li1, Rafael Brüschweiler.   

Abstract

We mined the most recent editions of the BioMagResDataBank and the protein data bank to parametrize a new empirical knowledge-based chemical shift predictor of protein backbone atoms using either a linear or an artificial neural network model. The resulting chemical shift predictor PPM_One accepts a single static 3D structure as input and emulates the effect of local protein dynamics via interatomic steric contacts. Furthermore, the chemical shift prediction was extended to most side-chain protons and it is found that the prediction accuracy is at a level allowing an independent assessment of stereospecific assignments. For a previously established set of test proteins some overall improvement was achieved over current top-performing chemical shift prediction programs.

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Year:  2015        PMID: 26091586     DOI: 10.1007/s10858-015-9958-z

Source DB:  PubMed          Journal:  J Biomol NMR        ISSN: 0925-2738            Impact factor:   2.835


  37 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Contact model for the prediction of NMR N-H order parameters in globular proteins.

Authors:  Fengli Zhang; Rafael Brüschweiler
Journal:  J Am Chem Soc       Date:  2002-10-30       Impact factor: 15.419

3.  Iterative Optimization of Molecular Mechanics Force Fields from NMR Data of Full-Length Proteins.

Authors:  Da-Wei Li; Rafael Brüschweiler
Journal:  J Chem Theory Comput       Date:  2011-05-05       Impact factor: 6.006

4.  Protein structure determination from NMR chemical shifts.

Authors:  Andrea Cavalli; Xavier Salvatella; Christopher M Dobson; Michele Vendruscolo
Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-29       Impact factor: 11.205

5.  Consistent blind protein structure generation from NMR chemical shift data.

Authors:  Yang Shen; Oliver Lange; Frank Delaglio; Paolo Rossi; James M Aramini; Gaohua Liu; Alexander Eletsky; Yibing Wu; Kiran K Singarapu; Alexander Lemak; Alexandr Ignatchenko; Cheryl H Arrowsmith; Thomas Szyperski; Gaetano T Montelione; David Baker; Ad Bax
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-07       Impact factor: 11.205

6.  All-atom contact model for understanding protein dynamics from crystallographic B-factors.

Authors:  Da-Wei Li; Rafael Brüschweiler
Journal:  Biophys J       Date:  2009-04-22       Impact factor: 4.033

7.  4D prediction of protein (1)H chemical shifts.

Authors:  Juuso Lehtivarjo; Tommi Hassinen; Samuli-Petrus Korhonen; Mikael Peräkylä; Reino Laatikainen
Journal:  J Biomol NMR       Date:  2009-10-30       Impact factor: 2.835

8.  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

9.  De novo protein structure generation from incomplete chemical shift assignments.

Authors:  Yang Shen; Robert Vernon; David Baker; Ad Bax
Journal:  J Biomol NMR       Date:  2008-11-26       Impact factor: 2.835

10.  CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data.

Authors:  David S Wishart; David Arndt; Mark Berjanskii; Peter Tang; Jianjun Zhou; Guohui Lin
Journal:  Nucleic Acids Res       Date:  2008-05-30       Impact factor: 16.971

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  18 in total

Review 1.  Hybrid methods for combined experimental and computational determination of protein structure.

Authors:  Justin T Seffernick; Steffen Lindert
Journal:  J Chem Phys       Date:  2020-12-28       Impact factor: 3.488

Review 2.  Using quantum chemistry to estimate chemical shifts in biomolecules.

Authors:  David A Case
Journal:  Biophys Chem       Date:  2020-09-16       Impact factor: 2.352

3.  Measuring the signs of the methyl 1H chemical shift differences between major and 'invisible' minor protein conformational states using methyl 1H multi-quantum spectroscopy.

Authors:  Anusha B Gopalan; Pramodh Vallurupalli
Journal:  J Biomol NMR       Date:  2018-03-21       Impact factor: 2.835

4.  Structural Characterization of a Heparan Sulfate Pentamer Interacting with LAR-Ig1-2.

Authors:  Qi Gao; Jeong-Yeh Yang; Kelley W Moremen; John G Flanagan; James H Prestegard
Journal:  Biochemistry       Date:  2018-04-03       Impact factor: 3.162

5.  NMR assignments of sparsely labeled proteins using a genetic algorithm.

Authors:  Qi Gao; Gordon R Chalmers; Kelley W Moremen; James H Prestegard
Journal:  J Biomol NMR       Date:  2017-03-13       Impact factor: 2.835

6.  NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins.

Authors:  Gordon R Chalmers; Alexander Eletsky; Laura C Morris; Jeong-Yeh Yang; Fang Tian; Robert J Woods; Kelley W Moremen; James H Prestegard
Journal:  J Mol Biol       Date:  2019-04-26       Impact factor: 5.469

7.  Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations.

Authors:  Elena Papaleo
Journal:  Methods Mol Biol       Date:  2021

8.  Structural Aspects of Heparan Sulfate Binding to Robo1-Ig1-2.

Authors:  Qi Gao; Cheng-Yu Chen; Chengli Zong; Shuo Wang; Annapoorani Ramiah; Pradeep Prabhakar; Laura C Morris; Geert-Jan Boons; Kelley W Moremen; James H Prestegard
Journal:  ACS Chem Biol       Date:  2016-09-29       Impact factor: 5.100

9.  NMR characterization of HtpG, the E. coli Hsp90, using sparse labeling with 13C-methyl alanine.

Authors:  Kari Pederson; Gordon R Chalmers; Qi Gao; Daniel Elnatan; Theresa A Ramelot; Li-Chung Ma; Gaetano T Montelione; Michael A Kennedy; David A Agard; James H Prestegard
Journal:  J Biomol NMR       Date:  2017-06-26       Impact factor: 2.835

10.  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

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