STAP Refinement of the NMR database: a database of 2405 refined solution NMR structures

Yang, Joshua Sung Woo; Kim, Ji Han; Oh, Sang Ho; Han, Gukjeong; Lee, Sanghyuk; Lee, Jinhyuk

doi:10.1093/nar/gkr1021

Cited by 14 publications

(16 citation statements)

References 27 publications

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“…Therefore, in this work, we performed refinements using the knowledge-based potential (STAP) developed by our group on 134 NMR structures in the Protein Data Bank (PDB). The efficiency of STAP was verified by a previous study [26] . Unlike the previous successful study on NMR structure refinement, we did not use the experimental data on the NMR structures (NOE data).…”

Section: Introductionsupporting

confidence: 58%

“…The protein structure refinement process has prevailed in NMR structures, especially because the quality of NMR structures is less accurate than that of X-ray crystallography structures [22] , [23] , which arises from the dynamic motion of proteins in solution and weak Nuclear Overhauser Effect (NOE) signal intensity. Out of necessity, several NMR structure refinement databases were introduced: REcalculated COORdinates Database (RECOORD), a database of REfined solution NMR structures (DRESS) and statistical torsion angle potential (STAP) [24] – [26] . Mao et.…”

Section: Introductionmentioning

confidence: 99%

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Protein NMR Structures Refined without NOE Data

et al. 2014

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The refinement of low-quality structures is an important challenge in protein structure prediction. Many studies have been conducted on protein structure refinement; the refinement of structures derived from NMR spectroscopy has been especially intensively studied. In this study, we generated flat-bottom distance potential instead of NOE data because NOE data have ambiguity and uncertainty. The potential was derived from distance information from given structures and prevented structural dislocation during the refinement process. A simulated annealing protocol was used to minimize the potential energy of the structure. The protocol was tested on 134 NMR structures in the Protein Data Bank (PDB) that also have X-ray structures. Among them, 50 structures were used as a training set to find the optimal “width” parameter in the flat-bottom distance potential functions. In the validation set (the other 84 structures), most of the 12 quality assessment scores of the refined structures were significantly improved (total score increased from 1.215 to 2.044). Moreover, the secondary structure similarity of the refined structure was improved over that of the original structure. Finally, we demonstrate that the combination of two energy potentials, statistical torsion angle potential (STAP) and the flat-bottom distance potential, can drive the refinement of NMR structures.

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Section: Introductionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Protein NMR Structures Refined without NOE Data

et al. 2014

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“…9,42,43 Although originally applied to solution Table II. Each statistic is a score: the larger the better.…”

Section: Discussionmentioning

confidence: 99%

Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures

2012

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Statistical potentials that embody torsion angle probability densities in databases of high-quality X-ray protein structures supplement the incomplete structural information of experimental nuclear magnetic resonance (NMR) datasets. By biasing the conformational search during the course of structure calculation toward highly populated regions in the database, the resulting protein structures display better validation criteria and accuracy. Here, a new statistical torsion angle potential is developed using adaptive kernel density estimation to extract probability densities from a large database of more than 10 6 quality-filtered amino acid residues. Incorporated into the Xplor-NIH software package, the new implementation clearly outperforms an older potential, widely used in NMR structure elucidation, in that it exhibits simultaneously smoother and sharper energy surfaces, and results in protein structures with improved conformation, nonbonded atomic interactions, and accuracy.

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“…The power of statistical torsional potentials to improve conformational quality criteria in protein NMR structures, such as the reduction of Ramachandran outliers, has been thoroughly documented (Bermejo et al, 2012; Bertini et al, 2003; Clore and Kuszewski, 2002; Kuszewski and Clore, 2000; Kuszewski et al, 1996, 1997; Mertens and Gooley, 2005; Yang et al, 2012). On the other hand, to our knowledge, there is only one comprehensive RNA study on the subject, performed on a single system (Clore and Kuszewski, 2003).…”

Section: Introductionmentioning

confidence: 99%

Improving NMR Structures of RNA

2016

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SUMMARY Here, we show that modern solution NMR structures of RNA exhibit more steric clashes and conformational ambiguities than their crystallographic X-ray counterparts. To tackle these issues, we developed RNA-ff1, a new force field for structure calculation with Xplor-NIH. Using seven published NMR datasets, RNA-ff1 improves covalent geometry and MolProbity validation criteria for clashes and backbone conformation in most cases, relative to both the previous Xplor-NIH force field and the original structures associated with the experimental data. In addition, with smaller base pair step rises in helical stems, RNA-ff1 structures enjoy more favorable base stacking. Finally, structural accuracy improves in the majority cases, as supported by complete residual dipolar coupling cross-validation. Thus, the reported advances show great promise in bridging the quality gap that separates NMR and X-ray structures of RNA.

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STAP Refinement of the NMR database: a database of 2405 refined solution NMR structures

Cited by 14 publications

References 27 publications

Protein NMR Structures Refined without NOE Data

Protein NMR Structures Refined without NOE Data

Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures

Improving NMR Structures of RNA

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