Journal of Biomolecular NMR

, Volume 62, Issue 3, pp 403–409 | Cite as

PPM_One: a static protein structure based chemical shift predictor

  • Dawei Li
  • Rafael Brüschweiler


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.


Protein chemical shift prediction Physics-based predictor Backbone and side-chain chemical shifts Neural network Database analysis 



This work was supported by the NSF (Grant MCB 1360966). R.B. is an Ohio Research Scholar.

Supplementary material

10858_2015_9958_MOESM1_ESM.pdf (186 kb)
Supplementary material 1 (PDF 186 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Campus Chemical Instrument CenterThe Ohio State UniversityColumbusUSA
  2. 2.Department of Chemistry and BiochemistryThe Ohio State UniversityColumbusUSA

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