ReviewProgress in nuclear magnetic resonance spectroscopy2012
Chemical shift prediction for protein structure calculation and quality assessment using an optimally parameterized force field.
Review in Progress in nuclear magnetic resonance spectroscopy, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- CheSPI: chemical shift secondary structure population inference.Journal of biomolecular NMR · 2021Article
- Accurate prediction of chemical shifts for aqueous protein structure on "Real World" data.Chemical science · 2020Article
- POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins.Journal of biomolecular NMR · 2018Article
- Protein structure refinement using a quantum mechanics-based chemical shielding predictor.Chemical science · 2017Article
- In situ high-resolution structure of the baseplate antenna complex in Chlorobaculum tepidum.Nature communications · 2016Article
- ARTSY-J: Convenient and precise measurement of (3)JHNHα couplings in medium-size proteins from TROSY-HSQC spectra.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2016Article
- There is Diversity in Disorder-"In all Chaos there is a Cosmos, in all Disorder a Secret Order".Frontiers in molecular biosciences · 2016Article
- Limitations and extensions of the lock-and-key principle: differences between gas state, solution and solid state structures.International journal of molecular sciences · 2015Review
- Article
- Protein-ligand structure guided by backbone and side-chain proton chemical shift perturbations.Journal of biomolecular NMR · 2014Article
- VirtualSpectrum, a tool for simulating peak list for multi-dimensional NMR spectra.Journal of biomolecular NMR · 2014Article
- Automated robust and accurate assignment of protein resonances for solid state NMR.Journal of biomolecular NMR · 2014Article
- Mutation in transforming growth factor beta induced protein associated with granular corneal dystrophy type 1 reduces the proteolytic susceptibility through local structural stabilization.Biochimica et biophysica acta · 2013Article
- Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks.Journal of biomolecular NMR · 2013Article
- Chemical shifts in biomolecules.Current opinion in structural biology · 2013Review
- NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction.BMC bioinformatics · 2013Article
- Improved chemical shift prediction by Rosetta conformational sampling.Journal of biomolecular NMR · 2012Article
- Assessing and refining molecular dynamics simulations of proteins with nuclear magnetic resonance data.Biophysical reviews · 2012Review
- Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction.Journal of biomolecular NMR · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The exquisite sensitivity of chemical shifts as reporters of structural information, and the ability to measure them routinely and accurately, gives great import to formulations that elucidate the structure-chemical-shift relationship. Here we present a new and highly accurate, precise, and robust formulation for the prediction of NMR chemical shifts from protein structures. Our approach, shAIC (shift prediction guided by Akaikes Information Criterion), capitalizes on mathematical ideas and an information-theoretic principle, to represent the functional form of the relationship between structure and chemical shift as a parsimonious sum of smooth analytical potentials which optimally takes into account short-, medium-, and long-range parameters in a nuclei-specific manner to capture potential chemical shift perturbations caused by distant nuclei. shAIC outperforms the state-of-the-art methods that use analytical formulations. Moreover, for structures derived by NMR or structures with novel folds, shAIC delivers better overall results; even when it is compared to sophisticated machine learning approaches. shAIC provides for a computationally lightweight implementation that is unimpeded by molecular size, making it an ideal for use as a force field.
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