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Probabilistic validation of protein NMR chemical shift assignments

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Abstract

Data validation plays an important role in ensuring the reliability and reproducibility of studies. NMR investigations of the functional properties, dynamics, chemical kinetics, and structures of proteins depend critically on the correctness of chemical shift assignments. We present a novel probabilistic method named ARECA for validating chemical shift assignments that relies on the nuclear Overhauser effect data . ARECA has been evaluated through its application to 26 case studies and has been shown to be complementary to, and usually more reliable than, approaches based on chemical shift databases. ARECA is available online at http://areca.nmrfam.wisc.edu/.

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Acknowledgments

HD thanks Alireza Siahpirani (Wisconsin Institute of Discovery) for invaluable discussions about the computational aspects of the introduced method and Dr. Vincent Chen (National Magnetic Resonance Facility at Madison) for helpful discussions concerning applications of the MolProbity program. For the use of web portals, computing and storage facilities, we thank the WeNMR project (European FP7 e-Infrastructure grant, contract no. 261572, www.wenmr.eu), supported by the European Grid Initiative (EGI) through the national GRID Initiatives of Belgium, France, Italy, Germany, the Netherlands, Poland, Portugal, Spain, UK, South Africa, Malaysia, Taiwan, the Latin America GRID infrastructure via the Gisela project, the International Desktop Grid Federation (IDGF) with its volunteers, and the US Open Science Grid (OSG). This study was carried out at the National Magnetic Resonance Facility at Madison, which is supported by US National Institutes of Health (NIH) grant P41GM103399.

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Authors and Affiliations

  1. Graduate Program in Biophysics, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA

    Hesam Dashti

  2. Biochemistry Department, National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI, USA

    Hesam Dashti, Marco Tonelli, Woonghee Lee, William M. Westler, Gabriel Cornilescu & John L. Markley

  3. BioMagResBank, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA

    Eldon L. Ulrich & John L. Markley

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  1. Hesam Dashti
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  2. Marco Tonelli
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  3. Woonghee Lee
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  4. William M. Westler
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  5. Gabriel Cornilescu
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  6. Eldon L. Ulrich
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  7. John L. Markley
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Correspondence to John L. Markley.

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Dashti, H., Tonelli, M., Lee, W. et al. Probabilistic validation of protein NMR chemical shift assignments. J Biomol NMR 64, 17–25 (2016). https://doi.org/10.1007/s10858-015-0007-8

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