Application of Dirichlet process mixture model to the identification of spin systems in protein NMR spectra

Abstract

Analysis of structure, function and interactions of proteins by NMR spectroscopy usually requires the assignment of resonances to the corresponding nuclei in protein. This task, although automated by methods such as FLYA or PINE, is still frequently performed manually. To facilitate the manual sequence-specific chemical shift assignment of complex proteins, we propose a method based on Dirichlet process mixture model (DPMM) that performs automated matching of groups of signals observed in NMR spectra to corresponding nuclei in protein sequence. The model has been extensively tested on 80 proteins retrieved from the BMRB database and has shown superior performance to the reference method.

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Data availability

The proposed model is implemented as part of the Dumpling software (available at https://dumpling.bio/). The DPMM model generates recommendations to a user, who performs a manual sequence-specific resonance assignment in Sparky-like graphical interface (Figs. S1–S4).

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Acknowledgements

The research has been co-financed by the Ministry of Science and Higher Education, Republic of Poland: Adam Gonczarek, Grant No. 0402/0082/17.

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Authors

Contributions

PK designed the model with the support of AG and MA; PK and MA implemented the model and the experiments; PK and MA designed the experiments; PK, MA, AG, MJW discussed the results and wrote the manuscript, MZ prepared the Dumpling components to make the model publicly available.

Corresponding author

Correspondence to Piotr Klukowski.

Additional information

Piotr Klukowski and Michał Augoff would like to be considered as joint first author.

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Supplementary material 1 (PDF 5.51 MB)

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Klukowski, P., Augoff, M., Zamorski, M. et al. Application of Dirichlet process mixture model to the identification of spin systems in protein NMR spectra. J Biomol NMR 71, 11–18 (2018). https://doi.org/10.1007/s10858-018-0185-2

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Keywords

  • Chemical shift assignment
  • Mixture models
  • Spin system identification