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

Piotr KlukowskiMichał AugoffMaciej ZamorskiAdam GonczarekMichał J Walczak
Published in: Journal of biomolecular NMR (2018)
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.
Keyphrases
  • amino acid
  • magnetic resonance
  • protein protein
  • machine learning
  • deep learning
  • density functional theory
  • high throughput
  • binding protein
  • multidrug resistant
  • solid state
  • small molecule