Literature DB >> 29777498

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

Piotr Klukowski1, Michał Augoff2, Maciej Zamorski2, Adam Gonczarek2,3, Michał J Walczak4,3.   

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.

Entities:  

Keywords:  Chemical shift assignment; Mixture models; Spin system identification

Mesh:

Substances:

Year:  2018        PMID: 29777498     DOI: 10.1007/s10858-018-0185-2

Source DB:  PubMed          Journal:  J Biomol NMR        ISSN: 0925-2738            Impact factor:   2.835


  13 in total

1.  Assignment validation software suite for the evaluation and presentation of protein resonance assignment data.

Authors:  Hunter N B Moseley; Gurmukh Sahota; Gaetano T Montelione
Journal:  J Biomol NMR       Date:  2004-04       Impact factor: 2.835

2.  A probabilistic approach for validating protein NMR chemical shift assignments.

Authors:  Bowei Wang; Yunjun Wang; David S Wishart
Journal:  J Biomol NMR       Date:  2010-05-06       Impact factor: 2.835

3.  Error tolerant NMR backbone resonance assignment and automated structure generation.

Authors:  Babak Alipanahi; Xin Gao; Emre Karakoc; Shuai Cheng Li; Frank Balbach; Guangyu Feng; Logan Donaldson; Ming Li
Journal:  J Bioinform Comput Biol       Date:  2011-02       Impact factor: 1.122

4.  A new algorithm for reliable and general NMR resonance assignment.

Authors:  Elena Schmidt; Peter Güntert
Journal:  J Am Chem Soc       Date:  2012-07-23       Impact factor: 15.419

5.  NMRNet: a deep learning approach to automated peak picking of protein NMR spectra.

Authors:  Piotr Klukowski; Michal Augoff; Maciej Zieba; Maciej Drwal; Adam Gonczarek; Michal J Walczak
Journal:  Bioinformatics       Date:  2018-08-01       Impact factor: 6.937

6.  Automated probabilistic method for assigning backbone resonances of (13C,15N)-labeled proteins.

Authors:  J A Lukin; A P Gove; S N Talukdar; C Ho
Journal:  J Biomol NMR       Date:  1997-02       Impact factor: 2.835

7.  Validation of archived chemical shifts through atomic coordinates.

Authors:  Wolfgang Rieping; Wim F Vranken
Journal:  Proteins       Date:  2010-08-15

8.  Amino acid type determination in the sequential assignment procedure of uniformly 13C/15N-enriched proteins.

Authors:  S Grzesiek; A Bax
Journal:  J Biomol NMR       Date:  1993-03       Impact factor: 2.835

9.  BioMagResBank.

Authors:  Eldon L Ulrich; Hideo Akutsu; Jurgen F Doreleijers; Yoko Harano; Yannis E Ioannidis; Jundong Lin; Miron Livny; Steve Mading; Dimitri Maziuk; Zachary Miller; Eiichi Nakatani; Christopher F Schulte; David E Tolmie; R Kent Wenger; Hongyang Yao; John L Markley
Journal:  Nucleic Acids Res       Date:  2007-11-04       Impact factor: 16.971

10.  Automated and assisted RNA resonance assignment using NMR chemical shift statistics.

Authors:  Thomas Aeschbacher; Elena Schmidt; Markus Blatter; Christophe Maris; Olivier Duss; Frédéric H-T Allain; Peter Güntert; Mario Schubert
Journal:  Nucleic Acids Res       Date:  2013-08-05       Impact factor: 16.971

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