An integrated platform for automated analysis of protein NMR structures

Yuanpeng Janet Huang, Hunter N.B. Moseley, Michael C. Baran, Cheryl Arrowsmith, Robert Powers, Roberto Tejero, Thomas Szyperski, Gaetano T. Montelione

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


Recent developments provide automated analysis of NMR assignments and three-dimensional (3D) structures of proteins. These approaches are generally applicable to proteins ranging from about 50 to 150 amino acids. In this chapter, we summarize progress by the Northeast Structural Genomics Consortium in standardizing the NMR data collection process for protein structure determination and in building an integrated platform for automated protein NMR structure analysis. Our integrated platform includes the following principal steps: (1) standardized NMR data collection, (2) standardized data processing (including spectral referencing and Fourier transformation), (3) automated peak picking and peak list editing, (4) automated analysis of resonance assignments, (5) automated analysis of NOESY data together with 3D structure determination, and (6) methods for protein structure validation. In particular, the software AutoStructure for automated NOESY data analysis is described in this chapter, together with a discussion of practical considerations for its use in high-throughput structure production efforts. The critical area of data quality assessment has evolved significantly over the past few years and involves evaluation of both intermediate and final peak lists, resonance assignments, and structural information derived from the NMR data. Methods for quality control of each of the major automated analysis steps in our platform are also discussed. Despite significant remaining challenges, when good quality data are available, automated analysis of protein NMR assignments and structures with this platform is both fast and reliable.

Original languageEnglish
Pages (from-to)111-141
Number of pages31
JournalMethods in Enzymology
StatePublished - 2005

Bibliographical note

Funding Information:
We thank J. Aramini, A. Bhattacharya, G. Sahota, D. Snyder, G. V. T. Swapna, and D. Zheng for useful discussions and for their efforts over the past several years in developing automated NMR data analysis algorithms and software. The authors' recent work on automated NMR data analysis has been supported by the NIH Protein Structure Initiative (P50-GM62413).

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology


Dive into the research topics of 'An integrated platform for automated analysis of protein NMR structures'. Together they form a unique fingerprint.

Cite this