Error tolerant nmr backbone resonance assignment and automated structure generation

Babak Alipanahi, Xin Gao, Emre Karakoc, Shuai Cheng Li, Frank Balbach, Guangyu Feng, Logan Donaldson, Ming Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) 15N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25Å, 0.88Å, 1.49Å, and 0.67Å to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at.

Original languageEnglish (US)
Pages (from-to)15-41
Number of pages27
JournalJournal of Bioinformatics and Computational Biology
Volume9
Issue number1
DOIs
StatePublished - Feb 1 2011

Keywords

  • NMR
  • backbone resonance assignment
  • protein structure determination

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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