Literature DB >> 21328705

Error tolerant NMR backbone resonance assignment and automated structure generation.

Babak Alipanahi1, Xin Gao, Emre Karakoc, Shuai Cheng Li, Frank Balbach, Guangyu Feng, Logan Donaldson, Ming Li.   

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) (15)N-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 http://monod.uwaterloo.ca/nmr/ipass.

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Year:  2011        PMID: 21328705     DOI: 10.1142/s0219720011005276

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  9 in total

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

Authors:  Piotr Klukowski; Michał Augoff; Maciej Zamorski; Adam Gonczarek; Michał J Walczak
Journal:  J Biomol NMR       Date:  2018-05-18       Impact factor: 2.835

2.  Determining protein structures from NOESY distance constraints by semidefinite programming.

Authors:  Babak Alipanahi; Nathan Krislock; Ali Ghodsi; Henry Wolkowicz; Logan Donaldson; Ming Li
Journal:  J Comput Biol       Date:  2012-10-31       Impact factor: 1.479

3.  An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming.

Authors:  Ahmed Abbas; Xianrong Guo; Bing-Yi Jing; Xin Gao
Journal:  J Biomol NMR       Date:  2014-04-19       Impact factor: 2.835

4.  NMR Assignment through Linear Programming.

Authors:  José F S Bravo-Ferreira; David Cowburn; Yuehaw Khoo; Amit Singer
Journal:  J Glob Optim       Date:  2021-03-11       Impact factor: 1.996

5.  WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering.

Authors:  Zhi Liu; Ahmed Abbas; Bing-Yi Jing; Xin Gao
Journal:  Bioinformatics       Date:  2012-02-10       Impact factor: 6.937

6.  Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra.

Authors:  Carlo Vittorio Cannistraci; Ahmed Abbas; Xin Gao
Journal:  Sci Rep       Date:  2015-01-26       Impact factor: 4.379

7.  LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone.

Authors:  Peng Chen; Jianhua Z Huang; Xin Gao
Journal:  BMC Bioinformatics       Date:  2014-12-03       Impact factor: 3.169

8.  Automatic peak selection by a Benjamini-Hochberg-based algorithm.

Authors:  Ahmed Abbas; Xin-Bing Kong; Zhi Liu; Bing-Yi Jing; Xin Gao
Journal:  PLoS One       Date:  2013-01-07       Impact factor: 3.240

Review 9.  Recent advances in computational methods for nuclear magnetic resonance data processing.

Authors:  Xin Gao
Journal:  Genomics Proteomics Bioinformatics       Date:  2013-01-11       Impact factor: 7.691

  9 in total

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