Literature DB >> 19127591

Comparisons of experimental and computed protein anisotropic temperature factors.

Lei Yang1, Guang Song, Robert L Jernigan.   

Abstract

Because of its appealing simplicity, the anisotropic network model (ANM) has been widely accepted and applied to study many molecular motion problems: such as ribosome motions, the molecular mechanisms of GroEL-GroES function, allosteric changes in hemoglobin, motor-protein motions, and conformational changes in general. However, the validity of the ANM has not been closely examined. In this work, we use ANM to predict the anisotropic temperature factors of proteins obtained from X-ray and NMR data. The rich, directional anisotropic temperature factor data available for hundreds of proteins in the protein data bank are used as validation data to closely test the ANM model. The significance of this work is that it presents a timely, important evaluation of the model, shows the extent of its accuracy in reproducing experimental anisotropic temperature factors, and suggests ways to improve the model. An improved model will help us better understand the internal dynamics of proteins, which in turn can greatly expand the usefulness of the models, which has already been demonstrated in many applications.

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Year:  2009        PMID: 19127591      PMCID: PMC2775930          DOI: 10.1002/prot.22328

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  29 in total

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  10 in total

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2.  Molecular dynamics simulation of triclinic lysozyme in a crystal lattice.

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Journal:  Biophys Rev       Date:  2017-11-04

4.  Anharmonic normal mode analysis of elastic network model improves the modeling of atomic fluctuations in protein crystal structures.

Authors:  Wenjun Zheng
Journal:  Biophys J       Date:  2010-06-16       Impact factor: 4.033

5.  Dynamical persistence of active sites identified in maltose-binding protein.

Authors:  Dragan Nikolić; Violeta Kovačev-Nikolić
Journal:  J Mol Model       Date:  2017-04-27       Impact factor: 1.810

6.  Computational and experimental characterization of RNA cubic nanoscaffolds.

Authors:  Kirill A Afonin; Wojciech Kasprzak; Eckart Bindewald; Praneet S Puppala; Alex R Diehl; Kenneth T Hall; Tae Jin Kim; Michael T Zimmermann; Robert L Jernigan; Luc Jaeger; Bruce A Shapiro
Journal:  Methods       Date:  2013-11-01       Impact factor: 3.608

7.  Aligning experimental and theoretical anisotropic B-factors: water models, normal-mode analysis methods, and metrics.

Authors:  Lei Zhou; Qinglian Liu
Journal:  J Phys Chem B       Date:  2014-04-08       Impact factor: 2.991

8.  Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions.

Authors:  Romain Amyot; Yuichi Togashi; Holger Flechsig
Journal:  Biomolecules       Date:  2019-09-30

9.  Molecular Dynamics Assisted Mechanistic Study of Isoniazid-Resistance against Mycobacterium tuberculosis InhA.

Authors:  Vivek Kumar; M Elizabeth Sobhia
Journal:  PLoS One       Date:  2015-12-14       Impact factor: 3.240

10.  Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method.

Authors:  Kannan Sankar; Kam Hon Hoi; Yizhou Yin; Prasanna Ramachandran; Nisana Andersen; Amy Hilderbrand; Paul McDonald; Christoph Spiess; Qing Zhang
Journal:  MAbs       Date:  2018-09-25       Impact factor: 5.857

  10 in total

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