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Bridging between normal mode analysis and elastic network models

Authors

  • Hyuntae Na,

    1. Department of Computer Science, Iowa State University, Ames, Iowa
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  • Guang Song

    Corresponding author
    1. Department of Computer Science, Iowa State University, Ames, Iowa
    2. Program of Bioinformatics and Computational Biology, Iowa State University, Ames, Iowa
    3. L. H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa
    • Correspondence to: Guang Song, Department of Computer Science, Iowa State University, Ames, IA 50011, USA. E-mail: gsong@iastate.edu

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ABSTRACT

Normal mode analysis (NMA) has been a powerful tool for studying protein dynamics. Elastic network models (ENM), through their simplicity, have made normal mode computations accessible to a much broader research community and for many more biomolecular systems. The drawback of ENMs, however, is that they are less accurate than NMA. In this work, through steps of simplification that starts with NMA and ends with ENMs we build a tight connection between NMA and ENMs. In the process of bridging between the two, we have also discovered several high-quality simplified models. Our best simplified model has a mean correlation with the original NMA that is as high as 0.88. In addition, the model is force-field independent and does not require energy minimization, and thus can be applied directly to experimental structures. Another benefit of drawing the connection is a clearer understanding why ENMs work well and how it can be further improved. We discovered that inline image can be greatly enhanced by including an additional torsional term and a geometry term. Proteins 2014; 82:2157–2168. © 2014 Wiley Periodicals, Inc.

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