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The Use of Experimental Structures to Model Protein Dynamics

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Molecular Modeling of Proteins

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1215))

Abstract

The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high—for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods—Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them.

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Acknowledgments

We gratefully acknowledge the support provided by NIH Grant R01GM072014 and NSF Grant MCB-1021785.

We used several Matlab functions from MAVEN by Zimmerman et al. [12] as mentioned in Subheading 4. MAVEN is also useful to compute PCs, PC-plot, ANM modes, overlap between PCs and ANM modes.

Author information

Authors and Affiliations

  1. National Cancer Institute, National Institute of Health, 37 Convent Drive Bldg 37, Bethesda, MD, 20892, USA

    Ataur R. Katebi, Kannan Sankar, Kejue Jia & Robert L. Jernigan

  2. Interdepartmental Program for Bioinformatics and Computational Biology, L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, 112 Office & Lab Building, Ames, IA, 50011-3020, USA

    Ataur R. Katebi, Kannan Sankar, Kejue Jia & Robert L. Jernigan

Authors
  1. Ataur R. Katebi
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  2. Kannan Sankar
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  3. Kejue Jia
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  4. Robert L. Jernigan
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Corresponding author

Correspondence to Robert L. Jernigan .

Editor information

Editors and Affiliations

  1. University of Hertfordshire, Hatfield, Hertfordshire, United Kingdom

    Andreas Kukol

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Katebi, A.R., Sankar, K., Jia, K., Jernigan, R.L. (2015). The Use of Experimental Structures to Model Protein Dynamics. In: Kukol, A. (eds) Molecular Modeling of Proteins. Methods in Molecular Biology, vol 1215. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1465-4_10

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  • DOI: https://doi.org/10.1007/978-1-4939-1465-4_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1464-7

  • Online ISBN: 978-1-4939-1465-4

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