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Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach

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Abstract

We present different means of classifying protein structure. One is made rigorous by mathematical knot invariants that coincide reasonably well with ordinary graphical fold classification and another classification is by packing analysis. Furthermore when constructing our mathematical fold classifications, we utilize standard neural network methods for predicting protein fold classes from amino acid sequences. We also make an analysis of the redundancy of the structural classifications in relation to function and ligand binding. Finally we advocate the use of combining the measurement of the VA, VCD, Raman, ROA, EA and ECD spectra with the primary sequence as a way to improve both the accuracy and reliability of fold class prediction schemes.

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Authors and Affiliations

  1. Sapient Discovery, 10929 Technology Place, Suite B, San Diego, CA, 92127, USA

    K. Ramnarayan

  2. Department of Physics, Quantum Protein Centre (QuP), Technical University of Denmark, Kngs. Lyngby, 2800, Denmark

    H. G. Bohr

  3. Nanochemistry Research Institute, Department of Applied Chemistry, Curtin University of Technology, GPO Box U1987, Perth, WA, 6845, Australia

    K. J. Jalkanen

Authors
  1. K. Ramnarayan
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  2. H. G. Bohr
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  3. K. J. Jalkanen
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Correspondence to K. J. Jalkanen.

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Ramnarayan, K., Bohr, H.G. & Jalkanen, K.J. Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach. Theor Chem Account 119, 265–274 (2008). https://doi.org/10.1007/s00214-007-0285-7

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