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MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

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Abstract

The prediction and analysis of the three- dimensional (3D) structure of proteins is a key research problem in Structural Bioinformatics. The 1990’s Genome Projects resulted in a large increase in the number of available protein sequences. However, the number of identified 3D protein structures have not followed the same growth trend. Currently, the number of available protein sequences greatly exceeds the number of known 3D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. The most significant progress in the last Critical Assessment of protein Structure Prediction was achieved by methods that use database information. Nevertheless, a major challenge remains in the development of better strategies for template identification and representation. This article describes a computational strategy to acquire and represent structural information of experimentally determined 3D protein structures. A clustering strategy was combined with artificial neural networks in order to extract structural information from experimental protein structure templates. In the proposed strategy, the main efforts focus on the acquisition of useful and accurate structural information from 3D protein templates stored in the Protein Data Bank (PDB). The proposed method was tested in twenty protein sequences whose sizes vary from 14 to 70 amino acid residues. Our results show that the proposed method is a good way to extract and represent valuable information obtained from the PDB and also significantly reduce the 3D protein conformational search space.

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Notes

  1. http://www.rcsb.org.

  2. predictioncenter.org.

  3. http://www.pdb.org.

  4. Hubbard, S.J. and Thornton, J.M. ’NACCESS’, computer program, 1993, Department of Biochemistry and Molecular Biology, University College London.

  5. Bloomsbury Center for Bioinformatics. http://www.bioinf.org.uk/software/swreg.html.

  6. Protein templates: in this work we define a structural template as a subsequence of amino acid residues found in experimentally determined 3D protein structures. We look for all proteins in the PDB that have a subsequence of amino acid residue identical to a target subsequence or fragment of amino acid residues.

  7. Template fragments are short sub-sequences of amino acid residues of proteins with known 3D structure.

  8. BLAST blast.ncbi.nlm.nih.gov.

  9. http://www.pymol.org.

  10. SCOP is a classification of protein structural domains based on similarities of their structures. http://scop.mrc-lmb.cam.ac.uk/scop.

  11. 1AB1 presents 46 amino acid residues. However, the fragmentation scheme adopted in our method implies that the first and the last two amino acid residues are lost. For these amino acid residues the torsion angles are fixed on 180.0\(^\circ \).

  12. http://www.bioinf.org.uk/software/profit.

  13. DOE Genomic Science. http://genomics.energy.gov.

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Acknowledgments

The authors thank MCT/CNPq, CAPES and FAPERGS (Brazil) for financial support.

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  1. Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

    Márcio Dorn, Luciana S. Buriol & Luis C. Lamb

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  1. Márcio Dorn
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  2. Luciana S. Buriol
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  3. Luis C. Lamb
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Correspondence to Márcio Dorn.

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Communicated by V. Piuri.

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Dorn, M., Buriol, L.S. & Lamb, L.C. MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates. Soft Comput 18, 773–795 (2014). https://doi.org/10.1007/s00500-013-1087-6

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