Albany 2013: Book of Abstracts

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Conversation 18
June 11-15 2013
©Adenine Press (2012)

Going Beyond ‘Lamarckism’ in Protein Tertiary Structure Prediction

Several novel techniques are employed for protein tertiary structure prediction, but the more successful ones are those that rely either solely or partly on template/homology based modeling of full or sub-structures. However, a critical look at the yearly growth of SCOP defined unique folds (Murzin et al., 1994) in the protein data bank shows a saturation at ~ 1400 beyond 2008 (Berman et al., 2000). This saturation may show completeness of protein fold space – an unlikely possibility. Alternatively, it either implies the need for development of higly sensitive newer experimental methods to unveil the unexplored regions of protein fold space, or, beckons the development of more accurate, time efficient, non-template based computational strategies for unravelling the uniqueness and diversity of protein structures. The latter is strongly supported by the following observations: (a) the avergae Cα RMSD (root mean square distance) from the native from the best server in each CASP (the biennial experiments for critical assessment of structure prediction) (Kryshtafovych, Fidelis & Moult, 2011) has been hovering around 7 Å since CASP7 and, (b) based on CASP10 results, the best servers can predict the tertiary structure of a soluble protein to within 5 Å RMSD from the native in 52% of the cases. It is quite apparent that efforts in tertiary structure prediction have saturated to a level where there is an immediate need for methodological innovations – beyond templates of folds to avoid falling into a trap of Lamarckism in protein folding. Here we discuss our recent efforts, based on some newer sequence alignment protocols (Jayaram, 2008), quantum mechanical corrections to the molecular mechanics generated structures and structure/fold invariant metrics (Mittal et al., 2010; Mittal & Jayaram, 2011; Jayaram et al., 2013) as incoporated in the continually evolving freely accessible Bhageerath-H webserver for protein tertiary structure prediction (http://www.scfbio-iitd.res.in/bhageerath/bhageerath_h.jsp) (Jayaram et al., 2012). We show that it is possible to push the accuracies in protein structure prediction beyond 60% even for tough targets.

This research has been facilitated by Programme Support to SCFBio from DBT, Govt. of India.


    Berman H. M. et al. (2000). The Protein Data Bank. Nucl. Acids Res. 28, 235-42. http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=fold-scop

    Jayaram B. (2008). Decoding the design principles of amino acids and the chemical logic of protein sequences. Nature Precedings. http://precedings.nature.com/documents/2135/version/1

    Jayaram, B., Mittal, A., Mishra, A., Acharya, C., Khandelwal, G. (2013). Universalities in Protein Tertiary Structures: Some New Concepts, in Biomolecular Forms and Functions, 210-219. World Scientific Publishing Co. Pte. Ltd., Singapore, Eds: Manju Bansal & N. Srinivasan.

    Jayaram, B., Dhingra, P., Lakhani, B., Shekhar, S. (2012). Bhageerath - Targeting the Near Impossible: Pushing the Frontiers of Atomic Models for Protein Tertiary Structure Prediction. J. Chem. Sci. 124, 83-91.

    Mittal, A., Jayaram, B., Shenoy, S. R., and Bawa, T. S. (2010). A Stoichiometry driven universal spatial organization of backbones of folded proteins: Are there Chargaff's rules for protein folding? J. Biomol. Struc. Dyn. 28, 133-142.

    Mittal, A., Jayaram, B. (2011). Backbones of folded proteins reveal novel invariant amino-acid neighborhoods. J. Biomol. Struc. Dyn. 28, 443-454.

    Mittal, A., Jayaram, B. (2011). The newest view on protein folding: stoichiometric and spatial unity in structural and functional diversity. J. Biomol. Struc. Dyn. 28, 669-674.

    Moult, J., Fidelis, K., Kryshtafovych, A., Tramontano, A. (2011). Critical assessment of methods of protein structure prediction (CASP) – round IX. Proteins, Structure, Function & Bioinformatics, 79, Suppl 10, 1-5.

    Murzin A. G. et al. (1994). SCOP: Structural Classification of Proteins. http://scop.mrc-lmb.cam.ac.uk/scop/

Priyanka Dhingra1, 2
Avinash Mishra3
Rahul Kaushik3
M. Hassan2
Prashant Rana2
Aditya Mittal3
B. Jayaram1, 2, 3

1Department of Chemistry
2Supercomputing Facility for Bioinformatics
Computational Biology
3Kusuma School of Biological Sciences
Indian Institute of Technology
Hauz Khas, New Delhi-110016, India

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