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Revision as of 13:46, 10 September 2012

Contents 1 Homologie Modeling 1.1 Workflow 1.2 Application 1.3 Results 1.3.1 PnB-Esterase 1.3.2 AroY= 1.3.3 TphA1 1.3.4 TphA2 1.3.5 TphA3 1.3.6 TphA2 1.4 References

Homologie Modeling

While our proteins are functionally described in literature and during the IGEM competition, no structures are available in the protein data bank. For further work and visualizations protein structures are indispensible. We used Yasara Structure [1]⁠ to calculate 3-dimensional structures of our proteins we used within the IGEM.

Workflow

Description how our Yasara scripts calculates homology model[7]:

1. Sequence is PSI-BLASTed against Uniprot [2]⁠
2. Calculation of a a position-specific scoring matrix (PSSM) from related sequences
3. Using the PSSM to search the PDB for potential modeling templates
4. The Templates are ranked based on the alignment score and the structural quality[3]⁠
5. Deriving additional information’s for template and target (prediction of secondary structure, structure-based alignment correction by using SSALN scoring matrices [4]⁠.
6. A graph of the side-chain rotamer network is built, dead-end elimination is used to find an initial rotamer solution in the context of a simple repulsive energy function [5]⁠
7. The loop-network is optimized using a high amount of different orientations
8. Side-chain rotamers are fine-tuned considering electrostatic and knowledge-based packing interactions as well as solvation effects.
9. An unrestrained high-resolution refinement with explicit solvent molecules is run, using the latest knowledge-based force fields[6]⁠.

Application

All these steps are performed to every template used for the modeling approach. For our project we set the maximum amount of templates to 20. Every derived structure is evaluated using an average per-residue quality Z-scores. At least a hybrid model is built containing the best regions of all predictions. This procedure make prediction’s accurate and thus more realistic.

Results

PnB-Esterase

AroY=

TphA1

TphA2

TphA3

TphA2

References

[1] E. Krieger, G. Koraimann, and G. Vriend, “Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field.,” Proteins, vol. 47, no. 3, pp. 393–402, 2002.

[2] S. F. Altschul, T. L. Madden, A. A. Schäffer, J. Zhang, Z. Zhang, W. Miller, and D. J. Lipman, “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.,” Nucleic Acids Res, vol. 25, no. 17, pp. 3389–3402, Sep. 1997.

[3] R. W. Hooft, G. Vriend, C. Sander, and E. E. Abola, “Errors in protein structures.,” Nature, vol. 381, no. 6580. Nature Publishing Group, p. 272, 1996.

[4] D. T. Jones, “Protein secondary structure prediction based on position-specific scoring matrices,” Journal of Molecular Biology, vol. 292, no. 2, pp. 195–202, 1999.

[5] A. A. Canutescu, A. A. Shelenkov, and R. L. Dunbrack, “A graph-theory algorithm for rapid protein side-chain prediction.,” Protein Science, vol. 12, no. 9, pp. 2001–2014, 2003.

[6] E. Krieger, K. Joo, J. Lee, J. Lee, S. Raman, J. Thompson, M. Tyka, D. Baker, and K. Karplus, “Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8.,” Proteins, vol. 77 Suppl 9, no. June, pp. 114–122, 2009.

[7] http://www.yasara.org/homologymodeling.htm