3. Fernandezらについて
• ドッキングソフトPyDOCK開発チーム
– FTDockがベース
– CAPRIでも結構強い
• FTDockが元
References
Grosdidier, S., Pons, C., Solernou, A., & Fernández-Recio, J. (2007).
Prediction and scoring of docking poses with pyDock. Proteins, 69(4), 852-8.
doi: 10.1002/prot.21796.
Cheng, T. M., Blundell, T. L., & Fernandez-Recio, J. (2007).
pyDock: electrostatics and desolvation for effective scoring of rigid-body
protein-protein docking. Proteins, 68(2), 503-15.
doi: 10.1002/prot.21419.
Pons, C., Solernou, A., Perez-Cano, L., Grosdidier, S., & Fernandez-Recio, J. (2010).
Optimization of pyDock for the new CAPRI challenges: Docking of homology-
based models, domain-domain assembly and protein-RNA binding. Proteins, 1-7.
doi: 10.1002/prot.22773. 3
8. イントロダクション
• Protein-RNA複合体の相互作用面の情報等
から相互作用の特徴の理解を目指した研究
– 水素結合の全原子統計的ポテンシャル
[3] Chen, Y., Kortemme, T., Robertson, T., Baker, D., & Varani, G.
(2004). A new hydrogen-bonding potential for the design of
protein-RNA interactions predicts specific contacts and
discriminates decoys. Nucleic acids research, 32(17), 5147-62.
doi: 10.1093/nar/gkh785.
• Rosetta(リガンドドッキングシステム)を使って
decoy生成,near nativeとそうでないのを比較して
チューニング
8
9. イントロダクション
• 統計的ポテンシャルに関する研究(一部)
[12] Lejeune, D., Delsaux, N., Charloteaux, B., Thomas, A., &
Brasseur, R. (2005). Protein-nucleic acid recognition: statistical
analysis of atomic interactions and influence of DNA structure.
Proteins, 61(2), 258-71. doi: 10.1002/prot.20607.
[9] Ellis, J. J., Broom, M., & Jones, S. (2007). Protein – RNA
Interactions : Structural Analysis and Functional Classes.
Bioinformatics, 911(December 2006), 903-911.
[13] Jeong, E., Kim, H., Lee, S., & Han, K. (2003). Discovering the
interaction propensities of amino acids and nucleotides from protein-
RNA complexes. Molecules and cells, 16(2), 161-7.
9
10. イントロダクション
• 類似の論文
[6] Pérez-Cano, L., & Fernández-Recio, J. (2010). Optimal protein-RNA
area, OPRA: a propensity-based method to identify RNA-binding sites
on proteins. Proteins, 78(1), 25-35. doi: 10.1002/prot.22527.
10
20. Protein-RNA rigid-body docking and scoring
10,000個のFTDock生成decoy中にNNSがあったもの(12複合体中7複合体)
・fnat is the fraction of RNA-protein contacts that is also found in the
native (target) structure
・fnon-nat is the fraction of RNA-protein contacts that is found, but that is
not present in the native (target) structure
・FTDock&Propensity はスコアの和(重み付けなし)
20
24. Example of successful prediction
PDB id : 2QUX
unbound protein vs. bound RNA
RMSD = 8.7Å
(Propensityで1位が当たったやつ)
シアン:予測
マゼンタ:X-ray
タンパク質表面での位置が
結構近いから良いんじゃね
24
27. 他のpropensity score
[16]Treger, M., & Westhof, E. (2001). Statistical analysis of atomic
contacts at RNA-protein interfaces. Journal of molecular recognition :
JMR, 14(4), 199-214. doi: 10.1002/jmr.534.
[9] Ellis, J. J., Broom, M., & Jones, S. (2007). Protein-RNA
interactions: structural analysis and functional classes. Proteins,
66(4), 903-11. John Wiley & Sons. doi: 10.1002/prot.21211. 27
28. 他のpropensity score
[15]Jones, S., Daley, D. T., Luscombe,
N. M., Berman, H. M., & Thornton, J.
M. (2001). Protein-RNA interactions:
a structural analysis. Nucleic acids
research, 29(4), 943-54.
28
29. 他のpropensity score
[13]Jeong, E., Kim, H., Lee, S., & Han, K. (2003). Discovering the
interaction propensities of amino acids and nucleotides from protein-
RNA complexes. Molecules and cells, 16(2), 161-7. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/14651256.
29