我們探討兩種蛋白激酶，CDK2和c-MET的分子嵌合計算。此兩種蛋白激酶在藥物發展上是許多實驗學家所感興趣的。
在CDK2蛋白激酶的部分，我們研究在虛擬篩選時，是否擺動胺基酸支鏈加上活化中心氫鍵限制可以提高enrichment factor(EF)。我們使用Directory of Useful Decoys (DUD)資料庫作為測試基準資料庫。平均來說，將活化中心氫鍵限制可以使EF提高2倍；若單獨擺動胺基酸支鏈僅能使EF微幅增加。兩個效應同時在嵌合計算開啟所得到的EFs和個別效應所得到的EFs並沒有明顯的增加。有趣的是，某些胺基酸支鏈相較其他而言會對EF有很大的影響。這些計算結果對於虛擬篩選是有幫助的，在大型的分子數據資料庫以獲得更好的命中。
除了CDK2激酶外，我們也使用相同的分子嵌合計算程序對c-MET激酶做研究。c-MET為一種蛋白質激酶，在多細胞生物中扮演重要的角色，如調控細胞的增殖、移動、侵襲、轉移與血管新生等，且對於胚胎發育和傷口癒合是不可或缺的；然而，c-Met的過量表現或突變，也是造成人類癌症的原因之一。
首先，我們從蛋白質資料庫可得的結晶構型，挑選10個c-MET抑制劑複合物做分子嵌合計算，研究其再現性。第二，我們再選擇5個c-MET抑制劑複合物做交叉分子嵌合，藉此研究當c-MET在不同的結晶構型時，如何再現小分子的構型。第三，我們選擇前3名的c-MET結晶構型進行虛擬篩選，計算10個已知具有抑制力的小分子，在嵌合計算中可篩選出多少個活性分子。同時也設定擺動胺基酸支鏈及活性中心氫鍵限制。我們發現，當設定3個可動胺基酸支鏈加上可形成氫鍵原子做限制時，和其他組合相較之下結果較佳。最後，我們藉由以上較佳的條件選擇和設定，高速虛擬篩選40萬個化合物，並分析數個具有較佳親和力的化合物與c-MET之間的作用力和結合圖形，而這些計算結果將有助於實驗學家設計與搜尋c-MET抑制劑。

關鍵字:激酶、分子嵌合計算、虛擬篩選

In the present study, we carried out docking computation of compounds against two kinases, CDK2 and c-MET, using GOLD program. Both are of pharmaceutical interest.
In the part of CDK2 kinase, we investigated if allowing side chains to move and applying hydrogen bond constrains can enhance enrichment factor(EF) in virtual screening. To this end, compounds from DUD database were used for benchmark. The computations gave that applying hydrogen bond constraints enhance EF, on average, by about a factor of 2, and allowing side chains to move only enhance EF slightly. With both effects turned on in docking computation, the calculated EFs do not enhance significantly compared with EFs obtained from individual effect. Interestingly, some side chains have more significant enrichment effects than others. These computed results should be useful for virtual screening over large compounds databases against CDK2 kinase in order to obtain better hits.
In addition to CDK2, we also investigated c-MET kinase using similar docking procedure. c-MET (MET) is a kinase protein that plays an important role in multi-cells, including regulation of proliferation, motility, invasion, migration, and angiogenesis. It is also essential for embryonic development and tissue damage repair. However, overexpression of c-MET or mutations occurs in many human cancers.
First, we carried out docking computations for 10 c-met inhibitor complexes with crystal structures available in the protein data bank in orderto examine their reproducibility.Second, we choose 5 complexes to do crossing docking and investigate how ligand conformations can be regained when protein structures from different complexes were used. Third, we selected top3 protein structures from these 5 complexes for subsequent virtual screening. For benchmark, we constructed a group of 1000 compounds consisting 10 active and 990 decoy compounds, and see if 10 active compounds can be screened out in docking computation.Effects of side chain flexibility and hinge hydrogen bond constraints were examined as well. We found that the results obtained byallowing 3 residues to move and constraining2hinge hydrogen bonds simultaneouslyare better than results obtained by other combinations. Finally, virtual screening for 400,000 compounds of c-MET was carried out. The interactions between top-ranked compounds and c-met were analyzed and discussed. These computed results and analysis should be of aid in design and discovery of c-met inhibitors.

key words:kinase、docking computations、Virtual Screening

1. Johnson SR (2008) J Chem Inf Model 48:25
2. Ajay N. Jain • Ann E. Cleves.Does your model weigh the same as a Duck? J Comput Aided Mol Des (2012) 26:57–67
3. Charifson, P.S. Practical Application of Computer-Aided Drug Design (Marcel Dekker, Inc., 1997).
4. Taylor, R.D., Jewsbury, P.J. & Essex, J.W. (2002).
5. Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discovery Today11, 580-594 (2006).
6. Galina G. Dubinina, Oleksandr O. Chupryna, Maxim O. Platonov, Petro O. Borisko, Galina V.Ostrovska, Andriy O. Tolmachov and Alexander A. Shtil. In Silico Design of Protein Kinase Inhibitors: Successes and Failures.Anti-Cancer Agents in Medicinal Chemistry, 2007, 7, 171-188
7. Verdonk, M.L. et al. Virtual Screening Using Protein−Ligand Docking:  Avoiding Artificial Enrichment. Journal of Chemical Information and Computer Sciences44, 793-806 (2004)
8. L. M. Schang. Advances on Cyclin-dependent Kinases (CDKs) as Novel Targets forAntiviral Drugs. Current Drug Targets – Infectious Disorders 2005, 5, 29-37
9. http://www.dls.ym.edu.tw/lesson/ccc.htm

10. Shu Liu, Joshua K. Bolger, Lindsay O. Kirkland, Padmavathy N. Premnath, and
Campbell McInnes. Structural and Functional Analysis of CyclinD1 Reveals p27 and Substrate InhibitorBinding Requirements. VOL.5 NO.12 • ACS CHEMICAL BIOLOG
11. Stephane Betzi,Riazul Alam,Mathew Martin,Donna J. Lubbers,Huijong Han,
Sudhakar R. Jakkaraj,‡Gunda I. Georg,‡and Ernst Schonbrunn.Discovery of a
Potential Allosteric Ligand Binding Site in CDK2. ACS Chem. Biol. 2011, 6, 492–501
12. Megan L. Peach,Nelly Tan,Sarah J. Choyke, Alessio Giubellino, GaganiAthauda, Terrence R. Burke, Jr.Marc C. Nicklaus, and Donald P. Bottaro. Directed Discovery of Agents Targeting the Met Tyrosine Kinase Domain by Virtual Screening. J. Med. Chem. 2009, 52, 943–951
13. Xiangdong Liu, Robert C. Newton and Peggy A. Scherle. Developing c-MET pathway inhibitorsfor cancer therapy: progress andchallenges.Trends in Molecular Medicine Vol.16 No.1
14. Christine M. Stellrecht a,Varsha Gandhi. MET receptor tyrosine kinase as a therapeutic anticancer target. Cancer Letters 280 (2009) 1–14
15. Nikolaus Schiering, Stefan Knapp, Marina Marconi, Maria M. Flocco, Jean Cui§, Rita Perego, Luisa Rusconi,and Cinzia Cristiani. Crystal structure of the tyrosine kinase domain of thehepatocyte growth factor receptor c-Met and itscomplex with the microbial alkaloid K-252a. PNASOctober 28, 2003vol. 100 no. 22
16. Morris, G.M. et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19, 1639-1662 (1998).
17. Makino, S. & Kuntz, I.D. Automated flexible ligand docking method and its application for database search. Journal of Computational Chemistry 18, 1812-1825 (1997)
18. Rarey, M., Kramer, B., Lengauer, T. & Klebe, G. A Fast Flexible Docking Method using an Incremental Construction Algorithm. Journal of Molecular Biology 261, 470-489 (1996)
19. Jones, G., Willett, P., Glen, R.C., Leach, A.R. & Taylor, R. Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology 267, 727-748 (1997)
20. Friesner, R.A. et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. Journal of Medicinal Chemistry 47, 1739-1749 (2004).
21. Niu Huang, Brian K. Shoichet, and John J. Irwin. Benchmarking Sets for Molecular Docking. J. Med. Chem. 2006, 49, 6789-6801
22. Koska, J.r. et al. Fully Automated Molecular Mechanics Based Induced Fit Protein-Ligand Docking Method. Journal of Chemical Information and Modeling 48, 1965-1973 (2008)
23. Davis, I.W. & Baker, D. RosettaLigand Docking with Full Ligand and Receptor Flexibility. Journal of Molecular Biology 385, 381-392 (2009).
24. Cavasotto, C.N. & Abagyan, R.A. Protein Flexibility in Ligand Docking and Virtual Screening to Protein Kinases. Journal of Molecular Biology 337, 209-225 (2004).
25. Mishra, N. et al. Structure based virtual screening of GSK-3[beta]: Importance of protein flexibility and induced fit. Bioorganic & Medicinal Chemistry Letters 19, 5582-5585 (2009).
26. http://zinc.docking.org/

27. Lovell, S.C., Word, J.M., Richardson, J.S. & Richardson, D.C. The penultimate rotamer library. Proteins-Structure Function and Genetics 40, 389-408 (2000).
28. Schlessinger, A. & Rost, B. Protein flexibility and rigidity predicted from sequence. Proteins-Structure Function and Bioinformatics 61, 115-126 (2005).
29. Emanuele Perola. Minimizing False Positives in Kinase Virtual Screens. Structure, Function, and Bioinformatics 64:422–435 (2006)
30. Cole, J.C., Murray, C.W., Nissink, J.W.M., Taylor, R.D. & Taylor, R. Comparing protein-ligand docking programs is difficult. Proteins-Structure Function and Bioinformatics60, 325-332 (2005)
31. Marcel L. Verdonk,Jason C. Cole,Michael J. Hartshorn,Christopher W. Murray,and Richard D. Taylor.Improved Protein–Ligand Docking Using GOLD. Structure, Function, and Genetics 52:609–623 (2003)
32. Eldridge, M.D., Murray, C.W., Auton, T.R., Paolini, G.V. & Mee, R.P. Empirical scoring functions .1. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of Computer-Aided Molecular Design11, 425-445 (1997).
33. Baxter, C.A., Murray, C.W., Clark, D.E., Westhead, D.R. & Eldridge, M.D. Flexible docking using Tabu search and an empirical estimate of binding affinity. Proteins-Structure Function and Genetics33, 367-382 (1998).
34. Tiejun Cheng, Xun Li, Yan Li, Zhihai Liu, and Renxiao Wang. Comparative Assessment of Scoring Functions on a Diverse Test Set. J. Chem. Inf. Model. 2009, 49, 1079–1093
35. Mooij, W.T.M. & Verdonk, M.L. General and targeted statistical potentials for protein-ligand interactions. Proteins-Structure Function and Bioinformatics61, 272-287 (2005).
36. Verdonk, M.L. Modeling water molecules in protein-ligand docking using GOLD. Journal of Medicinal Chemistry 48, 6504 (2005)
37. Jones, G., Willett, P. & Glen, R.C. MOLECULAR RECOGNITION OF RECEPTOR-SITES USING A GENETIC ALGORITHM WITH A DESCRIPTION OF DESOLVATION. Journal of Molecular Biology245, 43-53 (1995)
38. http://www.iem.bham.ac.uk/environmental/sharifi.htm. genetic algorithmprocedures
39. Bhusan K Kuntal, Polamarasetty Aparoy and Pallu Reddanna. EasyModeller: A graphical interface to MODELLER. BMC Research Notes 2010,3:226
40. Megan L. Peach, Nelly Tan,Sarah J. Choyke,Alessio Giubellino, Gagani Athauda,Terrence R. Burke, Jr.Marc C. Nicklaus, and Donald P. Bottaro. Directed Discovery of Agents Targeting the Met Tyrosine Kinase Domain by Virtual Screening. J. Med. Chem. 2009, 52, 943–951
41. Lipinski, C.A., Lombardo, F., Dominy, B.W. & Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews23, 3-25 (1997).
42. Jason B. Cross, David C. Thompson,Brajesh K. Rai, J. Christian Baber, Kristi Yi Fan,Yongbo Hu,| and Christine Humblet. Comparison of Several Molecular Docking Programs: Pose Prediction and VirtualScreening Accuracy. J. Chem. Inf. Model. 2009, 49, 1455–1474
43. Izhar Wallachand Ryan Lilien. Virtual Decoy Sets for Molecular Docking Benchmarks. J. Chem. Inf. Model. 2011, 51, 196–202
44. Daria B. Kokh and Wolfgang Wenzel. Flexible Side Chain Models Improve Enrichment Rates in In Silico Screening. J. Med. Chem. 2008, 51, 5919–5931
45. Nibha Mishra a, Arijit Basu a,Venkatesan Jayaprakash a, Ashoke Sharon b, Mahua Basu c, Kiran K. Patnaik. Structure based virtual screening of GSK-3b: Importance of proteinflexibility and induced fit. Bioorganic & Medicinal Chemistry Letters 19 (2009) 5582–55854
46. The Penultimate Rotamer Library, S. C. Lovell, J. M. Word, J. S. Richardson & D.
C.Richardson, Proteins, 40, 389-408, 2000