1. Structure-based Drug Design
陸志豪
分子系統生物醫學研究所
教材來源: 國立交通大學 楊進木博士

2. 陸志豪助理教授 chlu@mail.cmu.edu.tw 學歷 國立交通大學生物資訊所 博士 專長
結構生物資訊、計算生物學、 演化式計算與機器學習
研究領域
蛋白質區域結構模組與功能預測
蛋白質結構與動力學的相關研究
蛋白質與分子的交互作用相關研究

3. Outline Current state of drug discovery Structure-based drug discovery
Molecular docking
Method of iGEMDOCK
Practice of virtual screening
Prepare protein binding site
Prepare compounds library
Post-screening analysis

4. World‘s Top-10 Selling Drugs 2010
Rank
Drug
Companies
Indications
Sales $ billion 1
Lipitor
Pfizer,
Astellas
降血脂
11.8 2
Plavix
Bristol Myers Squibb,
Sanofi Aventis
抗血栓
9.4 3
Remicade
J&J, Merck,
Mitsubishi Tanabe
關節炎, 脊柱炎… 8 4
Advair
Glaxo Smith Kline 氣喘
7.96 5
Enbrel
Amgen, Pfizer,
Takeda
7.4 6
Avastin
Roche
肺癌，結腸癌，腎癌…
6.8 7
Abilify
Otsuka, BMS
精神病
Rituxan
非霍奇金淋巴瘤, 白血病
6.7 9
Humira
Abbott
關節炎, 脊柱炎, 克羅恩病…
6.5 10
Diovan
Novartis
高血壓
6.1 氣喘

5. Patents: Advair US $6 billion/year: 15% income for GlaxoSmithKline 氣喘
Manufacturer: GlaxoSmithKline FDA approval: 2000 Patent expiry: Feb. 12, 2008
US $6 billion/year: 15% income for GlaxoSmithKline

6. Drug Development Life Cycle
Discovery (Lead discovery, Lead optimization, Toxicity prediction)
Preclinical Testing
(Lab and Animal Testing)
$ US Million!
Phase I
(20-80 Healthy Volunteers used to
check for safety and dosage)
Identify lead compounds
Structure-based screening
ligand-based screening
High-throughput screening
Phase II
( Patient Volunteers used to
check for efficacy and side effects)
Phase III
( Patient Volunteers
used to monitor reactions to
long-term drug use)
Lead optimization: QSAR
FDA Review
& Approval
2 to 10 Years
Post-Marketing
Testing
Years
7 – 15 Years!

7. Classification of Drug Development
Protein (receptor) Structure
Compound similarity search
High-Throughput Screening
(HTS)
Unknown
query
Similar compounds
Structure-based Drug Design
(SBDD)
SBDD or de novo design
Known
DDT 2002
Known
Unknown
Compound structure

8. Comparison of SBDD and HTS
SBDD: high hit rate
SBDD
HTS
Yellow: virtual screening (SBDD)
Blue: high-throughput screening (HTS)
Lipinski's Rule of Five
There are more than 5 H-bond donors.
The molecular weight is over 500.
The LogP is over 5.
There are more than 10 H-bond acceptors.
Curr. opin. Chem. Biol. 2002, 439

9. Drugs derived from SBDD
瑞樂沙
克流感
Drug Discovery Today, 10, 895, 2005

10. Mechanism of drug actions
To identify drugs that inhibit target proteins involved in diseases and have therapeutic effect against diseases
Drugs often have stronger binding affinities than natural compounds
Natural compound
A pathway of disease
Drug x
The aim of drug discover is to indentify drugs that inhibit target proteins involved in diseases and have therapeutic effect against diseases
This is an example of disease pathway.
If the natural compound bind the target protein, that will start a series of reactions to cause diseases
Usually, drugs have stronger binding affinities than natural compounds,
If the drug compete with the natural compound and inhibit the target protein, that will interrupt the pathway and may cure disease
Protein
Protein
Target protein

11. Structure-based Drug Design
Compound database
Protein
Virtual screening using molecular docking
iGEMDOCK
Lead optimization
SiMMap, QSAR
Post-screening analysis
iGEMDOCK, SiMMap
Bioassay

12. What is the Molecular Docking ?
-30 -2
Energy
Conformation
Algorithm
Search
Evaluate
Scoring
function
Problem: Given two biological molecules determines:
1. Whether the two molecules “interact”
2. Formulated as a force field minimization process
Goal: Retrieve molecules that can interact with query protein structure

13. Three Components of Docking
PDB
Representation of receptor
binding site (blue) and ligand (red)
ZINC, NCI, FDA...
Sampling of configuration space
of the ligand-receptor complex (Search methods) GA SA
Protein-ligand complex
-scores
Evaluation of ligand-receptor
Interactions
(Scoring methods) ?
configurations of the complex

14. Types of Scoring Functions
Physics-based
Physics-based
nonbonded interaction terms as the score, sometimes in combination with solvation terms
Empirical
multivariate regression methods to fit coefficients of physically motivated structural functions by using a training set of ligand-receptor complexes with measured binding affinity
Knowledge-based
statistical atom pair potentials derived from structural databases as the score
Consensus scoring functions approach
Empirical

15. Docking Software GEMDOCK (Yang & Chen 2004) iGEMDOCK (Yang & HSU 2011)
DOCK: (Kuntz et al. 1982)
DOCK 4.0 (Ewing & Kuntz 1997)
AutoDOCK (Goodsell & Olson 1990)
AutoDOCK 3.0 (Morris et al. 1998)
GOLD (Jones et al. 1997)
FlexX: (Rarey et al. 1996)
GLIDE: (Friesner et al. 2004)
ADAM (Mizutani et al. 1994)
CDOCKER (Wu et al. 2003)
CombiDOCK (Sun et al. 1998)
DIVALI (Clark & Ajay 1995)
DockVision (Hart & Read 1992)
FLOG (Miller et al. 1994)
Hammerhead (Welch et al. 1996)
LIBDOCK (Diller & Merz 2001)
MCDOCK (Liu & Wang 1999)
PRO_LEADS (Baxter et al. 1998)
SDOCKER (Wu et al. 2004)
QXP (McMartin & Bohacek 1997)
Validate (Head et al. 1996)
de novo design tools
LUDI (Boehm 1992),
BUILDER (Roe & Kuntz 1995)
SMOG (DeWitte et al. 1997)
CONCEPTS (Pearlman & Murcko 1996)
DLD/MCSS (Stultz & Karplus 2000)
Genstar (Rotstein & Murcko 1993)
Group-Build (Rotstein & Murcko 1993)
Grow (Moon & Howe 1991)
HOOK (Eisen et al. 1994)
Legend (Nishibata & Itai 1993)
MCDNLG (Gehlhaar et al. 1995)
SPROUT (Gillet et al. 1993)

16. 研究成果: iGEMDOCK 電腦輔助藥物設計
Easy use, high accuracy, and automatic
GEMDOCK相關論文被引用次數超過 150次
GEMDOCK在實際應用上，與國內外超過十實驗室合作，發現20個潛力藥物

17. iGEMDOCK: Fitness Function
van der Waals Energy
Electrostatic Energy
H-Bond
Dihedral Parameter
An example of van der Waals force:
Gecko climbs on the glass
Ebind = Einter + Eintra + Epenal

18. Definition of Atom Types in iGEMDOCK
both
Atom type
Heavy atom name
Donor
Primary and secondary amines, sulfur, and metal atoms
Acceptor
Oxygen and nitrogen with no bound hydrogen
Both
Structural water and hydroxyl groups
Nonpolar
Other atoms (such as carbon and phosphorus)
HIS
ASP
Formal charge
Atom name
Receptor:
0.5
N atom in His (ND1 & NE2) and Arg (NH1 & NH2)
-0.5
O atom in Asp (OD1 & OD2) and Glu (OE1 & OE2)
1.0
N atom in Lys (NZ)
2.0
metal ions (MG, MN, CA, ZN, FE, and CU)
other atoms
Ligand:
N atom in –C(NH2)2+
O atom in -COO-, -PO2-, -PO3-, -SO3-, and -SO4-
N atom in -NH3+ and -N+(CH3)3
THR
-0.5
-0.5

19. Docking Problem Optimization steps center Rotatable bonds
Fix the location of the receptor, Initialize the orientation and conformation
Adapt the orientation and conformation of ligand
Evaluate the interaction energy and select the configuration
Repeatedly execute step 2 and 3
center
Rotatable bonds
(x1, x2, x3):3-dimensional location relating to the center of receptor
(x4, x5, x6): rotational angles of ligand relating to axes
(x7, …, xn): twisting angles of rotatable bonds in the ligand

20. The Evolution Approach of iGEMDOCK
-30 -2
Energy
Conformation
Population: 2
Generation: 3

21. Power of evolution -30 1 . 1 . 1 . 1 . 1 . 1 . Mutation Mutation
Energy
Conformation 1 . 1 . 1 . 1 . 1 . 1 .
Mutation
Mutation
Crossover

22. GEMDCOK – Docking Evaluation (100 complexes)
Proteins 2004
79%
66%
69%
GEMDOCK Dataset
J.-M. Yang* and C.-C. Chen, “GEMDOCK: A generic evolutionary method for molecular docking,” Proteins, 55, , 2004

23. Old drugs  New Use (舊藥新用)
Reasons
It takes too long and costs too much to bring new drugs to market.
Nobel laureate James Black (1988)
the most fruitful basis for the discovery of a new drug is to start with an old drug.
for their discoveries of important principles for drug treatment

24. Old drugs  New Use (舊藥新用)
Examples:舊藥新用的成功案例
 治療癌症常用藥「顆粒球生長激素」
可刺激骨髓細胞游移到大腦、分化成神經細胞，取代受損或死亡的腦神經細胞，達到治療老年痴呆症的效果 (院士沈哲鯤)
就利用該藥治療老年痴呆症，向美國、台灣、中國大陸和歐盟等地提出應用專利申請
葛蘭素史克公司（GSK） Seroxat:
原本是丹麥人於1970年發現的一種用來抗抑鬱的藥
2001年(32億美元)，2004年又名列收入最高的50種處方藥
加拿大Apotex公司經過研究，發現其可以治療強迫症、後外傷性壓迫症
輝瑞的minoxidil
治療高血壓的口服藥
發現能刺激毛髮生長，成為治療人類禿頭症的局部用藥

25. Steps of virtual screening
Compound databases
Target protein:
1kim
Preparation
of docking
databases
Molecular recognition
>100,000 compounds
Prepared binding site
of the target protein
經過 evaluation 證實 GEMDOCK 在 virtual screening 上的表現之後, 我們將 GMEDOCK 應用在登革熱的 E protein 上, 希望能透過這樣的流程找到潛在的抑制劑.
Docking
program
iGEMDOCK
Post-screening analysis
Ranking

26. Thymidine kinase (TK)
TK a drug target for the therapy of herpes simplex virus type-1
The role of TK
Thymidine kinase
1kim
DNA synthesis
The reaction of TK
Thymidine kinase
ATP
ADP
Thymidine
5’-phosphate

27. Thymidine: Drug design by modifying from substrate
Blue is different
Inhibitor

28. Preparation of protein binding site from Protein Data Bank (PDB)

29. Protein data bank (PDB)
Techniques for determining protein structures
X-ray crystallography, NMR spectroscopy and electron microscopy
PDB contains information about experimentally-determined structures of biological marcomoleculeas (proteins, and DNA/RNA)
Proteins (1kim)
DNAs/RNAs (2k7e)
Biological complexes (1zrc)
X-ray
NMR EM

30. Search protein structures in PDB
PDB provides search by protein name, ligand, or structrue related keywords
Search example:
thymidine kinase (TK)
Function: DNA synthesis
Therapeutic: Anticancer and antivirus drug target

31. Example: X-ray structures of virus’ thymidine kinase with substrates/inhibitors
Protein name
Thymidine kinase
Source spices
Viruses
Experimental method
0.1
2.5
has ligands
Yes

32. Search result of “X-ray structures of virus’ thymidine kinase with substrates/inhibitors”
23 structures for these keywords
PDB ID of this structure
TK of virus
TK with ligand (substrate)
X-ray structure

33. Structure and related data (1kim)
Related data of this structure
The title of this structure
Visualization of biological assembly
The citation of this structure

34. Structure and ligand data (1kim)
Ligand in this structure

35. Domain Annotations
Structure classification ID of 1kim

36. Advanced inspection for protein structure: download structure from PDB
Save the data on your PC
Open the file on a structure viewer program (swiss PDBviewer, pymol, and etc.)

39. Download iGEMDOCK
iGEMDOCK is available at

40. Install of iGEMDOCK
Click the zip file, iGEMDOCKv2.0.zip, and then right click
Decompress to the user defined path
Step 1.
Step 2.

41. Docking/Screening: 1. Prepare binding site by iGEMDOCK (Define by the ligand in the protein structure)
Press the button binding site
Browse and select protein file (in \examples\protein\1KIM.pdb) 1 2

42. Binding site of TK

43. Preparation of compounds

44. ZINC, a free compound database

45. Subset in ZINC1

46. Subset in ZINC1

47. Subset in ZINC2

48. Subset in ZINC2

49. Subset in ZINC2

50. Search compounds

51. Query compounds

52. Results of query

53. Molecular docking using iGEMDOCK

54. Execute iGEMDOCK
After installing the iGEMDOCK, execute the igemdock.exe in the fold “\bin\”
The environment of iGEMDOCK
Docking/screening
Post-analysis

55. Steps of docking/screening in iGEMDOCK
Protein structure/model
(prepare your binding site)
Compounds library
(load your compound set)
3. & 4. Setup parameters (output)
5. Run docking/screening
6. Analyze your predicted binding poses

56. Thymidine Kinase (TK) Inhibitors
1e2k_MCT
TK02
1e2m_HPT
TK09 1kim_THM
TK是一個將核酸???? 磷酸化的enzyme,
所以可以看到這10個inhibitors, 都有共同的???性質
抑制TK即可抑制DNA的合成,
TK作用, 密定形式, diversity?
Pair distance mean and std 全名
TK03
1e2n_RCA
TK04
1e2p_CCV
TK05
1ki2_GA2

57. Docking/Screening: 2. Prepare compounds
Press the button compounds
Browse and select ligand files (in \examples\compound\ESA and TK)

58. Docking/Screening: 3. Set output path
Press the button output
Browse and select the path for saving screening data, or directly key in the wanted path

59. Docking/Screening: 4. docking parameters
Setup docking parameters
Recommend parameters settings for general cases
For screening
Population size :200
Generations :70
Number of solution :3
For more precisely docking
Population size :300
Generations :80
Number of solution :10

60. Docking/Screening: 5. Run docking/screening
Press the button start
Run the screening jobs
The status will show in the window
The docked poses can be visualized in real time

61. Docking/Screening: After screening job finished
Press the button ok
Use the post-analysis interface of iGEMDOCK
Or directly look for the screening result in the fold “/output/”
iGEMDOCK provides an analysis environment with visual tool and post-analysis tool for users
Users can view the docked poses, cluster the poses by the protein-ligand interactions
The predicted poses and scores of ligands are saved in the user defined output path

62. Post-screening analysis

63. Post-analysis: 1. Load binding site (if iGEMDOCK does not pre-loaded)
If the binding site does not pre-loaded
Press the button binding site
Select binding site from the output
Or re-define by user

64. Post-analysis: 2. Load docked poses (poses generated into “output/best_Pose” in the screening process)
Press the button poses
Select docked poses from the output

65. Post-analysis: 3. Generate interaction profile (by iGEMDOCK scoring function)
Sort the table by the wanted columns)
View the predicted pose (to check the check box and press )

66. Post-analysis: 4. cluster analysis by the protein-ligand interactions
Look for compounds in the same cluster (#3)
Cluster analysis
View the poses in the same cluster by ClusterID
Press the button display

67. Post-analysis: 4. cluster analysis : the protein-ligand interaction map
y axis: Residues
Energy
Positive
Zero
Negative
x axis: Compounds

68. Post-analysis: 4. cluster analysis : the protein-ligand interaction analysis
Columns: Interactions between each residue and compounds
Rows: Compounds

69. Post-analysis: 4. cluster analysis : the protein-ligand interaction analysis
Select residues with consensus interactions (with statistical significances)
Default: confidence interval = 95% (z=1.96)
Hbond
Essential for catalytic mechanism
Stacking
J. Bio. Chem. v276, p , 2001.

70. Post-analysis: 5. save the post-analysis data
The data generated in the post-analysis
/output/
Summary table
Summary: screening ranks and energy
Profile : interaction / compound feature table
Selected ligands data
Cluster data: cluster index and profile

71. The data generated by iGEMDOCK in the process of screening and post-analysis
The poses with the best energies
All of the generated poses
The binding site used in screening …
Profile_residue-based_interactions.txt
Interaction table of protein-ligand
Summary_table.txt
Post-analysis result

72. References of iGEMDOCK
Primary References:
J.-M. Yang and C.-C. Chen, "GEMDOCK: A generic evolutionary method for molecular docking", Proteins: Structure, Function and Bioinformatics, vol. 55, pp , 2004.
J.-M. Yang, "Development and evaluation of a generic evolutionary method for protein-ligand docking", Journal of Computational Chemistry, vol. 25, pp , 2004.
J.-M. Yang and T.-W. Shen, "A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators", Proteins: Structure, Function and Bioinformatics,vol. 59, pp , 2005.
J.-M. Yang, Y.-F. Chen, T.-W. Shen, B. S. Kristal, and D. F. Hsu, "Consensus Scoring Criteria for Improving Enrichment in Virtual Screening ", Journal of Chemical Information and Modeling 2005.
K.-C. Hsu, Y.-F. Chen, S.-R. Lin and J.-M. Yang, "iGEMDOCK: A Graphical Environment of enhancing GEMDOCK using pharmacological interactions and postscreening analysis," BMC Bioinformatics, 12(Suppl 1):S33, 2011
Selected Applications:
J.-M. Yang, Y.-F. Chen, Y.-Y. Tu, K.-R. Yen, and Y.-L. Yang*, “Combinatorial computation approaches identifying tetracycline derivates as flaviviruses inhibitors”, PLoS ONE, pp. e e428.12, 2007.
E.-S. Lin, J.-M. Yang, and Y.-S. Yang, "Modeling the binding and inhibition mechanism of nucleotide and sulfotransferase using molecular docking", Journal of the Chinese Chemical Society, vol. 50, pp , (SCI)
J.-M. Yang and T.-W. Shen, "A Pharmacophore-Based Evolutionary Approach for Screening Estrogen Receptor Antagonists",Congress of Evolutionary Computation (CEC 2004),pp , 2004.

73. Thanks for your attention
聯絡資訊
藥物設計與系統生物實驗室 (BioXGEM Lab.), 交大生物資訊所
PI: Jinn-Moon Yang (楊進木)
Address: 75 PO-Ai Street, Hsinchu, Taiwan, 30050, ROC
Office & Lab : 308 and 304 (Lab) in Experiment Building Tel: ext.56946