bidd inventions for drug design, herbal medicine and...
TRANSCRIPT
Essential Bioinformatics and Biocomputing (LSM2104: Section I)
Biological Databases and Bioinformatics Software Prof. Chen Yu Zong
Tel: 6874-6877 Email: [email protected] http://xin.cz3.nus.edu.sg
Room 07-24, level 7, SOC1, NUS January 2003BIDDBIDD Inventions For
Drug Design, Herbal Medicine Inventions For Drug Design, Herbal
Medicine and Bioinformatics Toolsand Bioinformatics Tools
Chen Yu ZongChen Yu Zong
BiBioinformatics and oinformatics and DDrug rug DDesign Groupesign Group Department of Computational Science Room 07Department of Computational Science Room 07--24, level 7, SOC124, level 7, SOC1
National University of SingaporeNational University of Singapore Tel: 65Tel: 65--68746874--6877; Email: 6877; Email: [email protected]@nus.edu.sg ; Web: ; Web: http://xin.cz3.nus.edu.sghttp://xin.cz3.nus.edu.sg
Content:Content:
•• Brief introduction of BIDD group and researchBrief introduction of BIDD group and research..
•• BIDD inventionsBIDD inventions
22
BIDD: BIDD: BiBioinformatics and oinformatics and DDrug rug DDesign Groupesign Group Group Members:
• Computer-Aided Drug Design: C.W. Yap, C.J. Zheng, L.Y. Han, H. Li • Bioinformatics Methods/Software: C.Z. Cai, L.Y. Han, J. Cui, H. H. Lin • Medicinal Herb: C.Y. Ung, C.Y. Kong, H. Zhou • Bioinformatics Database: L.Y. Han, H. Zhou, C. J. Zheng, B. Xie
Former Members:
D.G. Zhi (UCSD), Y.J. Guo (GWU), L.Z. Sun (U Tenn.), J. F. Wang (MSU), L.X. Yao (RPI) W. Liu (DUT), D. Mi (NUS, DMU), Z.R. Li (SiChuan U), Y. Xue (SiChuan U), Z.W. Cao (SCBIT), Z.L. Ji (Xiamen U) X.L. Gu (?), X. Chen (Blueprint), W.K. Yeo (IMCB)
PI: Y. Z. Chen
33
Research at BIDD (Research at BIDD (BiBioinformatics and oinformatics and DDrug rug DDesign Groupesign Group)) • Computer aided drug design:
• INVDOCK drug target prediction method and software (US Patent US6,519,611; Proteins 1999; 36:1), side-effect target prediction (J. Mol. Graph. Mod. 2003; 21,309)
• SVM-based drug design, toxicity, pharmacokinetics, target prediction method and software (J. Chem. Inf. Comput. Sci. 2004, 44,1630; 44, 1497; Toxicol. Sci. 2004; 79,170; J Pharm Sci 2004 accepted)
• Drug resisitant mutation (J. Mol. Graph. Mod. 2001; 19, 560)
• Bioinformatics:
Database development:
• Drug-related proteins and pathways: Therapeutic targets TTD (Nucleic. Acids. Res. 2002, 30, 412) Drug ADME-related proteins ADME-AP (Clin. Pharmacol. Ther. 2002, 71, 405) Drug adverse reaction targets DART (Drug Safety 2003; 26, 685) Therapeutically relevant multiple pathways TMRP (Bioinformatics 2004, accepted)
• Biomolecular binding: Ligand-protein binding energy CLiBE (Comp. Chem.2002; 26, 661) Biomolecular binding kinetic data KDBI (Nucleic. Acids. Res.2003; 31, 255)
44
Research at Research at BIDD (BIDD (BiBioinformatics and oinformatics and DDrug rug DDesign Group)esign Group) • Bioinformatics:
Bioinformatics software: Protein function prediction SVMProt (Nucleic Acids Res. 2003; 31, 3692) Protein motions MoViES (Nucleic Acids Res.2004; 32, W679)
Hardware tool Simulation of biological pathways, molecules, nano-device by electronic circuits (U.S. Regular Patent Application No.: 10/674,586)
Research: Protein function prediction (Proteins 2004; 55, 66; RNA 2004; 10, 355; Virology 2004) Protein motions (Biopolymers, 2001; 58, 319; J. Mol. Graph. Mod. 2003; 21,309)
• Medicinal herb research:
• Foodstuff and botanicals benefit and consumption computing method, software and databases (US Provisional Application No. 60/512,479).
• Traditional Chinese Medicine Databases: TCM-ID (paper submitted) • Mechanistic study of medicinal herbs (Am. J. Chin. Med. 2002, 30, 139; Nat. Prod.
Rep. 2003; 20, 432) • TCM recepe analysis (Am. J. Chin. Med. 2004, accepted)
55
Research at Research at BIDD (BIDD (BiBioinformatics and oinformatics and DDrug rug DDesign Group)esign Group) Year Number of Publications
Impact factor > 7 Impact factor 4~7 Impact factor 2~4 Impact factor <2 Total
2001 1 2 4 1 8
2002 1 2 0 3 6
2003 3 1 2 2 8
2004 1 4 5 5 15
Publication Statistics
13.316Drug Safety
13.367Toxicological Science
•• Bioinformatics tools (protein function prediction, databases)Bioinformatics tools (protein function prediction, databases)
•• Simulation of biological pathways, molecules and Simulation of biological pathways, molecules and nanonano--devices by electronic circuitsdevices by electronic circuits
Technologies developed by Technologies developed by the Bioinformatics and Drug Design Group of NUSthe Bioinformatics and Drug Design Group of NUS •• FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant
software and databases (software and databases (US Provisional Application No. 60/512,479US Provisional Application No. 60/512,479).).
•• ComputerComputer--aided TCM info and research systemsaided TCM info and research systems: Integrated info sources of traditional : Integrated info sources of traditional Chinese medicinal recipes, herbs, ingredients and effects. TCM mChinese medicinal recipes, herbs, ingredients and effects. TCM mechanism study, echanism study, recipe design and validation software.recipe design and validation software.
•• Computer drug target search software INVDOCKComputer drug target search software INVDOCK (US Patent US6,519,611 B1):(US Patent US6,519,611 B1): Applications: prediction of unknown targets of drugs, drug side Applications: prediction of unknown targets of drugs, drug side effects and mechanism, effects and mechanism, mechanism of herbsmechanism of herbs
•• SVMSVM--based drug design and property prediction softwarebased drug design and property prediction software: : Useful for Useful for inhibitor/activator/substrate prediction, drug safety and pharmainhibitor/activator/substrate prediction, drug safety and pharmacokinetic prediction.cokinetic prediction.
•• Protein function prediction software Protein function prediction software SVMProtSVMProt: Useful for novel proteins, distantly: Useful for novel proteins, distantly-- related proteins, homologous proteins of different function.related proteins, homologous proteins of different function.
•• Simulation of biological pathways, Simulation of biological pathways, biomoleculesbiomolecules, , nanonano--machines by electronic circuits machines by electronic circuits (U.S. Regular Patent Application No.: 10/674,586):(U.S. Regular Patent Application No.: 10/674,586): FastFast--speed biospeed bio--system and system and nanonano-- system simulation tools, design of lifesystem simulation tools, design of life--processprocess--emulating circuitsemulating circuits
•• Bioinformatics software and databases developed by BIDD: Bioinformatics software and databases developed by BIDD: http://xin.cz3.nus.edu.sg/group/bidd.htmhttp://xin.cz3.nus.edu.sg/group/bidd.htm
Contact: Prof. Chen Yu Zong.Contact: Prof. Chen Yu Zong. Tel: 6874Tel: 6874--6877 6877
Email: Email: [email protected]@nus.edu.sg Web: Web: http://xin.cz3.nus.edu.sghttp://xin.cz3.nus.edu.sg..
FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant software and databasessoftware and databases
Foodstuff or botanicalsFoodstuff or botanicals Weighing deviceWeighing device
Computer loaded with Computer loaded with FBBC ConsultantFBBC Consultant
Select, input, scan name ofSelect, input, scan name of foodstuff or botanicalsfoodstuff or botanicals
1. Health or medical effects 2. Required daily quantity 3. Enough quantity on
weighing device?
Option 2Option 2
Option 1Option 1
FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant software and databasessoftware and databases
FBBC consultant currently covers 50 fruits, 46 vegetables, 2575 herbs Effort is being made to collect info for additional fruits, vegetables, foodstuffs of other classes, botanicals, and herbal products
FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant software and databasessoftware and databases
1111
TCM Info Sources at BIDDTCM Info Sources at BIDD TCM-ID: Traditional Chinese Medicine-Information Database
To provide integrated information about:
• TCM recipe, constituent herbs, herbal ingredients, effect on proteins • Function at the formula, herb and compound levels • Molecular structure
Recipe 1000 Herbs 1100 Ingredients 7200 Targeting protein 500
TCM Formula
Mechanism of TCM: Synergy of Multiple Herbal Mechanism of TCM: Synergy of Multiple Herbal Ingredients Against Multiple TargetsIngredients Against Multiple Targets
Mixture of multiple herbs: Actions > Simple sum
Synergy: Mutual enhancement Mutual counteraction Maintenance and balance
Multiple targets: Therapeutics Symptom treatment Side effect modulation Drug delivery and clearance Boost of immune system Energy, PH, temperature balance/restoration Harmonization Pharmacology & Therapeutics 2000, 86:191-198
1313
TCM Mechanism Study System at BIDDTCM Mechanism Study System at BIDD Computer Match-Making
TCMID Database FBICD
Computer Match-Making
Collective therapeutic and maintenance effects
Toxicity / side effects and modulation
Drug delivery and clearance Nat. Prod. Rep., 20, 432 - 444 (2003) Am. J. Chin. Med., 30, 139-154. (2002)
TCM recipe
or herb
Computer Drug Target Search Software INVDOCK Computer Drug Target Search Software INVDOCK (US Patent US6,519,611 B1)(US Patent US6,519,611 B1)
Application 1: search of unknown targets of a drug
Drug
ProteinProtein Cavity Cavity
DrugDrug--protein dockingprotein docking Get next protein ?Get next protein ?
Get proteinGet protein
Computer Drug Target Search Software INVDOCK Computer Drug Target Search Software INVDOCK (US Patent US6,519,611 B1)(US Patent US6,519,611 B1)
Application 2: Prediction of side-effect and mechanism of a drug
Drug
SideSide--effecteffect proteinprotein Cavity Cavity
Docked proteinsDocked proteins
Get proteinGet protein
Protein functionsProtein functions
1616
Classification of Drugs or Proteins by SVMClassification of Drugs or Proteins by SVM • Applications: Drug Design, Side-Effect Prediction, Pharmacokinetic Properties
• A drug or a protein is classified as either belong (+) or not belong (-) to a class
Examples of drug class: inhibitor of a protein, BBB penetrating, genotoxic Examples of protein class: enzyme EC3.4 family, DNA-binding
• By screening against all classes, the property of a drug or the function of a protein can be identified
Drug or Protein
Class-3
-
-
+
- -
Drug J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004) Toxicol. Sci. 79,170 (2004) J Pharm Sci., accepted (2004)
Protein Nucleic. Acids Res. 31, 3692 (2003) Proteins 55, 66 (2004) RNA 10, 355 (2004) Virology, accepted (2004)
1717
SVM for Classification of DrugsSVM for Classification of Drugs How to represent a drug?
• Each structure represented by specific feature vector assembled from structural, physico-chemical properties: – Simple molecular properties (molecular weight, no. of rotatable bonds
etc. 18 in total) – Molecular Connectivity and shape (28 in total) – Electro-topological state polarity (84 in total) – Quantum chemical properties (electric charge, polaritability etc. 13 in
total) – Geometrical properties (molecular size vector, van der Waals volume,
molecular surface etc. 16 in total)
J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004)
Toxicol. Sci. 79,170 (2004).
Computer loaded Computer loaded with with SVMProtSVMProt
Support vector machinesSupport vector machines classifier for every classifier for every
Drug classDrug class
Identified Identified classesclasses
Drug designed Drug designed or property or property predicted predicted
Send structure to classifierSend structure to classifier Input structure
through internet
Option 2
Option 1 http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Your drug structure
Which class your Which class your drug belongs to?drug belongs to?
Drug Chemical Structure Chemical
Structure
J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004)
Toxicol. Sci. 79,170 (2004).
1919
SVM for Classification of ProteinsSVM for Classification of Proteins How to represent a protein?
• Each sequence represented by specific feature vector assembled from encoded representations of tabulated residue properties: – amino acid composition – Hydrophobicity – normalized Van der Waals volume – polarity, – Polarizability – Charge – surface tension – secondary structure – solvent accessibility
• Three descriptors, composition (C), transition (T), and distribution (D), are used to describe global composition of each of these properties.
Nucleic Acids Res. 2003; 31: 3692-3697
Protein function prediction software Protein function prediction software SVMProtSVMProt Useful for functional prediction of novel proteins, distantly-related proteins, homologous proteins of different functions
Your protein sequence
Support vector machinesSupport vector machines classifier for every classifier for every
protein functional familyprotein functional family
Identified Identified Functional familiesFunctional families
Protein functionalProtein functional indicationsindications
Nucleic. Acids Res. 31, 3692-3697 (2003)
Input sequence through internet
http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Which functional Which functional families your protein families your protein
belong to?belong to?
Useful for functional prediction of novel proteins, distantly-related proteins, homologous proteins of different functions.
Protein families covered:
46 enzyme families, 3 receptor families, 4 transporter and channel families, 6 DNA- and RNA-binding families, 8 structural families, 2 regulator/factor families.
SVMProt web-version at: http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Protein function prediction software Protein function prediction software SVMProtSVMProt
Nucl. Acids Res. 31, 3692-3697 (2003)
Probability of correct prediction
Application 1: Fast-speed bio-system and nano-system simulation tools.
Biological pathway
voltages in circuit
Simulation of biological pathways, Simulation of biological pathways, biomoleculesbiomolecules, , nanonano--machines by electronic circuitsmachines by electronic circuits
Application 2: Design of life-system or life-process emulating electronic circuits
Biological process
Bioinformatics software and databasesBioinformatics software and databases developed by BIDDdeveloped by BIDD
Software: • SVM-based drug design and property prediction software, J. Chem. Inf. Comput. Sci. 44,1630
(2004), J. Chem. Inf. Comput. Sci. 44, 1497 (2004), Toxicol. Sci. 79,170 (2004). • SVMProt: protein function prediction software, http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi, 2,260
visits July 2003-Oct 2003, Nucleic Acids Res., 31: 3692-3697 (2003). • INVDOCK: drug/molecule target prediction software, US patent US6,519,611 B1, Proteins, 43,
217-226 (2001)
Databases: • Therapeutic target database, http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp, 11,261 visits Jan 2002-Oct
2003, Nucleic Acids Res. (2002) , 30, 412-415. • Drug adverse reaction target database, http://xin.cz3.nus.edu.sg/group/drt/dart.asp, 3,869 visits Oct
2002-Oct 2003, Drug Safety 2003; 26: 685-690 • Drug ADME associated protein database, http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp,
2,941visits June 2002-Oct 2003, Clin. Pharmacol. Ther. 71, 405 (2002) • Kinetic data of biomolecular interactions database, http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp,
2020 visits Jan 2003-Oct 2003, Nucleic. Acids. Res. 31, 255-257 (2003) • Computed ligand binding energy database, http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp, 2,404
visits Apr 2002-Oct 2003, Comp. Chem. 26, 661-666(2002)
Summary of proofSummary of proof--ofof--principles principles testing resultstesting results
Content:Content:
•• Herbal ingredient target identification and therapeutic effect pHerbal ingredient target identification and therapeutic effect prediction resultsrediction results
•• SVM drug design, side effect, pharmacokinetic property predictioSVM drug design, side effect, pharmacokinetic property prediction resultsn results
•• SVMProtSVMProt protein function prediction resultsprotein function prediction results
2727
INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Targets of 4H-tamoxifen (Proteins. 1999; 36:1)
PDB Putative Protein Target Experimental Finding Clinical Implication
1a52 Estrogen Receptor Drug target Confirmed Treatment of breast cancer 36
1akz Uracil-DNA Glycosylase
1ayk Collagenase Inhibited activity Confirmed Tumor cell invasion and cancer metastasis
38
Combination therapy for cancer 43
1dht, 1fdt 17β -Hydroxysteroid Dehydrogenase
Inhibitor Confirmed
1gsd, 3ljr
Suppressed enzyme and activity Genotoxicity and carcinogenicity
41
Implicated Modulation of immune response 44
1p1g Macrophage Migration Inhibitory factor
1ulb Purine Nucleoside Phosphorylase
1zqf DNA Polymerase β
2nll Retinoic Acid Receptor
1aa8 D-Amino Acid Oxidase Implicated1afs
3α -Hydroxysteroid Dehydrogenase Effect on androgen induced activity
Hepatic steroid metabolism 42
1sep Sepiapterin Reductase
2toh Tyrosine 3-Monooxygenase
2828
INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Drug Toxicity Targets (J. Mol. Graph. Mod. 2001, 20, 199)
Compound Number of experimentally confirmed or implicated toxicity targets
Number of toxicity targets predicted by INVDOCK
Number of toxicity targets missed by INVDOCK
Number of toxicity targets without structure or involving covalent bond
Number of INVDOCK predicted toxicity targets without experimental finding
Aspirin 15 9 2 4 2
Gentamicin 17 5 2 10 2
Ibuprofen 5 3 0 2 2
Indinavir 6 4 0 2 2
Neomycin 14 7 1 6 6
Penicillin G 7 6 0 1 8
Tamoxifen 2 2 0 0 4
Vitamin C 2 2 0 0 3
Total 68 38 5 25 29
2929
INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Toxicity and side effect targets of Aspirin (J. Mol. Graph. Mod. 2001, 20, 199)
PDB Protein Experimental
Finding Target Status
Toxicity/Side Effect Ref
1a42 Carbonic anhydrase II Activate enzyme activity that may lead to increase in plasma bicarbonate concentration.
Implicated Metabolic alkalosis (hypoventilation).
Implicated Aspirin-induced asthma
Implicated Hypertension, thrombolysis
1hdy Alcohol dehydrogenase Inhibition of activity
Confirmed Increased blood alcohol level
Gentry RT
3030
INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Targets of Chinese Medicinal Herbal Ingredients
(Am. J. Chin. Med. 2002, 30, 139) Chinese Natural Product
Number of Identified Putative and Known Therapeutic Targets
Number Confirmed or Implicated Therapeutic Targets by experiment
Number of Identified Putative and Known Toxicity/Side effect Targets
Number Confirmed or Implicated Toxicity/Side Effect Targets by experiment
Acronycine 3 1 4 -
Baicalin 14 4 6 -
Catechin 17 12 5 -
3131
Therapeutic Effects of Identified Therapeutic Targets
Therapeutic Effects Observed
disease Indigestion improvement No report Improvement of Vaso- dilation/contraction
Stimulation of blood circulation
Medicinal Plant Potential Therapeutic Targets Identified
Experimentally Confirmed and
Implicated So Far
SVM Drug Prediction ResultsSVM Drug Prediction Results Protein inhibitor/activator/substrate prediction:
• 86% of the 129 estrogen receptor activators and 84% of 101 non-activators correctly predicted.
• 81% of 116 P-glycoprotein substrates and 79% of 85 non-substrates correctly predicted
Drug Toxicity Prediction:
• 97% of 102 TdP+ and 84% of 243 TdP- agents correctly predicted • 73% of 229 genotoxic and 93% of 631 non-genotoxic agents correctly predicted
Pharmacokinetics prediction:
• 95% of 276 BBB+ and 82% of 139 BBB- agents correctly predicted • 90% of 131 human intestine absorption and 80% of 65 non-absoption agents
correctly predicted.
J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004)
Toxicol. Sci. 79,170 (2004).
Overall prediction accuracies:
• 87% of the 34,582 proteins correctly assigend to their respective functional family. • 97% of the 310,000 non-member proteins correctly predicted
Novel enzymes:
• 67% of the 12 non-homologous enzymes (having no homlogous proteins by PSI- BLAST search of NR databases) are correctly assigned
• 83% of the 29 non-homologous enzymes (having no homologous proteins by PSI- BLAST search of SwissProt database) are correctly assigned.
• 70% of the 20 pairs of homologous enzymes of different functions are correctly assigned.
NR databases include all non-redundant GenBank, CDS translations, PDB, SwissProt, PIR, and PRF databases
92% of 12,900 enzymes correctly assigned by BLAST in 1997 Nucleic Acids Res 2003; 31, 3692
Proteins 2004; 55, 66
Simulation of a biological pathway by electronic circuitSimulation of a biological pathway by electronic circuit
Bioinformatics software and databasesBioinformatics software and databases developed by BIDDdeveloped by BIDD
Software: • SVM-based drug design and property prediction software, J. Chem. Inf. Comput. Sci. 44,1630
(2004), J. Chem. Inf. Comput. Sci. 44, 1497 (2004), Toxicol. Sci. 79,170 (2004). • SVMProt: protein function prediction software, http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi, 2,260
visits July 2003-Oct 2003, Nucleic Acids Res., 31: 3692-3697 (2003). • INVDOCK: drug/molecule target prediction software, US patent US6,519,611 B1, Proteins, 43,
217-226 (2001)
Databases: • Therapeutic target database, http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp, 11,261 visits Jan 2002-Oct
2003, Nucleic Acids Res. (2002) , 30, 412-415. • Drug adverse reaction target database, http://xin.cz3.nus.edu.sg/group/drt/dart.asp, 3,869 visits Oct
2002-Oct 2003, Drug Safety 2003; 26: 685-690 • Drug ADME associated protein database, http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp,
2,941visits June 2002-Oct 2003, Clin. Pharmacol. Ther. 71, 405 (2002) • Kinetic data of biomolecular interactions database, http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp,
2020 visits Jan 2003-Oct 2003, Nucleic. Acids. Res. 31, 255-257 (2003) • Computed ligand binding energy database, http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp, 2,404
visits Apr 2002-Oct 2003, Comp. Chem. 26, 661-666(2002)
Chen Yu ZongChen Yu Zong
BiBioinformatics and oinformatics and DDrug rug DDesign Groupesign Group Department of Computational Science Room 07Department of Computational Science Room 07--24, level 7, SOC124, level 7, SOC1
National University of SingaporeNational University of Singapore Tel: 65Tel: 65--68746874--6877; Email: 6877; Email: [email protected]@nus.edu.sg ; Web: ; Web: http://xin.cz3.nus.edu.sghttp://xin.cz3.nus.edu.sg
Content:Content:
•• Brief introduction of BIDD group and researchBrief introduction of BIDD group and research..
•• BIDD inventionsBIDD inventions
22
BIDD: BIDD: BiBioinformatics and oinformatics and DDrug rug DDesign Groupesign Group Group Members:
• Computer-Aided Drug Design: C.W. Yap, C.J. Zheng, L.Y. Han, H. Li • Bioinformatics Methods/Software: C.Z. Cai, L.Y. Han, J. Cui, H. H. Lin • Medicinal Herb: C.Y. Ung, C.Y. Kong, H. Zhou • Bioinformatics Database: L.Y. Han, H. Zhou, C. J. Zheng, B. Xie
Former Members:
D.G. Zhi (UCSD), Y.J. Guo (GWU), L.Z. Sun (U Tenn.), J. F. Wang (MSU), L.X. Yao (RPI) W. Liu (DUT), D. Mi (NUS, DMU), Z.R. Li (SiChuan U), Y. Xue (SiChuan U), Z.W. Cao (SCBIT), Z.L. Ji (Xiamen U) X.L. Gu (?), X. Chen (Blueprint), W.K. Yeo (IMCB)
PI: Y. Z. Chen
33
Research at BIDD (Research at BIDD (BiBioinformatics and oinformatics and DDrug rug DDesign Groupesign Group)) • Computer aided drug design:
• INVDOCK drug target prediction method and software (US Patent US6,519,611; Proteins 1999; 36:1), side-effect target prediction (J. Mol. Graph. Mod. 2003; 21,309)
• SVM-based drug design, toxicity, pharmacokinetics, target prediction method and software (J. Chem. Inf. Comput. Sci. 2004, 44,1630; 44, 1497; Toxicol. Sci. 2004; 79,170; J Pharm Sci 2004 accepted)
• Drug resisitant mutation (J. Mol. Graph. Mod. 2001; 19, 560)
• Bioinformatics:
Database development:
• Drug-related proteins and pathways: Therapeutic targets TTD (Nucleic. Acids. Res. 2002, 30, 412) Drug ADME-related proteins ADME-AP (Clin. Pharmacol. Ther. 2002, 71, 405) Drug adverse reaction targets DART (Drug Safety 2003; 26, 685) Therapeutically relevant multiple pathways TMRP (Bioinformatics 2004, accepted)
• Biomolecular binding: Ligand-protein binding energy CLiBE (Comp. Chem.2002; 26, 661) Biomolecular binding kinetic data KDBI (Nucleic. Acids. Res.2003; 31, 255)
44
Research at Research at BIDD (BIDD (BiBioinformatics and oinformatics and DDrug rug DDesign Group)esign Group) • Bioinformatics:
Bioinformatics software: Protein function prediction SVMProt (Nucleic Acids Res. 2003; 31, 3692) Protein motions MoViES (Nucleic Acids Res.2004; 32, W679)
Hardware tool Simulation of biological pathways, molecules, nano-device by electronic circuits (U.S. Regular Patent Application No.: 10/674,586)
Research: Protein function prediction (Proteins 2004; 55, 66; RNA 2004; 10, 355; Virology 2004) Protein motions (Biopolymers, 2001; 58, 319; J. Mol. Graph. Mod. 2003; 21,309)
• Medicinal herb research:
• Foodstuff and botanicals benefit and consumption computing method, software and databases (US Provisional Application No. 60/512,479).
• Traditional Chinese Medicine Databases: TCM-ID (paper submitted) • Mechanistic study of medicinal herbs (Am. J. Chin. Med. 2002, 30, 139; Nat. Prod.
Rep. 2003; 20, 432) • TCM recepe analysis (Am. J. Chin. Med. 2004, accepted)
55
Research at Research at BIDD (BIDD (BiBioinformatics and oinformatics and DDrug rug DDesign Group)esign Group) Year Number of Publications
Impact factor > 7 Impact factor 4~7 Impact factor 2~4 Impact factor <2 Total
2001 1 2 4 1 8
2002 1 2 0 3 6
2003 3 1 2 2 8
2004 1 4 5 5 15
Publication Statistics
13.316Drug Safety
13.367Toxicological Science
•• Bioinformatics tools (protein function prediction, databases)Bioinformatics tools (protein function prediction, databases)
•• Simulation of biological pathways, molecules and Simulation of biological pathways, molecules and nanonano--devices by electronic circuitsdevices by electronic circuits
Technologies developed by Technologies developed by the Bioinformatics and Drug Design Group of NUSthe Bioinformatics and Drug Design Group of NUS •• FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant
software and databases (software and databases (US Provisional Application No. 60/512,479US Provisional Application No. 60/512,479).).
•• ComputerComputer--aided TCM info and research systemsaided TCM info and research systems: Integrated info sources of traditional : Integrated info sources of traditional Chinese medicinal recipes, herbs, ingredients and effects. TCM mChinese medicinal recipes, herbs, ingredients and effects. TCM mechanism study, echanism study, recipe design and validation software.recipe design and validation software.
•• Computer drug target search software INVDOCKComputer drug target search software INVDOCK (US Patent US6,519,611 B1):(US Patent US6,519,611 B1): Applications: prediction of unknown targets of drugs, drug side Applications: prediction of unknown targets of drugs, drug side effects and mechanism, effects and mechanism, mechanism of herbsmechanism of herbs
•• SVMSVM--based drug design and property prediction softwarebased drug design and property prediction software: : Useful for Useful for inhibitor/activator/substrate prediction, drug safety and pharmainhibitor/activator/substrate prediction, drug safety and pharmacokinetic prediction.cokinetic prediction.
•• Protein function prediction software Protein function prediction software SVMProtSVMProt: Useful for novel proteins, distantly: Useful for novel proteins, distantly-- related proteins, homologous proteins of different function.related proteins, homologous proteins of different function.
•• Simulation of biological pathways, Simulation of biological pathways, biomoleculesbiomolecules, , nanonano--machines by electronic circuits machines by electronic circuits (U.S. Regular Patent Application No.: 10/674,586):(U.S. Regular Patent Application No.: 10/674,586): FastFast--speed biospeed bio--system and system and nanonano-- system simulation tools, design of lifesystem simulation tools, design of life--processprocess--emulating circuitsemulating circuits
•• Bioinformatics software and databases developed by BIDD: Bioinformatics software and databases developed by BIDD: http://xin.cz3.nus.edu.sg/group/bidd.htmhttp://xin.cz3.nus.edu.sg/group/bidd.htm
Contact: Prof. Chen Yu Zong.Contact: Prof. Chen Yu Zong. Tel: 6874Tel: 6874--6877 6877
Email: Email: [email protected]@nus.edu.sg Web: Web: http://xin.cz3.nus.edu.sghttp://xin.cz3.nus.edu.sg..
FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant software and databasessoftware and databases
Foodstuff or botanicalsFoodstuff or botanicals Weighing deviceWeighing device
Computer loaded with Computer loaded with FBBC ConsultantFBBC Consultant
Select, input, scan name ofSelect, input, scan name of foodstuff or botanicalsfoodstuff or botanicals
1. Health or medical effects 2. Required daily quantity 3. Enough quantity on
weighing device?
Option 2Option 2
Option 1Option 1
FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant software and databasessoftware and databases
FBBC consultant currently covers 50 fruits, 46 vegetables, 2575 herbs Effort is being made to collect info for additional fruits, vegetables, foodstuffs of other classes, botanicals, and herbal products
FBBC ConsultantFBBC Consultant: : FFoodstuff and oodstuff and BBotanicals otanicals BBenefit and enefit and CConsumption onsumption ConsultantConsultant software and databasessoftware and databases
1111
TCM Info Sources at BIDDTCM Info Sources at BIDD TCM-ID: Traditional Chinese Medicine-Information Database
To provide integrated information about:
• TCM recipe, constituent herbs, herbal ingredients, effect on proteins • Function at the formula, herb and compound levels • Molecular structure
Recipe 1000 Herbs 1100 Ingredients 7200 Targeting protein 500
TCM Formula
Mechanism of TCM: Synergy of Multiple Herbal Mechanism of TCM: Synergy of Multiple Herbal Ingredients Against Multiple TargetsIngredients Against Multiple Targets
Mixture of multiple herbs: Actions > Simple sum
Synergy: Mutual enhancement Mutual counteraction Maintenance and balance
Multiple targets: Therapeutics Symptom treatment Side effect modulation Drug delivery and clearance Boost of immune system Energy, PH, temperature balance/restoration Harmonization Pharmacology & Therapeutics 2000, 86:191-198
1313
TCM Mechanism Study System at BIDDTCM Mechanism Study System at BIDD Computer Match-Making
TCMID Database FBICD
Computer Match-Making
Collective therapeutic and maintenance effects
Toxicity / side effects and modulation
Drug delivery and clearance Nat. Prod. Rep., 20, 432 - 444 (2003) Am. J. Chin. Med., 30, 139-154. (2002)
TCM recipe
or herb
Computer Drug Target Search Software INVDOCK Computer Drug Target Search Software INVDOCK (US Patent US6,519,611 B1)(US Patent US6,519,611 B1)
Application 1: search of unknown targets of a drug
Drug
ProteinProtein Cavity Cavity
DrugDrug--protein dockingprotein docking Get next protein ?Get next protein ?
Get proteinGet protein
Computer Drug Target Search Software INVDOCK Computer Drug Target Search Software INVDOCK (US Patent US6,519,611 B1)(US Patent US6,519,611 B1)
Application 2: Prediction of side-effect and mechanism of a drug
Drug
SideSide--effecteffect proteinprotein Cavity Cavity
Docked proteinsDocked proteins
Get proteinGet protein
Protein functionsProtein functions
1616
Classification of Drugs or Proteins by SVMClassification of Drugs or Proteins by SVM • Applications: Drug Design, Side-Effect Prediction, Pharmacokinetic Properties
• A drug or a protein is classified as either belong (+) or not belong (-) to a class
Examples of drug class: inhibitor of a protein, BBB penetrating, genotoxic Examples of protein class: enzyme EC3.4 family, DNA-binding
• By screening against all classes, the property of a drug or the function of a protein can be identified
Drug or Protein
Class-3
-
-
+
- -
Drug J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004) Toxicol. Sci. 79,170 (2004) J Pharm Sci., accepted (2004)
Protein Nucleic. Acids Res. 31, 3692 (2003) Proteins 55, 66 (2004) RNA 10, 355 (2004) Virology, accepted (2004)
1717
SVM for Classification of DrugsSVM for Classification of Drugs How to represent a drug?
• Each structure represented by specific feature vector assembled from structural, physico-chemical properties: – Simple molecular properties (molecular weight, no. of rotatable bonds
etc. 18 in total) – Molecular Connectivity and shape (28 in total) – Electro-topological state polarity (84 in total) – Quantum chemical properties (electric charge, polaritability etc. 13 in
total) – Geometrical properties (molecular size vector, van der Waals volume,
molecular surface etc. 16 in total)
J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004)
Toxicol. Sci. 79,170 (2004).
Computer loaded Computer loaded with with SVMProtSVMProt
Support vector machinesSupport vector machines classifier for every classifier for every
Drug classDrug class
Identified Identified classesclasses
Drug designed Drug designed or property or property predicted predicted
Send structure to classifierSend structure to classifier Input structure
through internet
Option 2
Option 1 http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Your drug structure
Which class your Which class your drug belongs to?drug belongs to?
Drug Chemical Structure Chemical
Structure
J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004)
Toxicol. Sci. 79,170 (2004).
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SVM for Classification of ProteinsSVM for Classification of Proteins How to represent a protein?
• Each sequence represented by specific feature vector assembled from encoded representations of tabulated residue properties: – amino acid composition – Hydrophobicity – normalized Van der Waals volume – polarity, – Polarizability – Charge – surface tension – secondary structure – solvent accessibility
• Three descriptors, composition (C), transition (T), and distribution (D), are used to describe global composition of each of these properties.
Nucleic Acids Res. 2003; 31: 3692-3697
Protein function prediction software Protein function prediction software SVMProtSVMProt Useful for functional prediction of novel proteins, distantly-related proteins, homologous proteins of different functions
Your protein sequence
Support vector machinesSupport vector machines classifier for every classifier for every
protein functional familyprotein functional family
Identified Identified Functional familiesFunctional families
Protein functionalProtein functional indicationsindications
Nucleic. Acids Res. 31, 3692-3697 (2003)
Input sequence through internet
http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Which functional Which functional families your protein families your protein
belong to?belong to?
Useful for functional prediction of novel proteins, distantly-related proteins, homologous proteins of different functions.
Protein families covered:
46 enzyme families, 3 receptor families, 4 transporter and channel families, 6 DNA- and RNA-binding families, 8 structural families, 2 regulator/factor families.
SVMProt web-version at: http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Protein function prediction software Protein function prediction software SVMProtSVMProt
Nucl. Acids Res. 31, 3692-3697 (2003)
Probability of correct prediction
Application 1: Fast-speed bio-system and nano-system simulation tools.
Biological pathway
voltages in circuit
Simulation of biological pathways, Simulation of biological pathways, biomoleculesbiomolecules, , nanonano--machines by electronic circuitsmachines by electronic circuits
Application 2: Design of life-system or life-process emulating electronic circuits
Biological process
Bioinformatics software and databasesBioinformatics software and databases developed by BIDDdeveloped by BIDD
Software: • SVM-based drug design and property prediction software, J. Chem. Inf. Comput. Sci. 44,1630
(2004), J. Chem. Inf. Comput. Sci. 44, 1497 (2004), Toxicol. Sci. 79,170 (2004). • SVMProt: protein function prediction software, http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi, 2,260
visits July 2003-Oct 2003, Nucleic Acids Res., 31: 3692-3697 (2003). • INVDOCK: drug/molecule target prediction software, US patent US6,519,611 B1, Proteins, 43,
217-226 (2001)
Databases: • Therapeutic target database, http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp, 11,261 visits Jan 2002-Oct
2003, Nucleic Acids Res. (2002) , 30, 412-415. • Drug adverse reaction target database, http://xin.cz3.nus.edu.sg/group/drt/dart.asp, 3,869 visits Oct
2002-Oct 2003, Drug Safety 2003; 26: 685-690 • Drug ADME associated protein database, http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp,
2,941visits June 2002-Oct 2003, Clin. Pharmacol. Ther. 71, 405 (2002) • Kinetic data of biomolecular interactions database, http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp,
2020 visits Jan 2003-Oct 2003, Nucleic. Acids. Res. 31, 255-257 (2003) • Computed ligand binding energy database, http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp, 2,404
visits Apr 2002-Oct 2003, Comp. Chem. 26, 661-666(2002)
Summary of proofSummary of proof--ofof--principles principles testing resultstesting results
Content:Content:
•• Herbal ingredient target identification and therapeutic effect pHerbal ingredient target identification and therapeutic effect prediction resultsrediction results
•• SVM drug design, side effect, pharmacokinetic property predictioSVM drug design, side effect, pharmacokinetic property prediction resultsn results
•• SVMProtSVMProt protein function prediction resultsprotein function prediction results
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INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Targets of 4H-tamoxifen (Proteins. 1999; 36:1)
PDB Putative Protein Target Experimental Finding Clinical Implication
1a52 Estrogen Receptor Drug target Confirmed Treatment of breast cancer 36
1akz Uracil-DNA Glycosylase
1ayk Collagenase Inhibited activity Confirmed Tumor cell invasion and cancer metastasis
38
Combination therapy for cancer 43
1dht, 1fdt 17β -Hydroxysteroid Dehydrogenase
Inhibitor Confirmed
1gsd, 3ljr
Suppressed enzyme and activity Genotoxicity and carcinogenicity
41
Implicated Modulation of immune response 44
1p1g Macrophage Migration Inhibitory factor
1ulb Purine Nucleoside Phosphorylase
1zqf DNA Polymerase β
2nll Retinoic Acid Receptor
1aa8 D-Amino Acid Oxidase Implicated1afs
3α -Hydroxysteroid Dehydrogenase Effect on androgen induced activity
Hepatic steroid metabolism 42
1sep Sepiapterin Reductase
2toh Tyrosine 3-Monooxygenase
2828
INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Drug Toxicity Targets (J. Mol. Graph. Mod. 2001, 20, 199)
Compound Number of experimentally confirmed or implicated toxicity targets
Number of toxicity targets predicted by INVDOCK
Number of toxicity targets missed by INVDOCK
Number of toxicity targets without structure or involving covalent bond
Number of INVDOCK predicted toxicity targets without experimental finding
Aspirin 15 9 2 4 2
Gentamicin 17 5 2 10 2
Ibuprofen 5 3 0 2 2
Indinavir 6 4 0 2 2
Neomycin 14 7 1 6 6
Penicillin G 7 6 0 1 8
Tamoxifen 2 2 0 0 4
Vitamin C 2 2 0 0 3
Total 68 38 5 25 29
2929
INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Toxicity and side effect targets of Aspirin (J. Mol. Graph. Mod. 2001, 20, 199)
PDB Protein Experimental
Finding Target Status
Toxicity/Side Effect Ref
1a42 Carbonic anhydrase II Activate enzyme activity that may lead to increase in plasma bicarbonate concentration.
Implicated Metabolic alkalosis (hypoventilation).
Implicated Aspirin-induced asthma
Implicated Hypertension, thrombolysis
1hdy Alcohol dehydrogenase Inhibition of activity
Confirmed Increased blood alcohol level
Gentry RT
3030
INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Targets of Chinese Medicinal Herbal Ingredients
(Am. J. Chin. Med. 2002, 30, 139) Chinese Natural Product
Number of Identified Putative and Known Therapeutic Targets
Number Confirmed or Implicated Therapeutic Targets by experiment
Number of Identified Putative and Known Toxicity/Side effect Targets
Number Confirmed or Implicated Toxicity/Side Effect Targets by experiment
Acronycine 3 1 4 -
Baicalin 14 4 6 -
Catechin 17 12 5 -
3131
Therapeutic Effects of Identified Therapeutic Targets
Therapeutic Effects Observed
disease Indigestion improvement No report Improvement of Vaso- dilation/contraction
Stimulation of blood circulation
Medicinal Plant Potential Therapeutic Targets Identified
Experimentally Confirmed and
Implicated So Far
SVM Drug Prediction ResultsSVM Drug Prediction Results Protein inhibitor/activator/substrate prediction:
• 86% of the 129 estrogen receptor activators and 84% of 101 non-activators correctly predicted.
• 81% of 116 P-glycoprotein substrates and 79% of 85 non-substrates correctly predicted
Drug Toxicity Prediction:
• 97% of 102 TdP+ and 84% of 243 TdP- agents correctly predicted • 73% of 229 genotoxic and 93% of 631 non-genotoxic agents correctly predicted
Pharmacokinetics prediction:
• 95% of 276 BBB+ and 82% of 139 BBB- agents correctly predicted • 90% of 131 human intestine absorption and 80% of 65 non-absoption agents
correctly predicted.
J. Chem. Inf. Comput. Sci. 44,1630 (2004) J. Chem. Inf. Comput. Sci. 44, 1497 (2004)
Toxicol. Sci. 79,170 (2004).
Overall prediction accuracies:
• 87% of the 34,582 proteins correctly assigend to their respective functional family. • 97% of the 310,000 non-member proteins correctly predicted
Novel enzymes:
• 67% of the 12 non-homologous enzymes (having no homlogous proteins by PSI- BLAST search of NR databases) are correctly assigned
• 83% of the 29 non-homologous enzymes (having no homologous proteins by PSI- BLAST search of SwissProt database) are correctly assigned.
• 70% of the 20 pairs of homologous enzymes of different functions are correctly assigned.
NR databases include all non-redundant GenBank, CDS translations, PDB, SwissProt, PIR, and PRF databases
92% of 12,900 enzymes correctly assigned by BLAST in 1997 Nucleic Acids Res 2003; 31, 3692
Proteins 2004; 55, 66
Simulation of a biological pathway by electronic circuitSimulation of a biological pathway by electronic circuit
Bioinformatics software and databasesBioinformatics software and databases developed by BIDDdeveloped by BIDD
Software: • SVM-based drug design and property prediction software, J. Chem. Inf. Comput. Sci. 44,1630
(2004), J. Chem. Inf. Comput. Sci. 44, 1497 (2004), Toxicol. Sci. 79,170 (2004). • SVMProt: protein function prediction software, http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi, 2,260
visits July 2003-Oct 2003, Nucleic Acids Res., 31: 3692-3697 (2003). • INVDOCK: drug/molecule target prediction software, US patent US6,519,611 B1, Proteins, 43,
217-226 (2001)
Databases: • Therapeutic target database, http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp, 11,261 visits Jan 2002-Oct
2003, Nucleic Acids Res. (2002) , 30, 412-415. • Drug adverse reaction target database, http://xin.cz3.nus.edu.sg/group/drt/dart.asp, 3,869 visits Oct
2002-Oct 2003, Drug Safety 2003; 26: 685-690 • Drug ADME associated protein database, http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp,
2,941visits June 2002-Oct 2003, Clin. Pharmacol. Ther. 71, 405 (2002) • Kinetic data of biomolecular interactions database, http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp,
2020 visits Jan 2003-Oct 2003, Nucleic. Acids. Res. 31, 255-257 (2003) • Computed ligand binding energy database, http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp, 2,404
visits Apr 2002-Oct 2003, Comp. Chem. 26, 661-666(2002)