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)