GGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTAC CCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGG

GGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAA

Recent Developments in CADD

Art Cherkasov UBC / VGH Prostate CentreUBC / VGH Prostate Centre [email protected]

THE UNIVERSITY OF BRITISH COLUMBIA MEDICINE INFECTIOUS DISEASES


1. Overview of ‘in silico’ Drug Design.SHBG (testosterone binding globulin) case study

2. “Inductive” qsar descriptors: background ,applications

3. QSAR Models of ‘Antibiotic-likeness’, etc

4. Chemoinformatics-based modeling of Antimicrobial Peptides

Functional genomics BioinformaticsSNPs

Gene expressionDNA chipsmicro-arraysSAGE

ProteomicsLS/MSchipsPSTProtein interaction

networks

Functional genomics BioinformaticsSNPs

Gene expressionDNA chipsmicro-arraysSAGE

ProteomicsLS/MSchipsPSTProtein interaction

networks

Discovery of Novel Target GenesIs Expected to Increase the NumberOf Current Drug Targets from 1,500 to 10,000+

HTS Discovery

The most commonly used method of identifying a lead molecule is experimental high throughput screening (HTS) of compound databases against a target protein.

(i) the numbers of compounds that can be economically screened is small compared with the chemical space available, and

(ii) the theoretical coverage of molecular diversity within the screening set is limited

(iii) SUCCESS RATE <0.1%

in silico DRUG DESIGN

Human Sex Hormone Binding Protein (HSBG)

Several alternative modes of bindingDocking pocket is allostericPresence of Zn ions in active site

High throughput in silico screening

Pharmacophore-based discovery

Superposition of natural ligands to find common structural features

OH

OH

OH

OH

DOCKINGQSAR % activity

6,000,000

1,600,000

150,000

1000

130

30

8

SUCCESS RATE >22%A. Cherkasov et alJ Med Chem 2005

Substitution of testosterone

Superposition of best lead with testosterone inside the SHBG



TARGET

Molecular Database

To create QSAR model using GlideScore values for docked

structures as dependent variable

Actual GlideScore values

‘INDUCTIVE’ DESCRIPTORS

To cluster and sort DB entries according to the

‘inductive’ descriptors values

Clustered and sorteddatabase of

potential leads

To select ~10% of non-docked DB entries covering the entire DB range (to ensure the maximum representation of

all clusters)

Remaining non-docked DB entriesDOCKING

Chemical structures

to dock

To use the QSAR models to predict

theoretical GlideScores for the

non-docked DB entries

To remove 20% of ‘non-docked’ entries

with the lowest predicted GlideScore

Predicted GlideScore

values

Pre-Docking

filters

To compute ‘inductive’ QSAR descriptors for all DB entries

12

3

45

6

7 8



True Positives (GS<-8.5)

-19

31

81

131

181

231

281

-9816 10184 30184 50184 70184 90184

Substances processed

non-Progressive Docking

20K_10%_TP

20K_20%_TP

10K_10%_TP

10K_20%_TP

5K_10%_TP

5K_20%_TP

1K_10%_TP

20K_20%_FN

20K_20%_FN

10K_10%_FN

10K_20%_FN

5K_10%_FN

5K_20%_FN

1K_10%_FN

False Negatives



3,300,000

2,066,000

143,421

7,171

87

41

25SUCCESS RATE >61%A. Cherkasov et alJ Med Chem 2008


QSAR Models of

‘Drug-likeness’, ‘Antibiotic-likeness’, ‘Bacterial Metabolite-likeness’ etc


from A.Cherkasov & A. Tropsha, Nature Drug Dscovery Reviews, 2008 (submitted)

QSAR – Quantitative Structure-Activity Relationships

0

100

200

300

400

500

600

700

800

900

1000

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

QSAR papers in PubMed

Protein structures in PDB (x100)

compounds in CAS (x100k)


QSAR, Chemometrics,Chemoinformatics

Molecular structuregets translated into numbers

(descriptors)


Mathematical function

f ( Descriptors) ~ Activity

GGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAA`

n

jiGi ji

ijG r

RRs

,2

2

n

jiGi ji

ijijG r

R

,2

200* )(

Cherkasov A et al, J Phys Org Chem1996

n

ji ji

ji

n

ji ji

jii

G

r

RR

r

RR

2

22

2

220 )(

SE *


‘‘inductive’ QSAR descriptorsinductive’ QSAR descriptors

Inductive constant

steric constant

electronegativity

“Inductive ” QSAR Descriptors

Average Electronegativities of the Negatively/Positively Charged AtomsMolecular (equalized) ElectronegativityTotal Formal ChargeAverage Atomic HardnessSum of Atomic HardnessesAverage Negative/Positive ChargesLargest Positive ChargeAverage Atomic Hardnesses of Negatively/Positively Charged AtomsHardness of the Most Positively Charged AtomLargest Hardness among the Negatively/Positively Charged AtomsSum of Softnesses of Negatively Charged AtomsSteric Effect on the Most Negatively Charged AtomMost Negative Inductive Constant of an Atom in MoleculeLargest Positive Inductive Effect on an Atom in MoleculeThe Smallest Steric Effect on a Atom in MoleculeSum of all Negative Inductive Effects on Atoms in Molecule …..



QSAR solutions with “Inductive ” DescriptorsQSAR models for antibiotics, drugs and bacteria and human metabolitesA. Cherkasov. J Chem Inf Model, 46, 1214 (2006), E. Karakoc et al Bioinformatics, 22, e243v (2006)QSAR models for antimicrobial peptides A. Cherkasov. Current Computer-Aided Drug Design, 1, 21 (2005)H. Jenssen et al Chem Biol Drug Des. 70, 134 (2007)A hybrid QSAR/Docking approach ‘Progressive docking’ has been developedA. Cherkasov et al, J Med Chem. 49, 7466 (2006)Various low-micromolar to nanomolar inhibitors developed with the help of ‘inductive’ QSAR solutionsA. Cherkasov et al, J Med Chem, 48, 3203 (2005)A. Cherkasov et al J Chem Inf Model, 45, 1842 (2005)QSAR model for pK of carboxylic acids and amines (3D Correlation Analysis)A. Cherkasov et al, J Phys Chem, 107, 9695 (2003)Descriptors-derived system for protein charge calculationA. Cherkasov. J Chem Inf Comp Sci, 43, 2039 (2003)QSAR model for dipole momentsA. Cherkasov et al, J. Mol. Struct. Theochem, 497, 115 (2000)QSAR models for oxidation energies of free radicals A Cherkasov et al, J Chem Inf Comp Sci, 40, 1222 (2000), 39, 1057 (1999), 38, 1151 (1998)QSAR models for Gas Basicity of AminesA. Cherkasov et al, J Mol Graph Model, 17, 28 (1999)Predictive model for inductive substituent constantsA. Cherkasov et al, J Phys Org Chem 11, 437 (1998)


QSAR MODEL FOR ANTIBIOTICS

A mixture of known antibiotics and non-antibiotics

2/3 of all compounds for training 1/3 of all compounds for testing

VALIDATION: How the model performs on test compounds

QSAR model for the training set

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Mea

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AntimicrobialBacterial MetabolitesHuman MetabolitesDrugsDruglikes




A number of QSAR models have been elaborated to separate individual clusters within the dataset of 958 human therapeutics, 519 antimicrobials, 1202 drug-like chemicals, as well as 1102 human-, 551 bacterial-, 2351 plant- and 825 fungal metabolites.

Antimicrobials from Drugs

Antimicrobials from Drug-likes

Distinguishing Antimicrobials from all others

Distinguishing Antimicrobials versus Drugs versus Drug-likes

QSAR model for Bacterial Metabolites

Train Test Train Test Train Test Train Test Train Test

T_P 327 130 332 140 294 124 270 89 360 139

T_N 631 248 841 342 1490 621 1486 644 792 347

F_P 49 33 7 14 32 20 17 14 39 26

F_N 33 35 30 23 66 41 108 58 48 19

SPEC 0.93 0.88 0.99 0.96 0.98 0.97 0.99 0.98 0.95 0.93

SENS 0.91 0.79 0.92 0.86 0.82 0.75 0.71 0.61 0.88 0.88

ACCUR 0.92 0.85 0.97 0.93 0.95 0.92 0.93 0.91 0.93 0.92

PPV 0.87 0.80 0.98 0.91 0.90 0.86 0.94 0.86 0.90 0.84

NPV 0.95 0.88 0.97 0.94 0.96 0.94 0.93 0.92 0.94 0.95

Method ValidationTruePosit.

TrueNegat

FalsePosit.

FalseNegat

Spec. Sens. Accur PPV NPV

Bacteria Metabolites versus (Drugs + Drug-likes + Antibacterials + Human Metabolites)

kNN

Training 70% 311 2537 112 83 0.96 0.79 0.94 074 0.97

Testing 30% 135 1091 44 33 0.96 0.80 0.94 0.75 0.97

LOO 455 3637 147 107 0.96 0.81 0.94 0.76 0.97

LDA

Training 70% 240 2587 62 154 0.98 0.61 0.93 0.79 0.94

Testing 30% 90 1088 47 78 0.96 0.54 0.90 0.66 0.93

LOO 336 3665 119 226 0.97 0.60 0.92 0.74 0.94

MLR

Training 70% 301 2525 124 93 0.95 0.76 0.93 0.71 0.96

Testing 30% 119 1073 62 49 0.95 0.71 0.91 0.66 0.96

LOO 406 3603 181 156 0.95 0.72 0.92 0.69 0.96

ANN

Training 70% 338 2597 52 55 0.98 0.86 0.96 0.87 0.98

Testing 30% 159 1076 59 10 0.95 0.94 0.95 0.73 0.99

LOO 534 3780 4 28 0.99 0.95 0.99 0.99 0.99

Separation of various classes of substances in the chemical space

Antibacterials

Inactive Chemicals

General drugs

Bacterial metabolites





Lead compound Merck annotation

BML score

Antibiotic-likeness (30-10-2 ANN)

Metabolite analogue

O

O

OH

OO

Lovastatin

Antilipemic 0.71 0.56 O

O

OH

OO

NP-007587

O

OO

O

O

OO

O

OH

OH OH

O

O

O

OH

O

O

OH

O

O

OO

OH

Olivomycin A

Antineoplastic Cytostatic agent

0.72 0.99

O

OO

O

OO

OH

OH

OH OH

O

O

OH

O

O

OH

O

O

OOOH

O

O

OH

OH

NP-009248

O

OHOH

OH Gentisic acid

Analgesic; Antiinflamatory

0.74 0.66

O

OH

OH

OH

NP-001423

MERCK Database scaningMERCK Database scaning



Lead compound Merck annotation

BML score

Antibiotic-likeness (30-10-2 ANN)

Metabolite analogue

O

O

OH

OO

Lovastatin

Antilipemic 0.71 0.56 O

O

OH

OO

NP-007587

O

OO

O

O

OO

O

OH

OH OH

O

O

O

OH

O

O

OH

O

O

OO

OH

Olivomycin A

Antineoplastic Cytostatic agent

0.72 0.99

O

OO

O

OO

OH

OH

OH OH

O

O

OH

O

O

OH

O

O

OOOH

O

O

OH

OH

NP-009248

O

OHOH

OH Gentisic acid

Analgesic; Antiinflamatory

0.74 0.66

O

OH

OH

OH

NP-001423

CONFIRMEDCONFIRMED

CONFIRMEDCONFIRMED

CONFIRMEDCONFIRMED

MERCK Database scanningMERCK Database scanning


Problem of Antibiotics Resistance

Bad Bugs Need Drugs: IDSA, March 2006

Antimicrobial Availability Task Force Widespread prevalence of MDR bacteria in hospitals Few drugs in a pipeline, Urgent need for R&D

Experts Fear Increase in Drug-resistant Infectious Here: Globe and Mail, March 2006

MRSA, a treatment-resistant form of bacteria that spreads through direct contact, is called a greater threat to public health than SARS or bird flu. The Boston Globe, August 21, 2006


Resistance mechanisms are very effective (specific toward the existing antibiotics)



Lateral gene transfer…




Active transport…




Active transport…

Resistant mutations…



Non-specific bacterial inhibition/killing may represent alternative strategy for antibiotic development


Active transport…

Resistant mutations…



Possible answer – Antimicrobial Peptides(AMP)


AMP Modes of ActionOren and Shai (Biopolymers 1998)


8-50 AA long, gene-coded, often contain lead sequence, parts of innate immunity



Treated



Intracellular targets of antibacterial peptides.Diagram from Brogden, 2005.

Factors Influencing activity of AMP’s:


Usually helical, but can be beta, cyclic, irregular,induced

IKWLKIFL

Factors Influencing activity of AMP’s: Hydrophobicity, Positive Charge, two-phased


IKWLKIFL BUT: 9^20 possible sequence variants!!!



Sources of antibiotic peptides

SWISS-PROT database: ftp://ftp.ebi.ac.uk/pub/databases/swissprot/release/sprot42.dat

University of Nebraska Medical Center: http://aps.unmc.edu/AP/main.php

Biochemistry Department University of Triest, Italy: http://www.bbcm.units.it/~tossi/pag5.htm

National Library of Health Sciences, TERKKO, University of Helsinki: http://oma.terkko.helsinki.fi:8080/~SAPD/login

School of Crystallography, Birkbeck University of London: http://www.cryst.bbk.ac.uk/peptaibol/peptaibol_database_1lettercodes.htm

ftp://ftp.ebi.ac.uk/pub/databases/swissprot/release/sprot42.dat

http://aps.unmc.edu/AP/main.php

http://www.bbcm.units.it/~tossi/pag5.htm

http://www.bbcm.units.it/~tossi/pag5.htm

http://oma.terkko.helsinki.fi:8080/~SAPD/login

http://www.cryst.bbk.ac.uk/peptaibol/peptaibol_database_1lettercodes.htm


Examples of typical gene-coded AMPs:


beta-Defensins, alpha-DefensinsCecropins, CholecystokininsStomoxyns, Gastrins, Transferrins, Magainins, Brevinins, Xenopsins Dermaseptins, Provicilins Cupiennines, Vicilins, Corticostatins, Apidaecin, Cathelicidin , Statherins, Histatins Bombinins, Dermaseptins,Maximins, Dermadistinctins, Maculatina, Caerins, Aureins, Citropin, Waglerins, Gastrins, Cholecystokinins, Magainins, Xenopsins


Examples of typical gene-coded AMPs:


beta-Defensins, alpha-DefensinsCecropins, CholecystokininsStomoxyns, Gastrins, Transferrins, Magainins, Brevinins, Xenopsins Dermaseptins, Provicilins Cupiennines, Vicilins, Corticostatins, Apidaecin, Cathelicidin , Statherins, Histatins Bombinins, Dermaseptins,Maximins, Dermadistinctins, Maculatina, Caerins, Aureins, Citropin, Waglerins, Gastrins, Cholecystokinins, Magainins, Xenopsins non -TOXICnon -TOXICnon - IMMUNOGENICnon - IMMUNOGENICdo not cause RESISTANCEdo not cause RESISTANCEfast and broadly ACTIVEfast and broadly ACTIVE



>1300 AMPs have been derived from the public sources, >1300 AMPs have been derived from the public sources, > 900 synthesised in-house> 900 synthesised in-house

BIOINFORMATICS PIPELINE



GENES

PEPTIDE SEQUENCES

PEPTIDE 3D

INDUCTIVE DESCRIPTORS, QSAR Models of Antibiotic-likeness, BM - likeness

n

jiGi ji

ijijG r

R

,2

200* )(

IKWLKIFL

BIOINFORMATICS

QSAR

CHEMINFORMATICS PIPELINE



~1300 peptides with measured activity

Trained QSAR model(s)



Training set

Top %as actives

Accuracy Specificity SensitivityPositive

Predictive Value

A

5% 0.96 0.98 0.62 0.58

10% 0.93 0.94 0.76 0.39

25% 0.78 0.78 0.85 0.17

B

5% 0.94 0.97 0.33 0.30

10% 0.88 0.90 0.33 0.12

25% 0.77 0.77 0.80 0.12

A+B

5% 0.95 0.97 0.47 0.47

10% 0.91 0.92 0.54 0.27

25% 0.76 0.77 0.66 0.13

10 cross

ROC


THE UNIVERSITY OF BRITISH COLUMBIA MEDICINE INFECTIOUS DISEASESDesigned Cheminformatics pipeline

To apply pre-trained QSAR modelsto virtual sequence variants


THE UNIVERSITY OF BRITISH COLUMBIA MEDICINE INFECTIOUS DISEASESDesigned Cheminformatics pipeline

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100,000 PEPTIDES have been designed using random sequence composition with ongoing enrichment for key aminoacids

20 TESTED



MX-226, MBI-226, omiganan

ILRWPWPWRRK

- prevention of wounds-Burn-device-related infections (central venous catheter related infections)

MIGENIX © Phase III-a clinical trials



Bac2A effect on bacteria

Pseudomonas aeruginosa, Pseudomonas maltophilia, Staphylococcus aureus, Enterobacter cloacae


100,000 randomly designed, 20 tested from Q1-Q4(predicted high-, medium-, and low-actives

EX VIVOagainst12 bacterial strains (uM)



Untreated



Treated



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ACTIVITYCHARGE

H20 – phobic momentH20 – phobicity

PROPERTIES DISTRIBUTIONS AMONG HIGH-, MEDIUM- AND LOW-ACTIVES



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PROPERTIES DISTRIBUTIONS AMONG HIGH-, MEDIUM- AND LOW-ACTIVES



% of Survived Reb Blood Cellsafter 24h peptide administration @50ug/ml

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Treatment Group

CF

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lIN VIVO Ability of new antimicrobial peptides HHC-10 and HHC-36 to protect mice against S. aureus infections. Bacterial loads in the peritoneal lavage from individual mice after 24 h of infection. Dead animals were assigned the highest CFU count obtained in the experiment. The solid line represents the arithmetic mean for each group. ``



CONCLUSIONS

Atomic-resolution Chemoinformatics allow building predictive models

outperforming sequence- based Bioinformatics methods


UBC I.D., Microbiology

CIHR/MSFHR Bioinformatics

CIHR

V.I.D.O. U.Sask.Saskatoon, SK

Genome Canada,Genome BC


Lab:Michael Hsing

Simon ChanNels Thorstein

Chris Fjell

Fuqiang BanMelian Huang

Ken BydlerOsvaldo Santos-Filho

Evgeny Maksakov

UBC MicrobiologyREW Hancock

K HilpertH Jenssen

U.Sask VIDO:L Babuick & team

Documents

GGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTAC CCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGG