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Tricks and improvements in Structural Genomics
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Tricks and improvements in Structural Genomics
Chantal Abergel
Structural and Genomic Information Laboratory
UMR 2589-CNRS, IBSM, 31 chemin J. Aiguier,
13009 Marseille, FRANCE
http://www.igs.cnrs-mrs.fr
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Microbial Genomics
Bioinform
atics
Structural Genomics
Mimivirus - T. whipplei -M. timonensis - A. baumannii
Functional Genomics
Exper
imen
ts
R. felis - R. africae - R. belli - R. massiliae, R. conorii -
Exploration of microbial genomes
Fundamental: surprisesDirected: biomedical
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Structural genomics projects: TargetDB
Escherichia coli • Orfan genes• Conserved genes of unknown function
search for new antibiotic targets PROFUN : search for new antifungal targets• Saccharomyces cerevisiae:
Essential genes conserved in fungi absent in vertebrate genomes
• Candida albicans: Conserved in fungal pathogens absent in vertebrate genomes
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MasterDB
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~ 300 Targets
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Incomplete Factorial Design: Experimental matrix (SAmBa)
Automated protocol, 12 conditions (out of 96 possible)
4 strains3 temperatures3 growth media
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Dot Blot for recombinant protein detection
Total extracts and soluble extracts (supernatant after centrifugation) deposited on nitrocellulose membranes
Use of anti (his)5 antibody coupled HRP (Qiagen)
ECL detection
Dot blot statistical analysisDot blot statistical analysis SSoluble expression oluble expression conditioncondition
Up to 12 proteins tested a day
90%
Rosetta 17°C 2YT
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Microdialysis
Affinity purification
Scale up (1L culture)
pINon ionic Non ionicionic ionic
pH
Overnight dialysis• ODtotal
• ODsoluble
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0
0,1
0,2
0,3
0,4
0,5
0,6
200 250 300 350
Buffer1
Buffer2
Buffer3
Buffer4
Buffer5
Desalting Concentration
Best buffer:ODtotal/ODsoluble ~1
λ nm
Abs
orba
nce
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• Electrophoresis (native, IEF, SDS)• N-terminal Sequencing /Mass Spectrometry• Circular Dichroïsm• Dynamic light scattering• Stability over time
Biochemical & Biophysical Characterization
• Purification optimization• Crystallization
• Stability of produced proteins ?• Folding ?• Protein solution monodispersity
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Combinatorial approach
• Functional & Structural predictionsFunctional & Structural predictions
• BioinformaticsBioinformatics
sequence homologies (BMCD)sequence homologies (BMCD)
binding motif, hypothetical ligand, co-factors…binding motif, hypothetical ligand, co-factors…
• Electrophoresis (native, IEF…)Electrophoresis (native, IEF…)
Set of variables to be tested Set of variables to be tested
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SAmBA: For crystallization prospection
www.igs.cnrs.fr/samba
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Tecan diluting
distributing robot8 needles
Greinercrystallization
plates 5x96 wells
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Promising crystals ...
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In house testing ...
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Poor diffraction
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Soaking/desiccation
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YbgL
Step 1
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Step 2
YbgL
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Final
YbgL
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YggV
Step 1
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Final
YggV
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CA0996
Step 1
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Final
CA0996
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What do they have in common ? (1)
Mol. weight (kDa)
OligomerizationSpace
group
Cell dimension
(Å)
Molecule per AU
YbgL 26 monomer C2
a = 70.3 b = 36.7 c = 79.7 β = 3.5
1
YggV 21 monomer P43212a = 78c = 7.9
1
CA0996 16 dodecamer P222a = 93
b = 304.5 c = 155.5
24
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What do they have in common ? (2)
Crystallization conditions
Cryoprotectant Soaking time Dataset
Resolution ESRF
YbgLSodium citrate
pH 9.15Ethylene
glycol 10%2h
FIP :2.29 ÅRmerge: 5.8%
(28.7)
YggV(NH4)2SO4
pH 9glycerol 10% 30 mins
ID14: 2.0 ÅRmerge: 6.4 %
(29.3)
CA0996PEG8000
LiSO4 pH 7 glycerol 10 % 15 mins FIP: 3.0
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Experimental Team
Sabine ChenivesseSophie Cozzani
Céline DeregnaucourtSandra Jeudy
Marjorie Varagnol
Bioinformatics Team
Hiroyuki OgataStéphane AudicOlivier Poirot
Pr. Jean-Michel Claverie