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

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

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

MasterDB

~ 300 Targets

Incomplete Factorial Design: Experimental matrix (SAmBa)

Automated protocol, 12 conditions (out of 96 possible)

4 strains3 temperatures3 growth media

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

Microdialysis

Affinity purification

Scale up (1L culture)

pINon ionic Non ionicionic ionic

pH

Overnight dialysis• ODtotal

• ODsoluble

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

• 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

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

SAmBA: For crystallization prospection

www.igs.cnrs.fr/samba

Tecan diluting

distributing robot8 needles

Greinercrystallization

plates 5x96 wells

Promising crystals ...

In house testing ...

Poor diffraction

Soaking/desiccation

YbgL

Step 1

Step 2

YbgL

Final

YbgL

YggV

Step 1

Final

YggV

CA0996

Step 1

Final

CA0996

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

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

Experimental Team

Sabine ChenivesseSophie Cozzani

Céline DeregnaucourtSandra Jeudy

Marjorie Varagnol

Bioinformatics Team

Hiroyuki OgataStéphane AudicOlivier Poirot

Pr. Jean-Michel Claverie