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![Page 1: Modelling proteomes Ram Samudrala Department of Microbiology How does the genome of an organism specify its behaviour and characteristics?](https://reader036.fdocuments.in/reader036/viewer/2022062520/5697bfd91a28abf838caf93a/html5/thumbnails/1.jpg)
Modelling proteomesRam Samudrala
Department of Microbiology
How does the genome of an organism specify its behaviour and characteristics?
![Page 2: Modelling proteomes Ram Samudrala Department of Microbiology How does the genome of an organism specify its behaviour and characteristics?](https://reader036.fdocuments.in/reader036/viewer/2022062520/5697bfd91a28abf838caf93a/html5/thumbnails/2.jpg)
Proteome – all proteins of a particular system
~60,000 in human
~60,000 in rice
~4500 in bacterialike Salmonella andE. coli
Several thousanddistinct sequencefamilies
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Modelling proteomes – understand the structure of individual proteins
A few thousanddistinct structuralfolds
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Modelling proteomes – understand their individual functions
Thousands ofpossible functions
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Modelling proteomes – understand their expression
Different expressionpatterns based ontime and location
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Modelling proteomes – understand their interactions
Interactions andexpression patternsare interdependentwith structure andfunction
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Protein folding
…-L-K-E-G-V-S-K-D-…
…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…
one amino acid
DNA
protein sequence
unfolded protein
native state
spontaneous self-organisation (~1 second)
not uniquemobileinactive
expandedirregular
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Protein folding
…-L-K-E-G-V-S-K-D-…
…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…
one amino acid
DNA
protein sequence
unfolded protein
native state
spontaneous self-organisation (~1 second)
unique shapeprecisely orderedstable/functionalglobular/compacthelices and sheets
not uniquemobileinactive
expandedirregular
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De novo prediction of protein structure
sample conformational space such thatnative-like conformations are found
astronomically large number of conformations5 states/100 residues = 5100 = 1070
select
hard to design functionsthat are not fooled by
non-native conformations(“decoys”)
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Semi-exhaustive segment-based foldingEFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK
generatecontinuous , distributionslocal and global moves
… …
minimisemonte carlo with simulated annealingconformational space annealing, GA
… …
filter all-atom pairwise interactions, bad contactscompactness, secondary structure,density of generated conformations
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2.52 Å 5.06 Å
Model 1
CASP6 prediction for T0215
Ling-Hong Hung/Shing-Chung Ngan
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3.63 Å 5.42 Å
Model 5
CASP6 prediction for T0236
Ling-Hong Hung/Shing-Chung Ngan
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2.25 Å 4.31 Å
Model 1
CASP6 prediction for T0281
Ling-Hong Hung/Shing-Chung Ngan
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Comparative modelling of protein structure
KDHPFGFAVPTKNPDGTMNLMNWECAIPKDPPAGIGAPQDN----QNIMLWNAVIP** * * * * * * * **
… …
scanalign
refine
physical functions
build initial model
minimum perturbation
construct non-conservedside chains and main chains
graph theory, semfold
de novo simulation
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T0247 RAPDF TMscore RMSD MaxSub
cf-model -30.14 0.8448 4.055 0.6563
parent 1 -27.09 0.8391 4.108 0.6446
parent 2 -26.68 0.8318 4.194 0.625
parent 3 -26.59 0.8252 4.197 0.6051
parent 4 -26.25 0.839 3.981 0.6281
parent 5 -18.51 0.8422 3.979 0.6416
CASP6 prediction for T0247
Model 1
Tianyun Liu
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Model 1
Parent 1
Parent 2 Parent 3
T0247 RAPDF TM-score RMSD MaxSub
cf-model -37.44 0.8718 2.166 0.7911
parent 1 -34.87 0.8662 2.233 0.7789
parent 2 -33.99 0.8248 2.166 0.7402
parent 3 -36.83 0.8254 2.139 0.7456
CASP6 prediction for T0271
Tianyun Liu
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0.45
0.55
0.65
0.75
0.85
0.95
1.05T0
246
T026
8
T023
3
T023
1
T027
7
T026
6
T027
1
T024
7
T026
7
T027
6
T027
4
T026
9
T028
2
T024
4
T021
1
T023
4
T023
2
T024
3
T026
4
T022
9
T020
0
T021
3
T027
9
Target ID
TM
-sc
ore
sCF-models average of parent models
CASP6 overall summaries
Tianyun Liu
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Similar global sequence or structure does not imply similar function
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Qualitative function classification
Kai Wang
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Prediction of HIV-1 protease-inhibitor binding energies with MD
MD simulation time
Cor
rela
tion
coe
ffic
ien
t
ps0 0.2 0.4 0.6 0.8 1.0
1.0
0.5
with MD
without MD
Ekachai Jenwitheesuk
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Prediction of inhibitor resistance/susceptibility
Kai Wang / Ekachai Jenwitheesuk
http://protinfo.compbio.washington.edu/pirspred/
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Integrated structural and functional annotation of proteomes
structure based methodsmicroenvironment analysis
zinc binding site?
structure comparison
homology function?
sequence based methods
sequence comparisonmotif searches
phylogenetic profilesdomain fusion analyses
+
*
**
*Bioverse
*
*
Assign function toentire protein space:
key paradigm is use ofhomology to transfer information across
organisms
experimental datasingle molecule + genomic/proteomic
+EXPRESSION
+INTERACTION
}
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Bioverse – explore relationships among molecules and systems
Jason McDermott/Michal Guerquin/Zach Frazier
http://bioverse.compbio.washington.edu
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Bioverse – explore relationships among molecules and systems
Jason McDermott/Michal Guerquin/Zach Frazier
http://bioverse.compbio.washington.edu
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Bioverse – explore relationships among molecules and systems
Jason McDermott/Michal Guerquin/Zach Frazier
http://bioverse.compbio.washington.edu
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Bioverse – explore relationships among molecules and systems
Jason McDermott/Michal Guerquin/Zach Frazier
http://bioverse.compbio.washington.edu
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Bioverse – prediction of protein interaction networks
Jason McDermott
Interacting protein database
protein α
protein β
experimentallydeterminedinteraction
Target proteome
protein A85%
predictedinteraction
protein B90%
Assign confidence based on similarity and strength of interaction
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Bioverse – E. coli predicted protein interaction network
Jason McDermott
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Bioverse – M. tuberculosis predicted protein interaction network
Jason McDermott
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Bioverse – C. elegans predicted protein interaction network
Jason McDermott
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Bioverse – H. sapiens predicted protein interaction network
Jason McDermott
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Bioverse – network-based annotation for C. elegans
Jason McDermott
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Jason McDermottArticulation point proteins
Bioverse – identifying key proteins on the anthrax predicted network
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Jason McDermott
Bioverse – identification of virulence factors
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Bioverse - Integrator
Aaron Chang
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Take home message
Prediction of protein structure, function, and networks may be used to model whole genomes to
understand organismal function and evolution
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Acknowledgements
Aaron ChangChuck MaderDavid NickleEkachai JenwitheesukGong ChengJason McDermottKai Wang
Ling-Hong HungMike InouyeMichal GuerquinStewart MoughonShing-Chung NganTianyun LiuZach Frazier
National Institutes of HealthNational Science Foundation
Searle Scholars Program (Kinship Foundation)UW Advanced Technology Initiative in Infectious Diseases
http://bioverse.compbio.washington.eduhttp://protinfo.compbio.washington.edu