familias de proteinas -...
TRANSCRIPT
Curso de verano UCM 2008- Ana Rojas-CNIOJUL-2008 1
FAMILIAS DE PROTEINAS
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What is a sequence? . . . a string of characters…
Amino acidsACDEFGHIKLMNPQRSTVWY
NucleotideA: adeninaC: citosinaT: timinaG: guanina
MMITRWLFSTNHKDIGTLYMIFGAWAGMVGTALSLLIRAELSQPGALLGDDQIYNVIV
GTGATAATCACTCGTTGACTATTCTCAACCAACCACAAAGATATTGGTACCCTATACATGATTTTCGGGGCCTGAGCTGGAATAGTTGGAACCGCTCTAAGCCTACTTATTCGAGCCGAACTCAGCCAACCTGGAGCTCTCCTA
The User Guide
“Real”players
Traducción del mensaje (previa transcripción a ARN)Genetic Code:Triplet AGG
R (Arg), Codon = amino ácido
DNA
Protein
{ATGC}
{43}
Modified from F. Abascal
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We want to understand Proteins
Reminder: the genetic code is “degenerated”, leaving room for change!
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H|
NH2- C -CO2H|R
. . . And Proteins are built from amino-acids
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. . . And amino acids have spatial constraints
Schema of a peptide bond
Peptide bonds are rigid and planar
O
NH2CH
R1
CN
H
CHC
O
NCH
R2
R3
HOOC
N-terminus C-terminus
Peptide bonds
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TTCCPSIVAR SNFNVCRLPG TPEAICATYT GCIIIPGATC PGDYANEE SSHHHH HHHHHHHTTT HHHHHHHH S EE SSS GGG
1D
3D
. . . And spatial constrainsts give a particular 3D shape
Sequences givealso structural information(Estructuras)
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We’ve seen so far:
We compare sequences
Goal: find the “most likely” alignment (we’ll never be sure) that reflects the changes.
RPE_YEAST 6 IAPSIL----ASDFANLGCECHKVINAGADWLHIDVMDGHFVPNITLGQP 51 ||.|:| ..|...| .:.:..|...:|.|||| |||.|.::...
RPE_MYCPN 10 IAFSLLPLLHQFDRKLL----EQFFADGLRLIHYDVMD-HFVDNTVFQGE 54
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What is homology?
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• Homologue: the same organ under every variety of form and function (true or essential correspondence).
• Analogy: superficial or misleading similarity.
Owen’s definition of homology
Richard Owen, 1843
Remember: everything is about homology.
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AnalogySame function but differentOrigin.
Homology: common ancestor.May have different function.
Do not forget the underlying concept!
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HOMOLOGIA IMPLICA UN ORIGEN EVOLUTIVOCOMUN
Remember: everything is about homology.
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Similarity ≠ Homology
Similarity: mathematical concept.Homology: biological concept.
Remember: everything is about homology.
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Sequence analyses
Transference of function by homology?
Modified from F. Abascal
But all is a matter of definition:
How do we define function?
How do we transfer the function?
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Multiple alignments give more information than pair-wise
-Consensus sequences:
-Regular expressions or patterns
(to screen motifs)
-Profiles & hmm profiles
ALRDFATHDDDF SMTAEATHDSI ECDQAATHEAS
A-T-H-[DE]
AGTVATVSCAGTSATHACIGRCARGSCIGEMARLACIGDYARWSC.........IGTVARVSC <= Consense
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Regular expressions
•Any: x•Ambiguity:
[A,B] A, or B... {A,B..} any except A and B.
•Repeat: A(2,4) means A-A o A-A-A o A-A-A-A•N terminal: <, C-terminal: >
Example: [AC]-x-V-x(4)-{E,D}.
[Ala or Cys]-any-Val-any-any-any-any-{any but Glu or Asp}
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Perfiles (o PSSM): Like substitution matrix (i.e. BLOSUM) position specific.
F K L L S H C L L VF K A F G Q T M F QY P I V G Q E L L GF P V V K E A I L KF K V L A A V I A DL E F I S E C I I QF K L L G N V L V C
A -18 -10 -1 -8 8 -3 3 -10 -2 -8C -22 -33 -18 -18 -22 -26 22 -24 -19 -7D -35 0 -32 -33 -7 6 -17 -34 -31 0E -27 15 -25 -26 -9 23 -9 -24 -23 -1F 60 -30 12 14 -26 -29 -15 4 12 -29G -30 -20 -28 -32 28 -14 -23 -33 -27 -5H -13 -12 -25 -25 -16 14 -22 -22 -23 -10I 3 -27 21 25 -29 -23 -8 33 19 -23K -26 25 -25 -27 -6 4 -15 -27 -26 0L 14 -28 19 27 -27 -20 -9 33 26 -21M 3 -15 10 14 -17 -10 -9 25 12 -11N -22 -6 -24 -27 1 8 -15 -24 -24 -4P -30 24 -26 -28 -14 -10 -22 -24 -26 -18Q -32 5 -25 -26 -9 24 -16 -17 -23 7R -18 9 -22 -22 -10 0 -18 -23 -22 -4S -22 -8 -16 -21 11 2 -1 -24 -19 -4T -10 -10 -6 -7 -5 -8 2 -10 -7 -11V 0 -25 22 25 -19 -26 6 19 16 -16W 9 -25 -18 -19 -25 -27 -34 -20 -17 -28Y 34 -18 -1 1 -23 -12 -19 0 0 -18
Multiple alignment
Profile
Profiles
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Alignment of 5 sequences
3 consensus columns
m: is a match state, each has 20 residues emission probabilities (black bars)i: is an insertion state with also 20 emission probabilities.d: states are “mute” states with NO emission probabilities. b: begin and e: end. Arrows are transitions probabilities.
Positions are NOT independent: HMMs profiles
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Do not forget that pair-wise alignments are still useful
global versus local
Goes to get best alignment using the whole sequence length
Only if proteins have the same composition!
Goes for maximum scores in fragments
Here we find the Domain shuffling Issue!~ pieces of sequences shuffling along the evolutionary history
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Evolution Models
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Long time ago…
ACCGTACGGTTAA
ACGGTACGGTTAAACCGTCCGGTTAAACCGT-CGGTTAACCCGTACGGTTAAACCCGTACGGTTAA
time
A general evolution Model : random change + natural selection
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Long time ago…
ACCGTACGGTTAA
ACGGTACGGTTAAACCGTCCGGTTAAACCGT-CGGTTAACCCGTACGGTTAAACCCGTACGGTTAA
time
ACCG-CCGGTTAAACCCTCCGGTTAAACCGTCCGGTTCCCAATCCGTCCGGTTAAACCGTCCGCTTAA
Model : random change + natural selection
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Long time ago…
ACCGTACGGTTAA
ACGGTACGGTTAAACCGTCCGGTTAAACCGT-CGGTTAACCCGTACGGTTAAACCCGTACGGTTAA
time
ACCG-CCGGTTAAACCCTCCGGTTAAACCGTCCGGTTCCCAATCCGTCCGGTTAAACCGTCCGCTTAA
xn especies
ACCTCTAGTTAA
ACCGTTCCGAA
ACCGTCCGGTTGA
GGAGTACGGTTAA
ACCTGCAATTA
ACCGTACGGTTATA
ACCGTCGTAA
ACCGTACCCCGGTTAAGCCGTACCGTGGTCCA
CCGTCCCGTTAA
AACCGTACGGTTAA
Model : random change + natural selection
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A T F Y A G C D E L
How do proteins evolve? A hypothetical case:
Duplication
Glucose hydrolisis
A L F Y A G C E E L
A S Y Y A G C D E I
A T F Y A G C D E L
A T F Y A G C D E L *A S Y Y A G G D E I A S Y Y A G G D E IA T Y Y D G G D E IA T Y L A G G D E IA S R L A G G D E IA S Y Y A G G D E I
*A L F Y A G C E E L A L F Y A G C E E LA I F R A G C E E TA I F R A G C E E LA V F Y A G C E E L
Pseudogen=>LOST
Time & mutation
A S Y Y A G G D E I
Hidrolysisribose
Mutation
Speciation
Hidrolysisglucose
Hydrolisisribose
F(x)
Specificity
Hydrolisisribose
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Back to protein evolutionGene duplication?
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Homólogos: ortólogos y parálogos.
Ortólogos: genes que comparten el último ancestro común y cuya divergencia se debe a la especiación.
Los mismos genes en distintas especies.
Parálogos: genes que debido a una duplicación, ya no comparten el último ancestro. Frecuentemente tienen funciones distintas.
Imagen tomada de una presentación de Manuel José Gómez (CAB)
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Superfamilia: grupo de proteínas con un origen común.
Familia / Subfamilia: grupo de proteínas con una función común (jerarquía subjetiva).
proteínas ATP/GTP binding(superfamilia)
familia rasproteínas GTP-binding
factores de elongación
proteínas ATP-binding
rab (H. sapiens)
rab (M. musculus)
rab (C. elegans)
ras (H. sapiens)
ras (M. musculus)
ras (C. elegans)
ras2 (H. sapiens)
Subfamilia ras
Subfamilia rab
Dos formas de representarlo
rasrab
Model : random change + natural selection + gene duplication
(From Federico Abascal)
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Homólogos: ortólogos y parálogos.
rab (H. sapiens)
rab (M. musculus)
rab (C. elegans)
ras (H. sapiens)
ras (M. musculus)
ras (C. elegans)
ras2 (H. sapiens)Subfamilia ras. Grupo de ortólogos e in-paralogs.
Subfamilia rab. Grupo de ortólogos.
Las dos subfamilias sonparálogas entre sí.
in-paralogs.Duplicación reciente
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¿Por qué compararamos secuencias de . . .
1- para conocer la función de las proteínas:
-función general.-residuos importantes: p.e. centros activos.
2- para determinar en qué especies está una proteína.3- para predecir la estructura 3D de las proteínas. 4- para predecir especificidad funcional
proteínas?
ADN?
-para buscar genes:-ESTs.-ADN genómico.
-para estudios de genética poblacional (SNPs).-para comparar secuencias no codificantes.
Modified from F. Abascal
(Genomica)
(Filogenia)
(Estructura)
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What are sequences telling us?
ADGHLSCETRDLWYALDSOPRL
A L F Y A G C E E LA I F R A G C E E T
A S Y Y A G G D E IA T Y Y D G G D E IA T Y L A G G D E IA S R L A G G D E IA S Y Y A G G D E I
A L F Y A G C E E LA I F R A G C E E TA I F R A G C E E LA V F Y A G C E E L
A L F Y A G C E E L
PROTEIN FAMILIES are analysed via multiple alignments.
•NOTHING!=uninformative in evolutionary terms. … But, the physical and chemical properties of amino acidsCAN AID IN Secondary structure prediction(Estructuras)
•Very little, we could find clear ortologs: to detect gene dup.
Maybe A LOT!,•we can analyse trends in the Alignments: evolutionary info.•We can use this information to increase the complexityof our searches
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How? What for?
-Pair-wise- align 2 sequences- BLAST search against databases.
-Several proteins- multiple alignments (Clustalw, TCOFFEE, probabilistic).
-con motifs, profiles and hmm's- profiles: PSI-BLAST.- som DB’s:
· PROSITE· PFam· InterPro
•CLEAR ORTOLOGS
•Detect Paralogs and highly diverged•Evolutionary history
•Detect partial sequences•Beyond the twigth-light
The methods: sequence comparison.
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How to identify orthologs?
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They might be lots of paralogs many similar each other:
Which one is the real ortholog?
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The BEST bidirectional hit method (BBHs)
Genomas X e Y
Genomaancestral 0
Genomaancestral 1
No haydelecion
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Gen A2 is deleted: Method is still working though!
The BEST bidirectional hit method (BBHs)
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Gen A2 is deleted in genome X and gene A1 is deleted in genome Y: Method DOES NOT work
The BEST bidirectional hit method (BBHs)
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Automatic methods: COGS
•Best BH for each protein within genomes.•In-paralogs fusion (the most similar one)•Graph building using triangles•Fusion of triangles sharing 2 vertices•Grouping
PROBLEMS: DOMAIN SHUFFLING (Next classes)
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We use multiple alignments to analyse families
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Problem: the computational power required for:•Aligning 2 sequeces is NxM.•Aligning 3 sequences is NxMxL.
i.e.: if 2 seqs of 300 aa would take 1 sec, aligning 3 will take 300 secs... 10 will take 3008 secs (more than universe’s age).
The solution comes from heuristics. (ClustalW, Muscle, T-coffee).
A reminder: multiple alignments
Multiple alignment is a NP problem
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Information extracted from multiple sequence alignments
conserved
tree-determinants correlated mutations
What can we get from multiple alignments?
Different trends give different information: types of residues
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Motifs
Small conserved regions
Tend to correlate with functional characteristics :
- Active sites
-ligand binding sites.
-etc.
Can we get anything else?
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Gets Real!
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•The Acetyltransferases•Chemokine receptors
examples of functional specificity
What can we get from multiple alignments?= REAL examples.
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Acetyl transferases
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Carnitine transferases catalyse the exchange of acyl groups between carnitine and CoA (fatty acid metabolism). According to the acyl-CoA sensitivity:
CPTs (carnitine palmitoyltransferases): active towards long chainsMitochondrial beta-oxidation.
COTs (carnitine octanoyltransferases): active towards medium chainsPeroxisomes: mediates transport to mitochondria
CrATs (carnitine acetyltransferases): active towards short chain acyl-CoAsReversible conversion of acetyl-CoA and carnitine to acetylcarnitine and free CoA
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L-CPT I
M-CPT I
COT
CPT II
CrAT
ChAT
long
cha
in a
cyl-C
oA
short chain acyl-C
oA
medium chain acyl-CoA
malonyl-CoA regulated
malonyl-CoA insensitive
choline
carnitine
F.G. Hegardt
Acetyl transferases
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Information extracted from multiple sequence alignments
conserved
tree-determinants correlated mutations
Acetyl transferases
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SINGLEMUTATION
DECREASEDSTABILITY
"RESTORED"STABILITY SECOND COMPENSATORY
MUTATION
Correlated Mutations
Pazos et al.J. Mol. Biol., 1997
Acetyl transferases
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Information extracted from multiple sequence alignments
tree-determinants
Acetyl transferases
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Malonyl-CoA regulation: Met vs. Ser
Carnitine-Choline: Thr/Glu/Thr vs. Val/Asp/Asn
Short vs. Long substrate: Gly vs. Met
Acetyl transferases
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Predicting functional specificity
Identifying dimerisation residuesIn chemokine receptors.
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Smallmolecules:aa’s, amines, nucleosides, peptides, etc.
Pheromonesodorants
Ca2+Light
TSH,LH,FSH, IL’s, CK;s, etc
Proteins
EFFECTOR:Enzymechannels
Intracelullarmessenger
INTERNALIZATION
Arrestin
TRANSDUCTION
GCRP: Ligand binding
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NT- Ca2+ sensing receptor
CT- GABAB receptor
TM IV- B-adrenergic
N
C
C
N
The GCPR’sdimerize
GCRP: Dimerization
Inhibitor
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The two main events here are:
•Binding specificity.
•Dimerization/Oligomerization.
•Can we predict the signals and distinguish themat the sequence level?
Then, we have two aims:
• Which residues are involved in dimerisation?
GCRP: the issue
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Selected group: Chemokines
Why?: they are known to dimerize!
GCRP: the chemokines.
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RossiRossi & & ZlotnikZlotnik. . AnnuAnnu. Rev.. Rev.ImmunolImmunol. . (2000). 18:217(2000). 18:217--242.242.
CHEMOKINESCHEMOKINES
WoundWoundhealinghealing
Th1/Th2Th1/Th2developmentdevelopment
AngiogenesisAngiogenesis
TumorTumormetastasismetastasis
CellCellrecruitmentrecruitment
InflammationInflammation
OrganogenesisOrganogenesis
LymphoidLymphoidtraffickingtrafficking
RogersRogers D. D. VanderbiltVanderbilt UniversityUniversity (1950s) (1950s)
Chemokines: biological functions
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ADHESIONADHESIONCHEMOTAXISCHEMOTAXIS
POLARIZATIONPOLARIZATIONINTERNALIZATIONINTERNALIZATIONGENE EXPRESSIONGENE EXPRESSION
JAK
JAK
Gi dependent Gi independent
ThelenThelen (2001)(2001)
Mellado et al, (2001)Mellado et al, (2001)
STATSTAT
SOCSSOCS
Chemokines: signaling
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Chemokine receptors are in an equilibrium betweenseveral conformations: monomers, homodimers andheterodimers
Chemokines: conformations.
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• Existing methods to detect important residues:
GCRP: methods
HannenhalliHannenhalli & & RussellRussell. . JMB JMB (2000). 306:61(2000). 306:61--76.76.
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1.- Alignment selection.
2.- Tree determinants searching.
3.- Selecting regions.
4.- Mapping and rough model generation basedon Rhodopsin (to visually represent the results).
Steps:
TEST CASE: CHEMOKINES, known to dimerise.
Our strategy
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(http://www.gpcr.org/7M/)
• Clustering: to obtain a representative alignment containing groups:CCR1-9, CXCR3-5, and IL8A-B (total 61).
• Different levels of redundancy tested (75-100%). A redundancy level of 95% selected to compensate the number of sequences and alignment bias reduction
• Realignment using T-COFFEE with secondary structure predictions taking into account the rhodopsin model.
TEST CASE: CHEMOKINES
Alignment selection
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TREE DETERMINANTSEARCHING
•Level entropy method•Mutational behaviourmethod (MB)•Sequence SpaceAutomated Method (FASS)
Basics: Homodimerization specificity is trying to avoid promiscuous dimerisation between homologous sequences!
Dimerization-focused strategy: obtaining the best subfamily division(as many subfamily groups as possible).
Finding residues
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What is a TreeDeterminant?
Finding residues
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•What methods do we use to predict functional sites?
MB method.
S-method
PCA
Finding residues
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An example:
Sequence Space: overviewCasari, G. et al. Nat. Struct. Biol (1995). 2:171-178.
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Finding residues
{Carro et al, NAR, 2006}
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Residues obtained by Sequence-Space family division.
Tree-determinants: Clustering results
CKR1/3
IL8A/B
CKR6/11/9/7
CKR5/2
CKR8/4
CKR10
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CXCR3/5CXCR4CCR1/3/VIL8A/B
CCR6/7/9/11
CCR2/5CCR4/8CCR10
Sequence Space: Clustering results
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Region selection and then, residue selection (not necessarily the TD’s)
solvent accessibleS-methodS-method,
buried
Both S-method & FASS
Visualizing interface regions
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Bioinformatics: Conclusions
•The automated version is capable to detectthe Functional signal
•The dimerization signal still needs extensive humansupervision.
•Not all the obtained pairs were tested so, functionalsignals could very well be dimer/oligomerization ones.
•… and experimental validation of certain pairsconfirmed the predicitive power of this approach.
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L1-2 Mut-CCR5 L1-2 Wt-CCR5
CCL5-biot CCL5-biot
Fluorescence intensity
Cell
num
ber
L1-2 Mut-CCR5 L1-2 Wt-CCR5
CCR5-03 CCR5-03
Fluorescence intensity
Cell
num
ber
CCR5wt (Kd 0.87 nM)CCR5mut (Kd 1.33 nM)
CCL5 (nM)
0.01 0. 1 1 10 100 1000
20
40
60
80
100
0
% Bo
und
125 I
-CCL
5
CCR5I52V/V150A
Anti CCr5 staining
Similar CCl5-binding
CCR5I52V/V150A and CCR5 show similar membrane expression and ligand binding
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Luis Sanchez-Pulido, CNB.Fede Abascal- CNB.Manuel Gomez-CAB.Juan Carlos Sanchez-CNB.
M. Mellado, DIO-CNB.
Many thanks to: