Transcript Detecting Positively and Negatively Selected Sites in a

Prediction of
functional/structural sites in a
protein using conservation and
hyper-variation
(ConSeq, ConSurf, Selecton)
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Empirical findings:
variation among genes
“Important” proteins evolve
slower
than “unimportant” ones.
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Histone H4 protein
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Empirical findings:
variation among genes
Functional regions evolve
slower
than nonfunctional regions.
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Conservation = functional/structural
importance
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Alignment preproinsulin
Xenopus
Bos
MALWMQCLP-LVLVLLFSTPNTEALANQHL
MALWTRLRPLLALLALWPPPPARAFVNQHL
**** : * *.*: *:..* :. *:****
Xenopus
Bos
CGSHLVEALYLVCGDRGFFYYPKIKRDIEQ
CGSHLVEALYLVCGERGFFYTPKARREVEG
**************:******** :*::*
Xenopus
Bos
AQVNGPQDNELDG-MQFQPQEYQKMKRGIV
PQVG---ALELAGGPGAGGLEGPPQKRGIV
.**.
** *
*
*****
Xenopus
Bos
EQCCHSTCSLFQLENYCN
EQCCASVCSLYQLENYCN
**** *.***:*******
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Conservation based inference
 Conserved sites:



Important for the function or structure
Not allowed to mutate
“Slow evolving” sites
Low rate of evolution
 Variable



sites:
Less important (usually)
Change more easily
“Fast evolving” sites
High rate of evolution
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Detecting conservation:
Evolutionary rates
• Rate (~speed) = distance / time
• Distance = number of substitutions per site
• Time = 2*#years (doubled because the sequences
evolved independently
d
d
r
2T
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Mean Rate of Nucleotide Substitution in
Mammalian Genomes
-9
~10
Substitutions/site/year
Evolution is a very slow
process at the molecular
level (“Nothing
happens…”)
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Rate computation
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2
3
4
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Human
D
M
A
A
H
A
M
Chimp
D
E
A
A
G
G
C
Cow
D
Q
A
A
W
A
P
Fish
D
L
A
A
C
A
L
S. cerevisiae
D
D
G
A
F
A
A
S. pombe
D
D
G
A
L
G
E
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http://conseq.tau.ac.il
Site-specific rate computation method
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Using the ConSeq server
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ConSeq results:
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Crash course in protein structure
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Why protein structure?
 Each protein has a particular 3D structure that determines
its function
 Protein structure is better conserved than protein sequence
and more closely related to function
 Analyzing a protein structure is
more informative than analyzing its
sequence for function inference
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PDB: Protein Data Bank
http://www.rcsb.org

Holds 3D models of biological macromolecules
(protein, RNA, DNA, small molecules)

All data are available to the public

X-Ray crystals (84%) NMR models (16%)

Submitted by biologists and biochemists from
around the world.
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PDB model

Defines the 3D coordinates (x,y,z) of each of the atoms
in one or more molecules (i.e., complex)

There are models of proteins, protein complexes,
proteins and DNA, protein segments, etc …

The models also include the positions of ligand
molecules, solvent molecules, metal ions, etc…

PDB code: integer + 3 integers/characters (e.g., 1a14)
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The PDB file – text format
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The PDB file – text format
Residue
identity
Atom
identity
Atom
number
chain
The coordinates
for each residue in
the structure
Residue
number
X
Y
Z
Temperature
factor
ATOM:
Usually protein
or DNA
HETATM:
Usually Ligand,
ion, water
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Viewing structures
Wireframe
Spacefill
Backbone
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Conservation in the structure
Protein core: structurally constrained - usually conserved
Active site: functionally constrained - usually conserved
Surface loops: tolerant to mutations - usually variable
Surface loops
Active site
Hydrophobic core
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http://consurf.tau.ac.il
Same algorithm as ConSeq, but here the results
are projected onto the 3D structure of the protein
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Using the ConSurf server
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ConSurf example:
potassium channel
 An
integral membrane protein with sequence
similarity to all known K+ channels, particularly
in the pore region.
 PDB
code: 1bl8, chain A
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ConSurf results:
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ConSurf example:
potassium channel
 Alignment of
homologs found by psi-blast:
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ConSurf results:
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ConSurf example:
potassium channel
 Neighbor-Joining reconstructed phylogenetic
tree:
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ConSurf results:
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Conservation scores:

The scores are standardized: the average score for all
residues is zero, and the standard deviation is one
 The lowest score represents the most conserved site
in the protein


negative values: slowly evolving (= low evolutionary rate),
conserved sites
The highest score represents the most variable site in
the protein

positive values: rapidly evolving (= fast evolutionary rate),
variable sites
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ConSurf results: amino-acid
conservation scores
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ConSurf result with First Glance in
Jmol:
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ConSeq/ConSurf user intervention
(advanced options)
1.
2.
3.
4.
5.
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7.
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10.
Method of calculating the amino acid conservation scores:
Bayesian/Max Likelihood
Enter your own MSA file
Multiply Align Sequences using: MUSCLE/CLUSTALW
Collect the Homologues from: SWISS-PROT/UniProt
Max. Number of Homologues (default = 50)
No. of PSI-BLAST Iterations (default = 1)
PSI-BLAST E-value Cutoff (default = 0.001)
Model of substitution for proteins:
JTT/Dayhoff/mtREV/cpREV/WAG
Enter your own PDB file
Enter your own TREE file
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Codon-level selection
 ConSeq/ConSurf:

Compute the evolutionary rate of amino-acid
sites → the data are amino acids.
 But,
codons encode amino acids…
 61 codons vs. 20 amino acids !
 Aren’t we loosing information ???
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Darwin – the theory of
natural selection
 Adaptive
evolution:
Favorable traits will become more
frequent in the population
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M. Kimura – the neutral theory
of molecular evolution
Most of the DNA variation between
species is neutral with regards to the
phenotype
Selection operates to preserve a trait
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Synonymous (silent) and nonsynonymous (non-silent) substitutions
Silent
Non-silent…
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Synonymous vs. nonsynonymous substitutions
synonymous substitutions = silent
substitutions
non-synonymous substitutions = non-silent
or amino-acid altering substitutions
UUU → UUC (Phe → Phe ): synonymous
UUU → CUU (Phe → Leu): non-synonymous
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Synonymous vs. non-synonymous substitutions
For most proteins, it is observed that the
rate of synonymous substitutions is much
Higher
than the non-synonymous rate
This is called purifying
selection
(= conservation
this is what ConSeq/Surf are computing
)
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Synonymous vs. non-synonymous substitutions
Structural proteins
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Saturation of synonymous substitutions
Histone H4 between human and wheat: saturation of
synonymous substitutions
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Synonymous vs. nonsynonymous substitutions
There are rare cases where the nonsynonymous rate is much larger than the
synonymous rate.
This is called Positive
selection
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Positive Selection
The hypothesis:
Promotes the fitness of the organism
Examples:
 Proteins of the immune system
 Pathogen proteins evading the host immune
system
 Pathogen proteins that are drug targets
 Proteins that are products of gene duplication
 Proteins involved in the reproduction system
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Computing synonymous and nonsynonymous rates
• Codon-based MSA: translate DNA to amino acids, align,
backtrack to the DNA but keep alignment
• Phylogenetic tree: 5 replacements in 10 positions between
human and chimp is a lot, but between human and
cucumber is nothing
• Different replacement probabilities between two amino
acids:
LysArg ≠ LysCys
Positive evolution occurs at only a few sites !
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Inferring positive selection
Divide the rate of non-silent substitutions (Ka)
by the rate of silent substitutions (Ks)
ka
ks
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Inferring positive selection
Basic assumptions:
Selection score (Ka/Ks) > 1
↓
positive selection
Selection score (Ka/Ks) < 1
↓
purifying selection
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Not so fast !!!
 Our
computational model assumes
there is positive selection in the data
 There is a good chance our model
will find a few positively selected
sites whatever the case
 Is this really indicative of positive
selection or plain randomness?
So, maybe there’s no positive selection after all
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Statistics helps us to compare
between hypotheses
H0: There’s no positive selection
 H1: There is positive selection




H0: compute the probability (likelihood) of
the data using a model that does not
account for positive selection
H1: compute the probability (likelihood) of
the data using a model that does account for
positive selection
Perform a likelihood ratio test (LRT)
L( Data | M ( H 1))
2
2  ln(
)~
L( Data | M ( H 0))
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http://selecton.tau.ac.il
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Using the selecton server
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Input = a coding sequence
at the codon level



The user must provide the sequences – no psi-blast option
The sequences’ lengths must divide by 3 (ORF) and must not
include any stop-codons
An alignment should be a codon alignment RevTrans
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Similar to ConSurf
optional
Nuclear/mitochondria
different species
Default run:
M8(H1) and the M8a(H0)
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Selecton Example: HIV Protease
The Protease is an
essential enzyme
for viral infectivity
PDB ID: 1hxw
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Selecton Results:
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Selecton Results:
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Selecton results:
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Selection scores (Ka/Ks):
 The
scores are normalized
 Ka/Ks > 1: positive selected site
 Ka/Ks <1: purified selected site
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Coloring scheme:
 Used
for visualization is based on the
continuous Ka/Ks scores.
 The color grades (1-7):


1 for positive selected sites (blue)
7 for purified selected sites (bordeaux)
Color coding scheme of Selecton
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