1 Prediction of functional/structural sites in a protein using conservation and hyper-variation (ConSeq, ConSurf, Selecton)

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Prediction of Prediction of functional/structural sites in functional/structural sites in a protein using conservation a protein using conservation

and hyper-variation and hyper-variation (ConSeq, ConSurf, Selecton)(ConSeq, ConSurf, Selecton)

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Empirical findings:Empirical findings:variation among genesvariation among genes

““ImportantImportant”” proteins evolveproteins evolve

slowerslowerthan “unimportantunimportant” onesones.

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Histone H4 proteinHistone H4 protein

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Empirical findings:Empirical findings:variation among genesvariation among genes

Functional Functional regionsregions evolveevolve

slowerslowerthanthan nonfunctional nonfunctional regions.regions.

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Conservation = functional/structural Conservation = functional/structural importanceimportance

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Xenopus MALWMQCLP-LVLVLLFSTPNTEALANQHLBos MALWTRLRPLLALLALWPPPPARAFVNQHL **** : * *.*: *:..* :. *:****

Xenopus CGSHLVEALYLVCGDRGFFYYPKIKRDIEQBos CGSHLVEALYLVCGERGFFYTPKARREVEG **************:******** :*::*

Xenopus AQVNGPQDNELDG-MQFQPQEYQKMKRGIVBos PQVG---ALELAGGPGAGGLEGPPQKRGIV .**. ** * * *****

Xenopus EQCCHSTCSLFQLENYCNBos EQCCASVCSLYQLENYCN **** *.***:*******

Alignment preproinsulin

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88

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Conserved sites: Important for the function or structureImportant for the function or structure Not allowed to mutateNot 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

Conservation based inferenceConservation based inference

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Detecting conservation: Detecting conservation: Evolutionary rates

d T

dr

2

• Rate (~speed) = distance / time• Distance = number of substitutions per site • Time = 2*#years (doubled because the sequences evolved independently

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Mean Rate of Nucleotide Substitution in Mammalian Genomes

Evolution is a very Evolution is a very slowslow process at the molecular process at the molecular level level (“Nothing (“Nothing happens…”)happens…”)

~10-9

Substitutions/site/year

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Rate computationRate computation11223344556677

HumanHumanDDMMAAAAHHAAMM

ChimpChimpDDEEAAAAGGGGCC

CowCowDDQQAAAAWWAAPP

FishFishDDLLAAAACCAALL

S. cerevisiaeS. cerevisiaeDDDDGGAAFFAAAA

S. pombeS. pombeDDDDGGAALLGGEE

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http://http://conseqconseq.tau.ac.il.tau.ac.ilSite-specific rate computation methodSite-specific rate computation method

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Using the ConSeq serverUsing the ConSeq server

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ConSeq results:ConSeq results:

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Crash course in protein structureCrash course in protein structure

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Why protein structure?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 BankPDB: Protein Data Bankhttp://www.rcsb.orghttp://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 modelPDB model

Defines the 3D coordinates (x,y,z) of each of the atoms Defines the 3D coordinates (x,y,z) of each of the atoms in one in one or moreor more molecules (i.e., complex) molecules (i.e., complex)

There are models of proteins, protein complexes, There are models of proteins, protein complexes, proteins and DNA, protein segments, etc …proteins and DNA, protein segments, etc …

The models also include the positions of ligand The models also include the positions of ligand molecules, solvent molecules, metal ions, etc…molecules, solvent molecules, metal ions, etc…

PDB code: integer + 3 integers/characters (e.g., 1a14) PDB code: integer + 3 integers/characters (e.g., 1a14)

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The PDB file – text formatThe PDB file – text format

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The PDB file – textThe PDB file – text formatformat

ATOM:

Usually protein or DNA

HETATM:

Usually Ligand, ion, water

chain

Residue identity

Residue number

Atom number

Atom identity

The coordinates for each residue in the structure Temperature

factorX Y Z

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Viewing structuresViewing structuresWireframe Spacefill

Backbone

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Protein core: structurally constrained - usually conserved

Active site: functionally constrained - usually conserved

Surface loops: tolerant to mutations - usually variable

Hydrophobic core

Surface loops

Conservation in the structureConservation in the structure

Active site

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http://http://consurfconsurf.tau.ac.il.tau.ac.ilSame algorithm as ConSeq, but here the resultsSame algorithm as ConSeq, but here the results are projected onto the 3D structure of the proteinare projected onto the 3D structure of the protein

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Using the ConSurf serverUsing the ConSurf server

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ConSurf example: ConSurf example: potassium channel potassium channel

An integral membrane protein with sequence An integral membrane protein with sequence similarity to all known K+ channels, particularly similarity to all known K+ channels, particularly in the pore region. in the pore region.

PDB code: 1bl8, chain A PDB code: 1bl8, chain A

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ConSurf ConSurf resultsresults::

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Alignment of homologs found by psi-blast:Alignment of homologs found by psi-blast:


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Neighbor-Joining reconstructed phylogenetic tree:Neighbor-Joining reconstructed phylogenetic tree:

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Conservation scoresConservation scores::

The scores are standardized: the average score for all The scores are standardized: the average score for all residues is zero, and the standard deviation is one residues is zero, and the standard deviation is one

The lowest score represents the most conserved site The lowest score represents the most conserved site in the protein in the protein negative values: slowly evolving (= low evolutionary rate), negative values: slowly evolving (= low evolutionary rate),

conserved sitesconserved sites The highest score represents the most variable site in The highest score represents the most variable site in

the proteinthe protein positive values: rapidly evolving (= fast evolutionary rate), positive values: rapidly evolving (= fast evolutionary rate),

variable sitesvariable sites

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ConSurf results: amino-acid ConSurf results: amino-acid conservation scoresconservation scores

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ConSurf result with First Glance in ConSurf result with First Glance in Jmol:Jmol:

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ConSeqConSeq//ConSurfConSurf user intervention user intervention(advanced options)(advanced options)

1.1. Method of calculating the amino acid conservation scores: Method of calculating the amino acid conservation scores: BayesianBayesian/Max Likelihood/Max Likelihood

2.2. Enter your own MSA fileEnter your own MSA file3.3. Multiply Align Sequences using: Multiply Align Sequences using: MUSCLEMUSCLE/CLUSTALW/CLUSTALW4.4. Collect the Homologues from: Collect the Homologues from: SWISS-PROTSWISS-PROT/UniProt/UniProt5.5. Max. Number of Homologues (default = 50)Max. Number of Homologues (default = 50)6.6. No. of PSI-BLAST Iterations (default = 1)No. of PSI-BLAST Iterations (default = 1)7.7. PSI-BLAST E-value Cutoff (default = 0.001PSI-BLAST E-value Cutoff (default = 0.001))8.8. Model of substitution for proteins: Model of substitution for proteins:

JTTJTT/Dayhoff/mtREV/cpREV/WAG/Dayhoff/mtREV/cpREV/WAG9.9. Enter your own PDB fileEnter your own PDB file10.10. Enter your own TREE fileEnter your own TREE file

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Codon-level selectionCodon-level selection

ConSeq/ConSurf:ConSeq/ConSurf: Compute the evolutionary rate of amino-acid Compute the evolutionary rate of amino-acid

sites → the data are amino acids.sites → the data are amino acids. But, codons encode amino acids…But, codons encode amino acids… 61 codons vs. 20 amino acids !61 codons vs. 20 amino acids ! Aren’t we loosing information ???Aren’t we loosing information ???

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Darwin – the theory of Darwin – the theory of natural selectionnatural selection

Adaptive evolutionAdaptive evolution::

Favorable traits will become more Favorable traits will become more frequent in the populationfrequent in the population

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M. Kimura – the neutral theory M. Kimura – the neutral theory of molecular evolutionof molecular evolution

Most of the DNA variation betweenMost of the DNA variation betweenspecies is neutral with regards to the species is neutral with regards to the phenotypephenotype

Selection operates to Selection operates to preservepreserve a trait a trait

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Synonymous (silent) and non-synonymous (non-silent) substitutions

Silent Non-silent…

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Synonymous vs. nonsynonymous substitutions

UUU → UUC (Phe → Phe ): synonymous

UUU → CUU (Phe → Leu): non-synonymous

synonymous substitutions = silent substitutions

non-synonymous substitutions = non-silent or amino-acid altering substitutions

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For For mostmost proteins, it is observed that the proteins, it is observed that the rate of rate of synonymoussynonymous substitutions is much substitutions is much

HigherHigherthan the than the non-synonymousnon-synonymous rate rate

This is called purifying selectionpurifying selection (= conservation (= conservation this is what ConSeq/Surf are computingthis is what ConSeq/Surf are computing))

Synonymous vs. non-synonymous substitutions

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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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There are rare cases where the non-synonymous rate is much larger than the synonymous rate.

This is called Positive selectionPositive selection

Synonymous vs. nonsynonymous substitutions

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Examples:Examples: Proteins of the immune systemProteins of the immune system Pathogen proteins evading the host immune Pathogen proteins evading the host immune

systemsystem Pathogen proteins that are drug targetsPathogen proteins that are drug targets Proteins that are products of gene duplicationProteins that are products of gene duplication Proteins involved in the reproduction systemProteins involved in the reproduction system

Positive Selection

The hypothesis:The hypothesis:

Promotes the fitness of the organism Promotes the fitness of the organism

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Computing synonymous and non-synonymous 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 5 replacements in 10 positions between human and chimp is a lot, but between human and human and chimp is a lot, but between human and cucumber is nothing cucumber is nothing

• Different replacement probabilities between two amino acids:

LysLysArg Arg ≠ ≠ LysLysCysCys

Positive evolution occurs at only a few sitesPositive evolution occurs at only a few sites! !

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Inferring positive selectionInferring positive selection

Divide the rate of non-silent Divide the rate of non-silent substitutions (substitutions (KKaa))

by the rate of silent substitutions (by the rate of silent substitutions (KKss))

s

ak

k

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Inferring positive selectionInferring positive selection

Basic assumptions:Basic assumptions:

Selection score Selection score ((Ka/KsKa/Ks) > 1) > 1

↓↓

positive selectionpositive selection

Selection score Selection score ((Ka/KsKa/Ks) < 1) < 1

↓↓

purifying selectionpurifying selection

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Not so fastNot so fast!!! !!!

Our computational model assumes Our computational model assumes there is positive selection in the datathere is positive selection in the data

There is a good chance our model There is a good chance our model will find a few positively selected will find a few positively selected sites whatever the case sites whatever the case

Is this really indicative of positive Is this really indicative of positive selection or plain randomness?selection or plain randomness?

So, maybe there’s no positive selection after all So, maybe there’s no positive selection after all

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Statistics helps us to compare Statistics helps us to compare between hypothesesbetween hypotheses

HH00: There’s no positive selection: There’s no positive selection

HH11: There is positive selection: There is positive selection

2~)))0(|(

))1(|(ln(2

HMDataL

HMDataL

HH00: compute the probability: compute the probability (likelihood) (likelihood) of the data of the data

using a model that using a model that does does not not account for positive account for positive selectionselection

HH11: compute the probability: compute the probability (likelihood) (likelihood) of the data of the data

using a model that using a model that does account for positive selectiondoes account for positive selection Perform a Perform a likelihood ratio test likelihood ratio test (LRT)(LRT)

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http://selecton.tau.ac.il

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Using the selecton serverUsing the selecton server

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Input = a coding sequence at the codon level

The user must provide the sequences – no psi-blast optionThe user must provide the sequences – no psi-blast option The sequences’ lengths must divide by 3 (ORF) and must The sequences’ lengths must divide by 3 (ORF) and must not not

include any stop-codonsinclude any stop-codons An alignment should be a An alignment should be a codon alignmentcodon alignment RevTransRevTrans

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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 ProteaseSelecton Example: HIV Protease

The Protease is The Protease is an essential an essential

enzymeenzyme for viral for viral

infectivityinfectivity

PDB ID: 1hxwPDB ID: 1hxw

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Selecton ResultsSelecton Results::

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Selecton ResultsSelecton Results::

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Selecton resultsSelecton results::

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Selection scores (Selection scores (Ka/KsKa/Ks):):

The scores are normalized The scores are normalized Ka/Ks Ka/Ks > 1: positive selected site > 1: positive selected site Ka/KsKa/Ks <1: purified selected site <1: purified selected site

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Coloring schemeColoring scheme::

Used for visualization is based on the Used for visualization is based on the continuous continuous Ka/KsKa/Ks scores. scores.

The color grades (1-7):The color grades (1-7): 1 for positive selected sites (blue)1 for positive selected sites (blue) 7 for purified selected sites (bordeaux)7 for purified selected sites (bordeaux)

Color coding scheme of Selecton

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Documents

1 Prediction of functional/structural sites in a protein using conservation and hyper-variation (ConSeq, ConSurf, Selecton)