EVOLUTION OF GLOBINS

Evolution of Globins

Evolution of visual pigments and related molecules

Evolution of gene clusters

• Many genes occur as multigene families (e.g., actin, tubulin, globins, Hox)– Inference is that they evolved from a common

ancestor– Families can be

• clustered - nearby on chromosomes (α-globins, HoxA)

• Dispersed – on various chromosomes (actin, tubulin)

• Both – related clusters on different chromosomes (α,β-globins, HoxA,B,C,D)

– Members of clusters may show stage ortissue-specific expression

• Implies means for coregulation as wellas individual regulation

Evolution of gene clusters• multigene families (contd)

– Gene number tends to increase withevolutionary complexity

• Globin genes increase in number from

primitive fish to humans

– Clusters evolve by duplication and divergence

• History of gene families can be traced by comparing sequences

– Molecular clock model holds that rate of change within a group is relatively constant

• Not totally accurate – check rat genome sequence paper

– Distance between related sequences combined with clock leads to inference about when duplication took place

Classic phylogenetic studies of sequenceconservation: the globinsThe globins are the best studied family in terms of sequence conservation, partly because they were one of the first families for which multiple members were sequenced, and partly because some of the earliest protein structures (in fact, the earliest) solved were globins. The classic papers of Perutz, Kendrew and Watson were the first to correlate sequence conservation with aspects of protein structure and function. They drew their conclusion based on only a few aligned sequences. Later globin studies, such as that of Bashford, Chothia and Lesk, expanded the analyses of globin sequence conservation to include hundreds of sequences.

Perutz, Kendrew & Watson J Mol Biol 13, 669 (1965)

Bashford, Chothia & Lesk J Mol Biol 196, 199 (1987)

Scapharca inaequivalvisoxygenated hemoglobin

Conservation of functional residues

Phe 43

His 87

heme

There were only 2 perfectly conserved residues among the 8 known globin structures at the time of the Bashford et al study. These are residues critical in binding of heme and/or interaction w/heme-bound oxygen. It will often be found that the best conserved residues in related proteins are those involved in critical aspects of the general function.

Residues involved in more specific aspects of function may or may not be conserved, depending upon the relationship between the proteins under consideration. For example, residues involved in substrate specificity for serine proteases may be conserved among orthologs, such as the chymotrypsins, but not between paralogs, such as chymotrypsins and trypsins.

yellow = small neutral/polargreen = hydrophobicred/pink = polar/acidicblue = basic

buriedhuman hemoglobinbeta chain

Conservation at buried positions

• core residues, which are usually hydrophobic, often tolerate conservative substitutions, i.e. to other hydrophobics• overall core volume is well-conserved (Lim & Ptitsyn, 1970) though individual core positions tolerate variation in volume• this reflects what we know about packing and the effects of core mutations on stability--thus sequence conservation is partly related to maintaining a stable structure

Y140

H156

portion of alignment of prokaryotic and eukaryotic globins

yellow = small neutral/polargreen = hydrophobicred/pink = polar/acidicblue = basic

human hemoglobinbeta chain

Y140

H156

Conservation at solvent-exposed positions

• solvent-exposed (surface) positions are mutable and usually toleratemutation to many residue types including hydrophobics. Bashford et al.,however, noted that for globins at least, some surface positions do nottolerate large hydrophobics. Since polar-to-hydrophobic mutations on proteinsurfaces do not reduce stability, this conservation could reflect constraintson solubility. Indeed, it is clear that the overall polar character of the surface is conserved for soluble, globular proteins, even though a certainnumber of hydrophobics may be tolerated.

examplesof surfaceresidues

Conservation of loops and turns

• “Spacer” regions between secondary structures, such as loops and turns, are often hypermutable and vary not only in sequence but in length, tolerating insertion and deletion events (Insertions and deletions are much less often found within secondary structure elements. Why?)

part of alignment of animal hemoglobin and chainshumanchain

Are the and chains related to each other by paralogy or orthology?

Sequence identity and homology: poor coverage

the two proteins have the same fold,both bind heme and oxygen in same place: good independent structural/functional evidence for homology...

Yet alignments of their sequences reveal only 24% identity. There are also many examples of related globins and other proteins with much lower identity than this.

1MBO and 1HBBhemoglobin and myoglobin

Any reasonable sequence identity criterion, whether it is a flat percent cutoff or a length-dependent cutoff, will give incomplete coverage--in other words, it will fail to identify many distant but true relationships.

Evolutionary analysis: one step into the a priori prediction

Consensus: AAT GGC TCT TTT GAA AAA ...

N G F F N K .

Seq2: AAC GGA TGT TTC GAG AAA...

N G C F E K .

Synonymous

Non-synonymous

Nu

mb

er

of

ind

ivid

ua

ls

Number of mutations

Neutrally fixed

Purifying selection

Positive selection

AAT GGC TGT TTT GAA AAA ...

N G C F N K .

E

Seq1

Seq2

Seq3

Seq4

Seq5Seq6

Seq7

Seq8

Seq9

Seq10

Seq11

Consensus

Amino acid replacements

Non-synonymous nucleotide substitution

Protein function or structure

changes

Neutral evolution vs selection

Biological fitness (W)

Amino acid changes

Neutrality

Purifying selection

Positive selection

Neutral Theory of molecular evolution

Measuring the strength of selection

)()(

S

N

dSynonymousdsynonymousNon n

NdN

sS

dS

= 1 < 1 > 1

Neutrality

Purifying selectionPositive selection

Two ways of testing the functional importance of peptide regions

Experimental (Functional Biologists) Predictive (Evolutionary Biologists)

Evolutionary and structural analysis

Serial deletions and random directed

mutagenesis

Consensus: AAT GGC TCT TTT GAA AAA ...

N G F F N K .

Seq2: AAC GGA TGT TTC GAG AAA...

N G C F E K .

Methods to detect adaptive evolution using DNA divergence data

Maximum-likelihoodmodels

Models to detect adaptive evolution at single codon sites

Models to detect adaptive evolution at specific lineages of

the tree

Kimura-based modelsMultiple alignment

Sq1: ...ATGGGCGTC...

Sq2: ...ATGGACGTA...

Sq3: ...ATGGGAGAG...

Sq4: ...ATGAGCGTC...

Sq1

Sq2

Sq4

Sq3

a

b

Tree

Parsimony method to detectSelection at single sites

Sliding-window basedMethods

Sq1: ...ATGGGCGTC...

Sq2: ...ATGGACGTA...

Sq3: ...ATGGGAGAG...

Sq4: ...ATGAGCGTC...

4

Sq1

Sq2

Sq4

Sq3

a

b

Tree

1

2

5

6

...ATGGGCGTC...

...ATGGACGTA...

...ATGGGAGAG...

...ATGAGCGTC...

Sq1

Sq2

Sq4

Sq3

a

b

Tree

1

2

5

6

A B

A1

A2

B1

B2

A3 A4B3

Different levels of protein’s function and evolution

Intra-molecular co-evolution

Tully and Fares (2006) Evol. Bioinf.

Inter-protein/gene co-evolution

Co-evolution/interaction between two different biological systems

Covariation analysis

Substitution patterns at different positions in a sequence alignment are not necessarily independent. This is sometimes referred to as covariation or correlated evolution.

name sequenceA YADLGRIKSB YSDLGSEKEC IDDFGEIAAD IDDFGVIGT

For example, in the mini multiple alignment shown at left, the identity of the residue at the 4th position is correlated to the identity of the residue at the 1st position.

A statistical perturbation analysis can be used to characterize this covariation. An alignment of related sequences is “perturbed” by only considering sequences at which, for example, the first position is Y. The effect of this perturbation on the residue distribution observed at other positions is then measured. If the distribution changes significantly, covariation between sequence changes at the first site and other sites in the alignment is inferred.

The hydrophobic core residues in related proteins tend to be covariant due to constraints on core packing. One sees compensatory volume changes at different positions.

Davidson and coworkers found that for 266 aligned SH3 domain sequences, the strongest covariation was observed for a cluster of central hydrophobic residues.

For example, substitution of a smaller residue (Ala->Gly) at 39 was strongly correlated to substitution of a larger residue (Ile->Phe) at 50.

Hydrophobic core of SH3domains, with most frequentlycovarying residues shown in yellow

Covariation and hydrophobic core packing

S.M. Larson, A.A. DiNardo and A.R. Davidson, J Mol Biol 303, 433 (2000)

Some recent studies (Suel et al) have suggested a connection between covarying clusters of residues and transduction of signals between distant sites in proteins.

For example, G-protein coupled receptors bind a ligand on one side of a membrane, and then transduce that signal to the other side through conformational change. Suel et al showed thatthe main clusters of covarying residues tended to connect the ligand and G-protein binding sites.

ligand

G-protein binding sites

membrane

covaryingnetworks(brown)

Suel et al. Nat Struct Biol 2003

A novel method to detect co-evolution in protein-coding genes

(Fares and Travers, Genetics 2006)

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

ij

ekijektxB

1

ijekijektxB

1

T

SSekA

T 1

1

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

T

SSekB

T 1

1

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

2ˆBijekekD

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

2

1

1

T

SASekA T

D 2

1

1

T

SBSekB T

D

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

AAMWCGPCPNDEE

CAMCCGMCMNDEE

CAMDCGACANDEE

AAMMCGCCCNDEE

T

S

T

SBSekASek

T

SBSekASek

AB

DDDD

DDDD

1 1

22

1

ˆˆ

ˆˆ

Testing the significance of the correlation coefficient

i

ii

Z

RP

95.0

,1000

1)95.0(

1000

1

2ˆAijekekD

Sequence alignment

3D

> 75%

> 75%

Clade 1

Clade 2

Tree

Molecular co-evolution analyses: CAPS (Fares and McNally, Bioinformatics 2006)

Collate results from ‘re-sampling’ and ‘real’ data and sort by

1 = 0.12 = 0.153 = 0.35...i = 0.40i+1 = 0.55..N-1 = 0.98N = 0.99

Re-sampling1 = 0.552 = 0.98

Real

Calculate probabilities of R-values applying the step-down permutational

correction

N

iP

155.0

Identify groups of co-evolving pairs with P > 0.95

Flow of information

in CAPS

Comparative analysis of sensitivities

MICKDependency

CAPSlnLCorr

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100

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0

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

SE

NS

ITIV

ITY

DISTANCE

TR

UE

PO

SIT

IVE

S

DistanceCAPSMICKDEPENDENCYlnLCorr

0102030405060708090

100

0.1 0.2 0.5 1

Mea

n S

ensi

tivi

tyDivergence

CAPS

MICK

Dep.

LnLCorr

0102030405060708090

100

10 20 30

Number of Sequences

Mea

n S

ensi

tivi

ty

n. sequence

CAPS

MICK

Dep.

LnLCorr

Three-dimensional spheres to detect protein-protein interfaces

Co-evolving amino acid sites

Highly conserved sites at overlapping areas

Spheres of 4Å radius

Co-evolving Amino acids share properties of hydrophobicity and molecular weight

Protein-protein interfaces could be predicted with greater accuracy