Protein Evolution: Structure, Function, and Human Health

Protein  Evolu-on  

Structure,  Func-on,  and  Human  Health  

11/28/2013  Dr.  Daniel  Gaston,  Department  

of  Pathology  1  

So,  about  this  evolu-on  thing?  

Why  should  I  care?  What  use  is  it?  

Lots  of  reasons  

•  Knowledge  for  its  own  sake  is  good  – Otherwise,  why  do  science  at  all?  

Lots  of  reasons  

•  Knowledge  for  its  own  sake  is  good  – Otherwise,  why  do  science  at  all?  

•  Shapes  our  understanding  of  ecology  and  biological  diversity  

Lots  of  reasons  •  Knowledge  for  its  own  sake  is  good  

– Otherwise,  why  do  science  at  all?  •  Shapes  our  understanding  of  ecology  and  biological  diversity  

•  Prac-cal  reasons  – An-bio-c  resistance  – Microbiome:  Fecal  transplanta-on  –  Cancer  –  Predic-ng  gene/protein  func-on  –  Predic-ng  the  impact  of  muta-ons  for  poten-al  to  cause  human  disease  (Genotype:Phenotype)  

Evolu-on  of  Life  on  Earth  

A  (Very)  Brief  Overview  

Eubacteria"

ROOT Iwabe et al. 1989 Gogarten et al. 1989

Eukaryota"

Archaebacteria"

Eubacteria"

ROOT Iwabe et al. 1989 Gogarten et al. 1989

Eukaryota"

Archaebacteria"

Eubacteria"

ROOT Iwabe et al. 1989 Gogarten et al. 1989

Eukaryota"

Archaebacteria"

You  are  here  

A  Brief  History  of  Cells  and  Molecules  

•  Origin of the earth ~4.5 billion years ago •  Origin of life: ~3.0-4.0 billion years ago

–  Origin of self-replicating entities –  The RNA world (?) –  Origin of the first genes, proteins & membranes –  Gave rise to the first cells –  the Last Universal Common Ancestor (LUCA) of all cells

–  Probably had 500-1000 genes •  First microfossils of bacteria: ~3.5 billion years ago (controversial)

~2.7 billion years ago (for certain) •  Oxygenation of the atmosphere: 2.3-2.4 billion years ago (by

photosynthetic bacteria) •  Origin of eukaryotes: ~1.0-2.2 billion years ago (probably 1.5) •  Origin of animals: ~0.6-1.0 billion years ago

•  Homology = descent from a common ancestor – homology is all or nothing: sequences are either

homologous (related) or not homologous (not related)

– Not the same as “similarity” (degrees of similarity are possible)

Some  Defini-ons  

Some  Defini-ons  •  Divergence = change in two sequences over time

(after splitting from a common ancestor)

•  Convergence = similarity due to independent evolutionary events

–  On the amino acid sequence level, it is relatively rare & difficult to prove (but see an example later)

T T

Ancestral sequence

Sequence 1 Sequence 2

How does evolutionary change happen in proteins?

Evolu-on:  Two  Groups  of  Processes  

•  Muta-on  – Many  different  processes  that  generate  muta-ons  – Muta-ons  are  the  raw  materials  needed  for  evolu-on  to  happen  

•  Selec-on  and  DriY  – Muta-ons  happen  in  individuals  – Evolu-on  happens  in  popula-ons  of  organisms  – Selec-on  and  Gene-c  DriY  affect  the  frequency  of  muta-ons  in  a  popula-on  over  -me  

Muta-ons  

Point  Muta-ons

! ! AGGTTCCAATTAA!! ! TCCAAGGTCAATT!

!!AGGTTCCAATTAA ! TCCAAGGTTAATT!!

REPLICATION (meiotic or mitotic division)

Unrepaired mispaired base

Mutant allele Wild-type alleles

Mutant Gamete (for multicellular org.)

Wild-type Gamete (for multicellular org.)

AGGTTCCAGTTAA ! TCCAAGGTCAATT!

AGTCCAAGGCCTTAA -------------> AGTTCAAGGCCTTAA point mutation ���

CCTTA AGTCCAAGGCCTTAA -------------> AGTCCAAGGCCTTACCTTAA

insertion

AAGG AGTCCAAGGCCTTAA -------------> AGTCC-CCTTAA

deletion AGTCCAAGGCCTTAA -------------> AGTCCCCTTCCTTAA

` inversion AGTCCAAGGCCTTAA -------------> AGTCCAAGGCC + translocation + GGTCCTGGAATTCAG GGTCCTGGAATTCAGTTAA AGTCCAAGGCC --------------> AGTCCAAGGCCAGTCCAAGGCC duplication AAGG AGTCCAAGGCCTTAA ---------------> AGTCCAAAGGCTTAA

recombination AGGC

Larger  Scale  Muta-ons  

Exon  shuffling  and  Protein  Domains  

Exon1   Exon  2   Exon  3  

Exon  shuffling  and  Protein  Domains  

Exon1   Exon  2   Exon  3  

Domain  1   Domain  2  

Exon  shuffling  and  Protein  Domains  

Exon1  Exon  2   Exon  3  

Exon  shuffling  and  Protein  Domains  

Exon1  Exon  2   Exon  3  

Domain  2  Domain  A  

Genomic  Scale  Muta-ons  

Gene  1   Gene  2  

Genomic  Scale  Muta-ons  

Gene  1   Gene  2  

Gene  Duplica-on  

Gene  1   Gene  2  

Gene  Duplica-on  

Gene  1   Gene  2  Gene  1a  

Gene-c  DriY  and  Selec-on  

Mutations vs. substitutions

•  Mutations happen in individual organisms

•  A nucleotide ‘substitution’ occurs IF after many generations, all individuals in the population harbour the ‘mutation’

•  This process is called “fixation of mutations”

•  substitution = fixed mutation •  When comparing homologous protein sequences between

species, looking at amino acid substitutions

Fixation of alleles

N generations

Proportion of = 1.0 (100%) This is the same as saying that was fixed in the population in N generations The ‘mutation’ became a ‘substitution’ after it was fixed in the population

Population with two alleles:

Proportion of = 1/14 (7.1%) Proportion of = 13/14 (93%)

Natural selection and Neutral drift •  Positive selection

–  Mutation confers fitness advantage (more offspring that survive)

–  RARE •  Purifying selection (negative selection)

–  Mutation confers fitness disadvantage (less offspring or ‘no’ viable offspring - e.g. lethal)

–  FREQUENT •  Neutral evolution (genetic drift)

–  Mutation has very little fitness effect –  Will drift in frequency in the population due to random

sampling effects –  VERY FREQUENT

Nearly-neutral theory ���

Common  Examples  of  Posi-ve  Selec-on  

•  MHC  Genes  – Diversity  =  Good  – Very  polymorphic  in  humans  

•  Envelope  (gp120)  of  HIV  –  Immune  system  evasion  

•  Enzymes  involved  in  human  dietary  metabolism  – Accelerated  posi-ve  selec-on  over  last  ~10,000  years  

Gene-c  DriY  

Select  a  marble  randomly  from  a  jar  and  “copy”  it  in  to  the  next  Fixa-on  of  the  plain  blue  allele  in  5  genera-ons  

Polymorphism  

•  Polymorphisms  are  sites  with  more  than  one  allele  present  in  a  popula-on  – Muta-ons  that  have  not  yet  been  fixed  

Muta-on  and  Codons  

Not  all  muta-ons  are  created  equal  

Point mutations in protein genes are classified according to the genetic code:

The genetic code is degenerate: more than one codon often specifies a single amino acid. E.g. Serine has 6 codons, Tyrosine has 2 codons and Tryptophan has one codon!

Point mutations in ���protein-coding genes

•  synonymous (silent) substitutions: cause interchange between two codons that code for the same amino acid:

e.g. CTG --> CTA = Leu --> Leu Mostly invisible to selection

•  non-synonymous (replacement) mutations: cause change between codons that code for different amino acids (missense) or stop codons (nonsense)

e.g. CTG --> ATG = Leu --> Met TGG --> TGA = Trp --> Stop

8 kinds of 1st codon-position synonymous mutation: R-->R and L-->L

126 kinds of 3rd-codon position synonymous mutation:

A  Note  on  Indels  

•  Ignored  because  indels  are  far  more  likely  to  be  deleterious  – More  likely  to  result  in  frame  shiYs    

•  Can  s-ll  be  non-­‐deleterious  – Par-cularly  if  in  mul-ples  of  three  – Over  evolu-onary  -me  indels  more  oYen  observed  in  loops  than  more  constrained  structural  elements  

Evolu-onary  Rates  

Speed  of  Evolu-on  

Rates of protein evolution���(i.e. rates that individual amino acids are substituted)

•  Different regions in proteins have different rates of evolution (functional constraints)

•  Different proteins have different overall rates of evolution

Enolase •  Ubiquitous glycolytic enzyme, highly conserved throughout evolution

•  TIM Barrel family doing an α-proton abstraction

cMLE

MLE

Archaea

Bacteria

Euks

β α γ

All Eukaryotes site rates (63 taxa) mapped on Lobster Enolase

low rates blue high rates red

Site rate categories 1 and 2 (slowest sites)

Site rates Categories 3 and 4

Site rates Categories 5 and 6

Site rates Categories 7 and 8 (fastest sites)

Evolutionary rates as a function of enolase structure/function

•  Rates of evolution increase from the centre of the molecule (slow) to the surface (fast)

•  The pattern is probably due to: –  Distance from the catalytic centre --> catalytic residues don’t change

(slowest), residues that interact with catalytic residues are constrained (slow)

–  Geometric constraints - residues in the centre of the molecule have restricted ‘space’ around them that constrains them. At the surface, there are fewer such constraints

–  Hydrophobic core in centre –  More loops and alpha helices on surface

•  NOTE: this pattern seems to work for soluble globular enzymes with catalytic centre in the centre of mass. It does not hold for structural proteins like tubulin, actin etc.

Rates of evolution of sites versus their structural position

•  There are no completely general rules! –  It depends on what the protein is doing and where.

•  Functional sites (catalytic sites) or sites at interfaces (protein-protein interactions) are conserved

•  Geometric, chemical, folding and functional constraints (catalysis, binding) determine evolutionary constraints

Detec-ng  and  Quan-fying  Evolu-onary  Rela-onships  

How do we know if two proteins are homologous?

(A) If sequences > 100 amino long are >25% identical --> they are probably significantly similar and very likely to be homologous -BLAST, FASTA, Smith-Waterman algorithms are likely to find them “significantly similar” (E-value << 1x10-4)

(B) If they are >100 long and 15-25% identical (Twilight Zone) --> probably homologous BUT need to rigourously test it -a number of methods are available: permutation test

(C) If they are <15% identical......difficult to prove homology -test it -if its not significant look for motifs in multiple alignments -look at tertiary structure

15-23%!identity!

}!

Applica-ons  

•  Evolu-onary  methods  for  studying  protein  func-on  – Annota-ng  novel  proteins  – Func-onal  divergence  

•  Predic-ng  pathogenicity  of  muta-ons  Informing  protein  structure  predic-on  – Mendelian  disease  – Cancer  

Applica-ons  of  Evolu-onary  Biology  to  Medicine  

Inherited  Gene-c  Diseases  and  Cancer  

Lynch  Syndrome  

•  Autosomal  dominant  cancer  syndrome  •  Increased  risk  for  many  cancers,  mostly  colorectal  cancer  due  to  mismatch  repair  defects  

Lynch  Syndrome  

•  Autosomal  dominant  cancer  syndrome  •  Increased  risk  for  many  cancers,  mostly  colorectal  cancer  due  to  mismatch  repair  defects  

Mutator  Phenotype  

•  Inac-va-on  of  mismatch  repair  (MMR)  genes  led  to  mutator  phenotypes  in  E.  coli  and  yeast  •  Included  Microsatellite  instability  

Mutator  Phenotype  

•  Inac-va-on  of  mismatch  repair  (MMR)  genes  led  to  mutator  phenotypes  in  E.  coli  and  yeast  •  Included  Microsatellite  instability  

•  Careful  research  iden-fied  human  homologs  – MLH1  and  MSH2  – Defects  in  these  genes  cause  Lynch  Syndrome    

Mismatch  Repair  

•  Mismatch  Repair  -­‐>    •  Microsatellite  Instability  -­‐>    •  Cancer    Most  microsatellites  spread  throughout  the  genome  in  non-­‐genic  regions    But  some  are  found  in  important  tumor  suppressor  genes  

Applica-ons  of  Evolu-onary  Biology  to  Medicine  

Predic-ng  Pathogenicity  and  Impact  of  Human  Muta-ons  

The  Sequencing  Revolu-on  

Problem  

•  OYen  leY  with  hundreds  to  thousands  of  poten-al  muta-ons  in  a  family  that  “track”  with  the  disease  – Needle  in  a  “stack  of  needles”  problem  

•  Must  discriminate  neutral  missense  muta-ons  from  pathogenic  ones  

Evolu-on  at  Work  

•  Many  programs  exist  to  make  these  predic-ons:  – PolyPhen  – Muta-on  Taster  – EvoD  – SIFT  – PROVEAN  – FATHMM  – etc  

Evolu-on  at  Work  

•  Important  amino  acids  have  low  evolu-onary  rates  – Higher  conserva-on  

•  The  more  important  the  protein  the  more  likely  it  is  to  be  broadly  found  among  eukaryotes  – Also  higher  overall  conserva-on  

•  However  many  important  proteins  in  humans  only  found  in  primates,  mammals,  or  animals  

Evolu-on  at  Work  

…RPLAHTY…! …RPLAHTY…!…RPLVHTY…!…RPIAHTY…!…RPIGHTY…!…RPIICTY…!…RPLACTY…!…RPLLCTY…!!  

Reference  Sequence   Mul-ple  Sequence  Alignment  

Evolu-on  at  Work  

…RPLAHTY…! …RPLAHTY…!…RPLVHTY…!…RPIAHTY…!…RPIGHTY…!…RPIICTY…!…RPLACTY…!…RPLLCTY…!!  

Reference  Sequence   Mul-ple  Sequence  Alignment  

Compute  an  Evolu-onary  Conserva-on  Score  for  Each  Posi-on  

Evolu-on  at  Work  

…RPLACTY…! …RPLAHTY…!…RPLVHTY…!…RPIAHTY…!…RPIGHTY…!…RPIICTY…!…RPLACTY…!…RPLLCTY…!!  

Reference  Sequence   Mul-ple  Sequence  Alignment  

Conserva-ve  changes  more  likely  to  be  neutral  

Evolu-on  at  Work  

…RPLACTP…! …RPLAHTY…!…RPLVHTY…!…RPIAHTY…!…RPIGHTY…!…RPIICTY…!…RPLACTY…!…RPLLCTY…!!  

Reference  Sequence   Mul-ple  Sequence  Alignment  

Radical  changes  more  likely  to  be  deleterious  

Applica-ons  of  Evolu-onary  to  Protein  Func-on  

Func-onal  Divergence  

Func-onal  Divergence  

Gene  1   Gene  2  Gene  1a  

Over  evolu-onary  -me  scales  Gene  1  and  Gene  1a  are  known  as  paralogs,  a    subset  of  homologs    They  can  diverge  from  one  another  in  sequence,  as  well  as  func-on.  

Types  of  Func-onal  Divergence  

•  Subfunc-onaliza-on  – Paralog  specializes  and  retains  only  a  subset  of  ancestral  func-on    

•  Neofunc-onaliza-on  – Paralog  gains  a  new  func-on,  and  loses  old  func-on(s)  

•  Subneofunc-onaliza-on  – Paralog  undergoes  rapid  subfunc-onaliza-on  but  then  undergoes  neofunc-onaliza-on  

Gene  A  

Family  B  

Family  A  

Func-onal  Divergence  

Func-onal  Divergence  …A L H… Species 1 …A L H… Species 2 …A L H… Species 3 …A L H… Species 4 …A L H… Species 5 …A L H… Species 6

…R A H… Species 1 …R R H… Species 2 …R C H… Species 3 …R A H… Species 4 …R A H… Species 5 …R Y H… Species 6

Family  B  

Family  A  

Glyceraldehyde-­‐3-­‐Phosphate  Dehydrogenase  

NAD+  NADH  +Pi  +H+  

NAD+  NADH  +  Pi      +  H+  

Glyceraldehyde-­‐3-­‐Phosphate   1,3-­‐Biphosphoglycerate  

Cytosol:  Glycolysis  

Glyceraldehyde-­‐3-­‐Phosphate  Dehydrogenase  

NADP+  NADPH  +Pi  +H+  

NADP+  NADPH  +Pi  +H+  

Glyceraldehyde-­‐3-­‐Phosphate   1,3-­‐Biphosphoglycerate  

Plas-d:  Calvin  Cycle  

GAPDH  Evolu-on  

Green  Plants  

Cyanobacteria  

‘Chromalveolates’  

Cytosolic  GapC  

Cytosolic  GapC  

GAPDH  Structure  

NADPH  Binding  Necessary  for  Calvin  Cycle  Func-on