Protein Evolution: Structure, Function, and Human Health
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Transcript of Protein Evolution: Structure, Function, and Human Health
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Protein Evolu-on
Structure, Func-on, and Human Health
11/28/2013 Dr. Daniel Gaston, Department
of Pathology 1
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So, about this evolu-on thing?
Why should I care? What use is it?
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Lots of reasons
• Knowledge for its own sake is good – Otherwise, why do science at all?
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Lots of reasons
• Knowledge for its own sake is good – Otherwise, why do science at all?
• Shapes our understanding of ecology and biological diversity
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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)
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Evolu-on of Life on Earth
A (Very) Brief Overview
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Eubacteria"
ROOT Iwabe et al. 1989 Gogarten et al. 1989
Eukaryota"
Archaebacteria"
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Eubacteria"
ROOT Iwabe et al. 1989 Gogarten et al. 1989
Eukaryota"
Archaebacteria"
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Eubacteria"
ROOT Iwabe et al. 1989 Gogarten et al. 1989
Eukaryota"
Archaebacteria"
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You are here
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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
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• 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
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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
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How does evolutionary change happen in proteins?
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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
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Muta-ons
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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!
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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
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Larger Scale Muta-ons
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Exon shuffling and Protein Domains
Exon1 Exon 2 Exon 3
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Exon shuffling and Protein Domains
Exon1 Exon 2 Exon 3
Domain 1 Domain 2
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Exon shuffling and Protein Domains
Exon1 Exon 2 Exon 3
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Exon shuffling and Protein Domains
Exon1 Exon 2 Exon 3
Domain 2 Domain A
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Genomic Scale Muta-ons
Gene 1 Gene 2
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Genomic Scale Muta-ons
Gene 1 Gene 2
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Gene Duplica-on
Gene 1 Gene 2
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Gene Duplica-on
Gene 1 Gene 2 Gene 1a
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Gene-c DriY and Selec-on
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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
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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%)
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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
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Nearly-neutral theory ���
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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
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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
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Polymorphism
• Polymorphisms are sites with more than one allele present in a popula-on – Muta-ons that have not yet been fixed
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Muta-on and Codons
Not all muta-ons are created equal
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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!
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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
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8 kinds of 1st codon-position synonymous mutation: R-->R and L-->L
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126 kinds of 3rd-codon position synonymous mutation:
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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
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Evolu-onary Rates
Speed of Evolu-on
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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
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Enolase • Ubiquitous glycolytic enzyme, highly conserved throughout evolution
• TIM Barrel family doing an α-proton abstraction
cMLE
MLE
Archaea
Bacteria
Euks
β α γ
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All Eukaryotes site rates (63 taxa) mapped on Lobster Enolase
low rates blue high rates red
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Site rate categories 1 and 2 (slowest sites)
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Site rates Categories 3 and 4
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Site rates Categories 5 and 6
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Site rates Categories 7 and 8 (fastest sites)
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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.
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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
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Detec-ng and Quan-fying Evolu-onary Rela-onships
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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
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15-23%!identity!
}!
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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
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Applica-ons of Evolu-onary Biology to Medicine
Inherited Gene-c Diseases and Cancer
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Lynch Syndrome
• Autosomal dominant cancer syndrome • Increased risk for many cancers, mostly colorectal cancer due to mismatch repair defects
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Lynch Syndrome
• Autosomal dominant cancer syndrome • Increased risk for many cancers, mostly colorectal cancer due to mismatch repair defects
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Mutator Phenotype
• Inac-va-on of mismatch repair (MMR) genes led to mutator phenotypes in E. coli and yeast • Included Microsatellite instability
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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
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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
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Applica-ons of Evolu-onary Biology to Medicine
Predic-ng Pathogenicity and Impact of Human Muta-ons
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The Sequencing Revolu-on
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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
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Evolu-on at Work
• Many programs exist to make these predic-ons: – PolyPhen – Muta-on Taster – EvoD – SIFT – PROVEAN – FATHMM – etc
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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
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Evolu-on at Work
…RPLAHTY…! …RPLAHTY…!…RPLVHTY…!…RPIAHTY…!…RPIGHTY…!…RPIICTY…!…RPLACTY…!…RPLLCTY…!!
Reference Sequence Mul-ple Sequence Alignment
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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
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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
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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
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Applica-ons of Evolu-onary to Protein Func-on
Func-onal Divergence
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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.
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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
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Gene A
Family B
Family A
Func-onal Divergence
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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
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Glyceraldehyde-‐3-‐Phosphate Dehydrogenase
NAD+ NADH +Pi +H+
NAD+ NADH + Pi + H+
Glyceraldehyde-‐3-‐Phosphate 1,3-‐Biphosphoglycerate
Cytosol: Glycolysis
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Glyceraldehyde-‐3-‐Phosphate Dehydrogenase
NADP+ NADPH +Pi +H+
NADP+ NADPH +Pi +H+
Glyceraldehyde-‐3-‐Phosphate 1,3-‐Biphosphoglycerate
Plas-d: Calvin Cycle
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GAPDH Evolu-on
Green Plants
Cyanobacteria
‘Chromalveolates’
Cytosolic GapC
Cytosolic GapC
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GAPDH Structure
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NADPH Binding Necessary for Calvin Cycle Func-on