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Protein Evolution: Structure, Function, and Human Health
The document discusses the evolutionary processes of proteins and their implications for human health, detailing the origins of life and key evolutionary concepts such as homologous sequences, mutations, and natural selection. It emphasizes the significance of understanding protein evolution for applications in medicine, including cancer genetics and predicting pathogenic mutations. Moreover, it explores evolutionary rates in proteins, their functional constraints, and the classification of mutations, highlighting the interplay between evolution and protein functionality.
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Protein Evolution: Structure, Function, and Human Health
- 1. Protein Evolu-on Structure, Func-on, and Human Health 11/28/2013 Dr. Daniel Gaston, Department of Pathology 1
- 2. So, about this evolu-on thing? Why should I care? What use is it?
- 3. Lots of reasons • Knowledge for its own sake is good – Otherwise, why do science at all?
- 4. Lots of reasons • Knowledge for its own sake is good – Otherwise, why do science at all? • Shapes our understanding of ecology and biological diversity
- 5. 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)
- 6. Evolu-on of Life on Earth A (Very) Brief Overview
- 7. Eukaryota" Eubacteria" Archaebacteria" ROOT Iwabe et al.1989 Gogarten et al. 1989
- 8. Eukaryota" Eubacteria" Archaebacteria" ROOT Iwabe et al.1989 Gogarten et al. 1989
- 9. Eukaryota" Eubacteria" Archaebacteria" ROOT Iwabe et al.1989 Gogarten et al. 1989
- 11.
- 12. 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
- 13. Some Defini-ons • 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)
- 14. Some Defini-ons • Divergence = change in two sequences over time (after splitting from a common ancestor) Ancestral sequence T Sequence 1 T Sequence 2 • 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)
- 15. How does evolutionarychange happen in proteins?
- 16. 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
- 17.
- 18. Point Muta-ons Unrepaired mispairedbase ! ! ! ! AGGTTCCAATTAA! TCCAAGGTCAATT! ! REPLICATION (meiotic or mitotic division) ! AGGTTCCAATTAA ! AGGTTCCAGTTAA ! TCCAAGGTCAATT! TCCAAGGTTAATT! Wild-type alleles ! Mutant allele Mutant Gamete (for multicellular org.) Wild-type Gamete (for multicellular org.)
- 19. 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
- 20.
- 21. Exon shuffling and Protein Domains Exon1 Exon 2 Exon 3
- 22. Exon shuffling and Protein Domains Exon1 Exon 2 Domain 1 Exon 3 Domain 2
- 23. Exon shuffling and Protein Domains Exon 2 Exon1 Exon 3
- 24. Exon shuffling and Protein Domains Exon 2 Exon1 Domain A Exon 3 Domain 2
- 25. Genomic Scale Muta-ons Gene 1 Gene 2
- 26. Genomic Scale Muta-ons Gene 1 Gene 2
- 27. Gene Duplica-on Gene 1 Gene 2
- 28. Gene Duplica-on Gene 1 Gene 1a Gene 2
- 29. Gene-c DriY and Selec-on
- 30. 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
- 31. Fixation of alleles Populationwith two alleles: N generations Proportion of Proportion of = 1/14 (7.1%) = 13/14 (93%) 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
- 32. Natural selection andNeutral 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
- 33.
- 34. 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
- 35. 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
- 36. Polymorphism • Polymorphisms are sites with more than one allele present in a popula-on – Muta-ons that have not yet been fixed
- 37. Muta-on and Codons Not all muta-ons are created equal
- 38. Point mutations inprotein 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!
- 39. 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
- 41. 8 kinds of1st codon-position synonymous mutation: R--R and L--L
- 42. 126 kinds of3rd-codon position synonymous mutation:
- 43. 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
- 44. Evolu-onary Rates Speed of Evolu-on
- 45. Rates of proteinevolution (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
- 47. Enolase • Ubiquitous glycolyticenzyme, highly conserved throughout evolution • TIM Barrel family doing an α-proton abstraction cMLE Euks Archaea β MLE α γ Bacteria
- 48. All Eukaryotes siterates (63 taxa) mapped on Lobster Enolase low rates blue high rates red
- 49. Site rate categories1 and 2 (slowest sites)
- 50. Site rates Categories3 and 4
- 51. Site rates Categories5 and 6
- 52. Site rates Categories7 and 8 (fastest sites)
- 53. Evolutionary rates asa 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.
- 54. Rates of evolutionof 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
- 55. Detec-ng and Quan-fying Evolu-onary Rela-onships
- 56. How do weknow 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
- 57.
- 59. 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
- 60. Applica-ons of Evolu-onary Biology to Medicine Inherited Gene-c Diseases and Cancer
- 61. Lynch Syndrome • Autosomal dominant cancer syndrome • Increased risk for many cancers, mostly colorectal cancer due to mismatch repair defects
- 62. Lynch Syndrome • Autosomal dominant cancer syndrome • Increased risk for many cancers, mostly colorectal cancer due to mismatch repair defects
- 63. Mutator Phenotype • Inac-va-on of mismatch repair (MMR) genes led to mutator phenotypes in E. coli and yeast • Included Microsatellite instability
- 64. 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
- 65. 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
- 66. Applica-ons of Evolu-onary Biology to Medicine Predic-ng Pathogenicity and Impact of Human Muta-ons
- 67.
- 68. 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
- 69. Evolu-on at Work • Many programs exist to make these predic-ons: – PolyPhen – Muta-on Taster – EvoD – SIFT – PROVEAN – FATHMM – etc
- 70. 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
- 71. Evolu-on at Work Reference Sequence …RPLAHTY…! Mul-ple Sequence Alignment …RPLAHTY…! …RPLVHTY…! …RPIAHTY…! …RPIGHTY…! …RPIICTY…! …RPLACTY…! …RPLLCTY…! !
- 72. Evolu-on at Work Reference Sequence …RPLAHTY…! Mul-ple Sequence Alignment …RPLAHTY…! …RPLVHTY…! …RPIAHTY…! …RPIGHTY…! …RPIICTY…! …RPLACTY…! …RPLLCTY…! ! Compute an Evolu-onary Conserva-on Score for Each Posi-on
- 73. Evolu-on at Work Reference Sequence …RPLACTY…! Mul-ple Sequence Alignment …RPLAHTY…! …RPLVHTY…! …RPIAHTY…! …RPIGHTY…! …RPIICTY…! …RPLACTY…! …RPLLCTY…! ! Conserva-ve changes more likely to be neutral
- 74. Evolu-on at Work Reference Sequence …RPLACTP…! Mul-ple Sequence Alignment …RPLAHTY…! …RPLVHTY…! …RPIAHTY…! …RPIGHTY…! …RPIICTY…! …RPLACTY…! …RPLLCTY…! ! Radical changes more likely to be deleterious
- 75. Applica-ons of Evolu-onary to Protein Func-on Func-onal Divergence
- 76. Func-onal Divergence Gene 1 Gene 1a Gene 2 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.
- 77. 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
- 78. Func-onal Divergence Family A Gene A Family B
- 79. Func-onal Divergence Family A …A L H… …A L H… …A L H… …A L H… …A L H… …A L H… Species 1 Species 2 Species 3 Species 4 Species 5 Species 6 Family B …R A H… …R R H… …R C H… …R A H… …R A H… …R Y H… Species 1 Species 2 Species 3 Species 4 Species 5 Species 6
- 80. Glyceraldehyde-‐3-‐Phosphate Dehydrogenase NAD+ +Pi NAD+ + Pi Glyceraldehyde-‐3-‐Phosphate NADH +H+ NADH + H+ 1,3-‐Biphosphoglycerate Cytosol: Glycolysis
- 81. Glyceraldehyde-‐3-‐Phosphate Dehydrogenase NADP+ NADPH +Pi +H+ NADP+ NADPH +Pi +H+ Glyceraldehyde-‐3-‐Phosphate 1,3-‐Biphosphoglycerate Plas-d: Calvin Cycle
- 82. GAPDH Evolu-on Cytosolic GapC Green Plants Cyanobacteria ‘Chromalveolates’ Cytosolic GapC
- 83.
- 84. NADPH Binding Necessary for Calvin Cycle Func-on