Protein evolution - is it plausible?
https://reasonandscience.catsboard.com/t3189-protein-evolution-is-it-plausible
Claim: Thousands of new metabolic and regulatory enzymes have evolved by gene duplication and divergence since the dawn of life. 2
Reply: Although the process of gene duplication and subsequent random mutation has certainly contributed to the size and diversity of the genome, it is alone insufficient in explaining the origination of the highly complex information pertinent to the essential functioning of living organisms. 3
In 2015 another group from Cornell tried to take on the “waiting time problem.” Their results were published in the peer-reviewed journal Theoretical Biology and Medical Modeling.
What were the results? According to the lead researcher:
“The waiting time problem cannot be honestly ignored. Even given best-case scenarios, using parameters that are grossly over-generous, waiting times are consistently prohibitively long.” His conclusions in this connection are stunning: “Even in a hundred billion years, much longer than the age of the universe, there is not enough time to establish the genetic equivalent of a very simple “sentence” (ten or more nucleotides). This problem is so fundamental that it justifies a complete re-assessment of the basic Darwinian mechanism.”
The ”Waiting Time Problem” is serious and it is very real.
Boammaaruri Evolution’s time problem – waiting for genetic change January 26, 2017 1
The most common process in protein evolution is duplication followed by divergence. Sanford et al, show it would take 84 million years depending on the selective advantage of the gene in question. Gene duplication, mutations and natural selection cannot explain genetic change because it would take too much time. Evolution’s gene duplication mechanism that is supposed to primarily explain how genes evolve into new genes with new functions has been shown to be ineffective and false.
John Sanford The waiting time problem in a model hominin population 2015 Sep 17 4
We simulated a classic pre-human hominin population of at least 10,000 individuals, with a generation time of 20 years, and with very strong selection (50 % selective elimination). Biologically realistic numerical simulations revealed that a population of this type required inordinately long waiting times to establish even the shortest nucleotide strings. Establishing a string of two nucleotides required on average 84 million years. Establishing a string of five nucleotides required on average 2 billion years. We found that waiting times were reduced by higher mutation rates, stronger fitness benefits, and larger population sizes. However, even using the most generous feasible parameters settings, the waiting time required to establish any specific nucleotide string within this type of population was consistently prohibitive.
Csaba Pál Evolution of complex adaptations in molecular systems 21 July 2017 5
By defnition, complex adaptations are phenotypic traits requiring multiple, specific mutations to yield a functional advantage. Despite substantial efforts, the population genetic mechanisms driving complex adaptations are unclear. In a nutshell, the paradox is as follows. As mutational events are rare, complex adaptations are unlikely to occur in small populations: the waiting time for the rise of multiple specific mutations would be very long. Tis would suggest that the evolution of complex adaptations is facilitated in large populations. The issue is more complicated, though. If the intermediate mutational steps towards complex adaptation are individually deleterious, they will be purged in large populations. As the fixation of deleterious mutations is exceedingly unlikely with growing population size, shift from one adaptive peak to another through weakly deleterious intermediates can occur only when genetic drift prevails, that is, in small populations.
How can the seeming paradox be resolved?
First, the intermediate mutation may confer a benefit under alternative environmental or genetic conditions. Alternatively, we may need to abandon the idea of sequential fixation of intermediate mutations. Although individual deleterious intermediate-stage mutations have a negligible chance of fixation in large populations, there is a steady input of such mutations. Indeed, segregating deleterious mutations are common in natural populations of yeast and in humans. Such a stable reservoir of non-adaptive mutations is poised for the rise of a second mutation that is adaptive in the specific genetic background, leading to simultaneous fixation of the two mutations. As the size of the reservoir increases with the growing population size, the time required for this process declines sharply as population size increases. Thus, selection alone can offer a solution to escape local fitness peaks in natural populations. One may argue that this theory is unlikely to work in the case of three or more non-adaptive intermediate steps, as the simultaneous rise of multiple mutations in a single genotype is exceedingly unlikely. Although this criticism may hold when the intermediate steps are deleterious, the effect is partly offset by the elevated number of paths towards the final adaptation when the intermediate states are neutral. Evolutionary escape from local fitness peaks has been a central problem for more than 85 years now. Unfortunately, the lack of consensus on the relative roles played by mutation, recombination, and random genetic drift hinders empirical tests. The role of recombination is particularly controversial: it can either facilitate the escape from local fitness peaks by combining mutations from different individuals or hinder it by breaking up adaptive combinations. As a result, only low recombination rates can speed up the crossing of fitness valleys, while high rates are predicted to be disadvantageous. Rates of recombination and mutation, and the importance of genetic drift vary enormously across evolutionary lineages, but do they influence which evolutionary pathways are realized in nature? For instance, it is currently unknown whether multicellular eukaryotic species with relatively low population size and high mutation rates have the greater or smaller acquisition of complex adaptations on a per generation basis. As a further complication, the answer probably depends on the molecular basis of adaptation, for example, the number of sites involved and whether the intermediate states are deleterious or neutral. Finally, most theoretical considerations have focused on the rate of traversing a single specific mutational path, which is expected to be low when neutral or deleterious mutations are involved. However, in realistic fitness landscapes, there could be numerous different possible mutational paths to the same genotype, and there might be several possible beneficial genotypes that are phenotypically equivalent. As a result, evolution may follow trajectories that involve neutral or deleterious intermediates even in the presence of directly uphill trajectories. Addressing this issue demands quantification of the frequencies of these different modes of mutational trajectories on realistic fitness landscapes.
1. The more statistically improbable something is, the less it makes sense to believe that it just happened by blind chance.
2. About 90% of feathers are made of Beta-Keratin proteins. The central filament has about 32 amino acids in length, which is very similar in all beta keratin sequences, and according to a science paper, highly conserved and with almost no change in the 285 Million years. The odds to have that right sequence, if we consider that one can choose amongst 20 different amino acids in each of the 32 positions, it is as hitting the jackpot after 10^42 power of attempts. That is as if tossing a coin tredecillion times, and having it come up all heads in a row. The paper: Molecular packing in the feather keratin filament, states: "There are seven residues that are maintained absolutely across the range of keratins whose sequences have thus far been determined (1 P, 16 P, 22 T, 24 P, 25 G, 26 P, and 28 L, where the numbering commences from the start of the designated 32-residue segment)". That does not mean, that the other of the 32 residues do not need to be right, but let's work just with these 7 for a moment. The odds to have them right are one to 20^7 or 1 to 10^10. That is one in 10,000,000,000 or one attempt in 10 billion.
According to Sanford, from Cornell University, the establishment of just a two-letter word (two specific mutations within a hominin population of ten thousand) requires at least 84 million years. A three-letter word requires at least 376 million years. A six-letter word requires over 4 billion years. An eight-letter word requires over 18 billion years.
3. In my opinion, that stretches the credibility, that this is plausible and that it happened. It is more rational to believe that beta keratin was a material selected by an intelligent designer for specific purposes.
1. https://boammaaruri.blog/2017/01/26/evolutions-time-problem-waiting-for-genetic-change/
2. https://febs.onlinelibrary.wiley.com/doi/10.1111/febs.15299
3. http://onlinelibrary.wiley.com/doi/10.1002/cplx.20365/abstract
4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4573302/
5. https://sci-hub.ren/10.1038/s41559-017-0228-1
https://reasonandscience.catsboard.com/t3189-protein-evolution-is-it-plausible
Claim: Thousands of new metabolic and regulatory enzymes have evolved by gene duplication and divergence since the dawn of life. 2
Reply: Although the process of gene duplication and subsequent random mutation has certainly contributed to the size and diversity of the genome, it is alone insufficient in explaining the origination of the highly complex information pertinent to the essential functioning of living organisms. 3
In 2015 another group from Cornell tried to take on the “waiting time problem.” Their results were published in the peer-reviewed journal Theoretical Biology and Medical Modeling.
What were the results? According to the lead researcher:
“The waiting time problem cannot be honestly ignored. Even given best-case scenarios, using parameters that are grossly over-generous, waiting times are consistently prohibitively long.” His conclusions in this connection are stunning: “Even in a hundred billion years, much longer than the age of the universe, there is not enough time to establish the genetic equivalent of a very simple “sentence” (ten or more nucleotides). This problem is so fundamental that it justifies a complete re-assessment of the basic Darwinian mechanism.”
The ”Waiting Time Problem” is serious and it is very real.
Boammaaruri Evolution’s time problem – waiting for genetic change January 26, 2017 1
The most common process in protein evolution is duplication followed by divergence. Sanford et al, show it would take 84 million years depending on the selective advantage of the gene in question. Gene duplication, mutations and natural selection cannot explain genetic change because it would take too much time. Evolution’s gene duplication mechanism that is supposed to primarily explain how genes evolve into new genes with new functions has been shown to be ineffective and false.
John Sanford The waiting time problem in a model hominin population 2015 Sep 17 4
We simulated a classic pre-human hominin population of at least 10,000 individuals, with a generation time of 20 years, and with very strong selection (50 % selective elimination). Biologically realistic numerical simulations revealed that a population of this type required inordinately long waiting times to establish even the shortest nucleotide strings. Establishing a string of two nucleotides required on average 84 million years. Establishing a string of five nucleotides required on average 2 billion years. We found that waiting times were reduced by higher mutation rates, stronger fitness benefits, and larger population sizes. However, even using the most generous feasible parameters settings, the waiting time required to establish any specific nucleotide string within this type of population was consistently prohibitive.
Csaba Pál Evolution of complex adaptations in molecular systems 21 July 2017 5
By defnition, complex adaptations are phenotypic traits requiring multiple, specific mutations to yield a functional advantage. Despite substantial efforts, the population genetic mechanisms driving complex adaptations are unclear. In a nutshell, the paradox is as follows. As mutational events are rare, complex adaptations are unlikely to occur in small populations: the waiting time for the rise of multiple specific mutations would be very long. Tis would suggest that the evolution of complex adaptations is facilitated in large populations. The issue is more complicated, though. If the intermediate mutational steps towards complex adaptation are individually deleterious, they will be purged in large populations. As the fixation of deleterious mutations is exceedingly unlikely with growing population size, shift from one adaptive peak to another through weakly deleterious intermediates can occur only when genetic drift prevails, that is, in small populations.
How can the seeming paradox be resolved?
First, the intermediate mutation may confer a benefit under alternative environmental or genetic conditions. Alternatively, we may need to abandon the idea of sequential fixation of intermediate mutations. Although individual deleterious intermediate-stage mutations have a negligible chance of fixation in large populations, there is a steady input of such mutations. Indeed, segregating deleterious mutations are common in natural populations of yeast and in humans. Such a stable reservoir of non-adaptive mutations is poised for the rise of a second mutation that is adaptive in the specific genetic background, leading to simultaneous fixation of the two mutations. As the size of the reservoir increases with the growing population size, the time required for this process declines sharply as population size increases. Thus, selection alone can offer a solution to escape local fitness peaks in natural populations. One may argue that this theory is unlikely to work in the case of three or more non-adaptive intermediate steps, as the simultaneous rise of multiple mutations in a single genotype is exceedingly unlikely. Although this criticism may hold when the intermediate steps are deleterious, the effect is partly offset by the elevated number of paths towards the final adaptation when the intermediate states are neutral. Evolutionary escape from local fitness peaks has been a central problem for more than 85 years now. Unfortunately, the lack of consensus on the relative roles played by mutation, recombination, and random genetic drift hinders empirical tests. The role of recombination is particularly controversial: it can either facilitate the escape from local fitness peaks by combining mutations from different individuals or hinder it by breaking up adaptive combinations. As a result, only low recombination rates can speed up the crossing of fitness valleys, while high rates are predicted to be disadvantageous. Rates of recombination and mutation, and the importance of genetic drift vary enormously across evolutionary lineages, but do they influence which evolutionary pathways are realized in nature? For instance, it is currently unknown whether multicellular eukaryotic species with relatively low population size and high mutation rates have the greater or smaller acquisition of complex adaptations on a per generation basis. As a further complication, the answer probably depends on the molecular basis of adaptation, for example, the number of sites involved and whether the intermediate states are deleterious or neutral. Finally, most theoretical considerations have focused on the rate of traversing a single specific mutational path, which is expected to be low when neutral or deleterious mutations are involved. However, in realistic fitness landscapes, there could be numerous different possible mutational paths to the same genotype, and there might be several possible beneficial genotypes that are phenotypically equivalent. As a result, evolution may follow trajectories that involve neutral or deleterious intermediates even in the presence of directly uphill trajectories. Addressing this issue demands quantification of the frequencies of these different modes of mutational trajectories on realistic fitness landscapes.
1. The more statistically improbable something is, the less it makes sense to believe that it just happened by blind chance.
2. About 90% of feathers are made of Beta-Keratin proteins. The central filament has about 32 amino acids in length, which is very similar in all beta keratin sequences, and according to a science paper, highly conserved and with almost no change in the 285 Million years. The odds to have that right sequence, if we consider that one can choose amongst 20 different amino acids in each of the 32 positions, it is as hitting the jackpot after 10^42 power of attempts. That is as if tossing a coin tredecillion times, and having it come up all heads in a row. The paper: Molecular packing in the feather keratin filament, states: "There are seven residues that are maintained absolutely across the range of keratins whose sequences have thus far been determined (1 P, 16 P, 22 T, 24 P, 25 G, 26 P, and 28 L, where the numbering commences from the start of the designated 32-residue segment)". That does not mean, that the other of the 32 residues do not need to be right, but let's work just with these 7 for a moment. The odds to have them right are one to 20^7 or 1 to 10^10. That is one in 10,000,000,000 or one attempt in 10 billion.
According to Sanford, from Cornell University, the establishment of just a two-letter word (two specific mutations within a hominin population of ten thousand) requires at least 84 million years. A three-letter word requires at least 376 million years. A six-letter word requires over 4 billion years. An eight-letter word requires over 18 billion years.
3. In my opinion, that stretches the credibility, that this is plausible and that it happened. It is more rational to believe that beta keratin was a material selected by an intelligent designer for specific purposes.
1. https://boammaaruri.blog/2017/01/26/evolutions-time-problem-waiting-for-genetic-change/
2. https://febs.onlinelibrary.wiley.com/doi/10.1111/febs.15299
3. http://onlinelibrary.wiley.com/doi/10.1002/cplx.20365/abstract
4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4573302/
5. https://sci-hub.ren/10.1038/s41559-017-0228-1
