Next month in Tartu there will be a conference on antibiotics and protein synthesis organized by Tanel Tenson.
Registration is FREE and now open!
Confirmed speakers:
James Williamson (Scripps Research Institute),
Alexander Mankin (University of Illinois at Chicago),
Steven Douthwaite (University of South Denmark),
Daniel Wilson (University of Munich),
Karen Shaw (Trius Therapeutics),
Ada Yonath (Weizmann Institute of Science).
Birte Vester (University of Southern Denmark)
Joyce Sutcliffe (Tetraphase Pharmaceuticals)
Mans Ehrenberg (Uppsala University)
Chaitan Khosla (Stanford University)
Markus Zeitlinger (Medical University of Vienna)
It's also probable that Vasili Hauryliuk and I will be speaking too.
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the evolutionary rate of protein–protein interactions
This is my first post for a while, since I've been pretty busy - first I was writing a grant proposal for some research money, then I was on holiday in Bavaria and Austria, after which I was busy finishing off a manuscript on evolution of starvation response enzymes. As of yesterday, the manuscript is with my boss, so time to catch up with the world.
I noticed an interesting upcoming PNAS paper: Measuring the evolutionary rate of protein–protein interaction. This tackles a subject close to my heart - functional evolution of proteins. The authors tackle measuring the rate at which functional changes happen, with function in this case measured by gain and loss of PPIs (protein-protein interactions).
They start by comparing yeast S. cerevisiae, which has abundant PPI data with another yeast Kluyveromyces waltii. These two diverged ∼150 MYA, and they are sort of special relatives since a whole genome duplication occurred in the lineage to S. cerevisiae after the divergence of K. waltii. This worried me that this could affect the rate of PPI change, due to the sudden influx of homologues in the S. cerevisiae lineage inflating the PPI count. However, the problem of duplicates was surmounted by only considering one to one orthologs (ie proteins related by vertical descent and not gene duplication (which would be paralogues)). In all, 43 proteins passed the yeast 2 hybrid test for PPIs, and all of these were found to be conserved in both yeasts. From this, they estimated that the 95% confidence interval of the total rate of PPI evolution is between 0 and 4.6 × 10−10 per PPI per year
They then went on to consider animals. Using PPI data from nemtodes, they found two of five confirmed S. cerevisiae PPIs are conserved in C. elegans. These two species diverged ~1,300 MYA, so the 95% confidence interval is 1.6 × 10−10 to 2.0 × 10−9. Using transcription factor PPI data from humans and mice, which diverged 90 MYA, they found that six of six mouse PPIs are conserved in humans. From this, they estimate the 95% confidence interval is 0 to 5.5 × 10−9. Using all the dataset together, they arrive at the final value for the rate of PPI change: (2.6 ± 1.6) × 10−10 per PPI per year.
It's great to have a value for the rate of this sort of rare evolutionary change, and the authors are certainly very rigorous is eliminating the possibility of false positives and false positives. However, I'm left wondering whether after all this filtering, they're left with enough data to be really sure of their estimates. I count 54 PPIs in total, of which only 3 are lost, and that's in one lineage. Is that really enough data to go on? Well, I'm certainly not a statistician, so I can only assume that this was checked out thoroughly by folks much more informed on this kind of thing than I am.
An interesting future route would be to compare protein substitution and PPI rates between lineages. I'm wondering whether organisms with high amino acids substituion rates (like nematodes and other parasites) have a PPI rate that's (relatively) just as high, or whether this is dampened by compensatory mutations in binding interfaces. It'd also be interesting to compare the eukaryotic PPI rate to the bacterial one.
Qian W, He X, Chan E, Xu H, & Zhang J (2011). Measuring the evolutionary rate of protein-protein interaction. Proceedings of the National Academy of Sciences of the United States of America PMID: 21555556
I noticed an interesting upcoming PNAS paper: Measuring the evolutionary rate of protein–protein interaction. This tackles a subject close to my heart - functional evolution of proteins. The authors tackle measuring the rate at which functional changes happen, with function in this case measured by gain and loss of PPIs (protein-protein interactions).
They start by comparing yeast S. cerevisiae, which has abundant PPI data with another yeast Kluyveromyces waltii. These two diverged ∼150 MYA, and they are sort of special relatives since a whole genome duplication occurred in the lineage to S. cerevisiae after the divergence of K. waltii. This worried me that this could affect the rate of PPI change, due to the sudden influx of homologues in the S. cerevisiae lineage inflating the PPI count. However, the problem of duplicates was surmounted by only considering one to one orthologs (ie proteins related by vertical descent and not gene duplication (which would be paralogues)). In all, 43 proteins passed the yeast 2 hybrid test for PPIs, and all of these were found to be conserved in both yeasts. From this, they estimated that the 95% confidence interval of the total rate of PPI evolution is between 0 and 4.6 × 10−10 per PPI per year
They then went on to consider animals. Using PPI data from nemtodes, they found two of five confirmed S. cerevisiae PPIs are conserved in C. elegans. These two species diverged ~1,300 MYA, so the 95% confidence interval is 1.6 × 10−10 to 2.0 × 10−9. Using transcription factor PPI data from humans and mice, which diverged 90 MYA, they found that six of six mouse PPIs are conserved in humans. From this, they estimate the 95% confidence interval is 0 to 5.5 × 10−9. Using all the dataset together, they arrive at the final value for the rate of PPI change: (2.6 ± 1.6) × 10−10 per PPI per year.
It's great to have a value for the rate of this sort of rare evolutionary change, and the authors are certainly very rigorous is eliminating the possibility of false positives and false positives. However, I'm left wondering whether after all this filtering, they're left with enough data to be really sure of their estimates. I count 54 PPIs in total, of which only 3 are lost, and that's in one lineage. Is that really enough data to go on? Well, I'm certainly not a statistician, so I can only assume that this was checked out thoroughly by folks much more informed on this kind of thing than I am.
An interesting future route would be to compare protein substitution and PPI rates between lineages. I'm wondering whether organisms with high amino acids substituion rates (like nematodes and other parasites) have a PPI rate that's (relatively) just as high, or whether this is dampened by compensatory mutations in binding interfaces. It'd also be interesting to compare the eukaryotic PPI rate to the bacterial one.
Qian W, He X, Chan E, Xu H, & Zhang J (2011). Measuring the evolutionary rate of protein-protein interaction. Proceedings of the National Academy of Sciences of the United States of America PMID: 21555556
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