Modeling Structural and Genomic Constraints in the Evolution of Proteins

Author(s):  
Ugo Bastolla ◽  
Markus Porto
Genetics ◽  
1986 ◽  
Vol 113 (4) ◽  
pp. 1077-1091
Author(s):  
John H Gillespie

ABSTRACT A statistical analysis of DNA sequences from four nuclear loci and five mitochondrial loci from different orders of mammals is described. A major aim of the study is to describe the variation in the rate of molecular evolution of proteins and DNA. A measure of rate variability is the statistic R, the ratio of the variance in the number of substitutions to the mean number. For proteins, R is found to be in the range 0.16 < R < 35.55, thus extending in both directions the values seen in previous studies. An analysis of codons shows that there is a highly significant excess of double substitutions in the first and second positions, but not in the second and third or first and third positions. The analysis of the dynamics of nucleotide evolution showed that the ergodic Markov chain models that are the basis of most published formulas for correcting for multiple substitutions are incompatible with the data. A bootstrap procedure was used to show that the evolution of the individual nucleotides, even the third positions, show the same variation in rates as seen in the proteins. It is argued that protein and silent DNA evolution are uncoupled, with the evolution at both levels showing patterns that are better explained by the action of natural selection than by neutrality. This conclusion is based primarily on a comparison of the nuclear and mitochondrial results.


1992 ◽  
Vol 267 (23) ◽  
pp. 16007-16010
Author(s):  
J.D. Marks ◽  
H.R. Hoogenboom ◽  
A.D. Griffiths ◽  
G Winter

2014 ◽  
Vol 22 ◽  
pp. 129-136 ◽  
Author(s):  
Michael D Lane ◽  
Burckhard Seelig

2008 ◽  
Author(s):  
Jeremy Koscielecki ◽  
Jason Hillebrecht ◽  
Robert Birge

2015 ◽  
Author(s):  
Maximilian O. Press ◽  
Christine Queitsch ◽  
Elhanan Borenstein

AbstractEvolutionary innovation must occur in the context of some genomic background, which limits available evolutionary paths. For example, protein evolution by sequence substitution is constrained by epistasis between residues. In prokaryotes, evolutionary innovation frequently happens by macrogenomic events such as horizontal gene transfer (HGT). Previous work has suggested that HGT can be influenced by ancestral genomic content, yet the extent of such gene-level constraints has not yet been systematically characterized. Here, we evaluated the evolutionary impact of such constraints in prokaryotes, using probabilistic ancestral reconstructions from 634 extant prokaryotic genomes and a novel framework for detecting evolutionary constraints on HGT events. We identified 8,228 directional dependencies between genes, and demonstrated that many such dependencies reflect known functional relationships, including, for example, evolutionary dependencies of the photosynthetic enzyme RuBisCO. Modeling all dependencies as a network, we adapted an approach from graph theory to establish chronological precedence in the acquisition of different genomic functions. Specifically, we demonstrated that specific functions tend to be gained sequentially, suggesting that evolution in prokaryotes is governed by functional assembly patterns. Finally, we showed that these dependencies are universal rather than clade-specific and are often sufficient for predicting whether or not a given ancestral genome will acquire specific genes. Combined, our results indicate that evolutionary innovation via HGT is profoundly constrained by epistasis and historical contingency, similar to the evolution of proteins and phenotypic characters, and suggest that the emergence of specific metabolic and pathological phenotypes in prokaryotes can be predictable from current genomes.


PLoS Biology ◽  
2007 ◽  
Vol 5 (2) ◽  
pp. e14 ◽  
Author(s):  
Joanna L Parmley ◽  
Araxi O Urrutia ◽  
Lukasz Potrzebowski ◽  
Henrik Kaessmann ◽  
Laurence D Hurst

2020 ◽  
Vol 9 (6) ◽  
pp. 1270-1276 ◽  
Author(s):  
Ziwei Zhong ◽  
Brandon G. Wong ◽  
Arjun Ravikumar ◽  
Garri A. Arzumanyan ◽  
Ahmad S. Khalil ◽  
...  

2020 ◽  
Vol 85 (S1) ◽  
pp. 131-153 ◽  
Author(s):  
Yu. L. Vechtomova ◽  
T. A. Telegina ◽  
M. S. Kritsky

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