TY - JOUR
T1 - How Often Do Protein Genes Navigate Valleys of Low Fitness?
AU - Nelson, Erik D.
AU - Grishin, Nick V.
N1 - Funding Information:
This research was funded in part by a grant from the National Institutes of Health GM127390 to NVG.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/4
Y1 - 2019/4
N2 - To escape from local fitness peaks, a population must navigate across valleys of low fitness. How these transitions occur, and what role they play in adaptation, have been subjects of active interest in evolutionary genetics for almost a century. However, to our knowledge, this problem has never been addressed directly by considering the evolution of a gene, or group of genes, as a whole, including the complex effects of fitness interactions among multiple loci. Here, we use a precise model of protein fitness to compute the probability P(s, Δt) that an allele, randomly sampled from a population at time t, has crossed a fitness valley of depth s during an interval [t − Δt, t] in the immediate past. We study populations of model genes evolving under equilibrium conditions consistent with those in mammalian mitochondria. From this data, we estimate that genes encoding small protein motifs navigate fitness valleys of depth 2Ns Δ 30 with probability P Δ 0.1 on a time scale of human evolution, where N is the (mitochondrial) effective population size. The results are consistent with recent findings for Watson–Crick switching in mammalian mitochondrial tRNA molecules.
AB - To escape from local fitness peaks, a population must navigate across valleys of low fitness. How these transitions occur, and what role they play in adaptation, have been subjects of active interest in evolutionary genetics for almost a century. However, to our knowledge, this problem has never been addressed directly by considering the evolution of a gene, or group of genes, as a whole, including the complex effects of fitness interactions among multiple loci. Here, we use a precise model of protein fitness to compute the probability P(s, Δt) that an allele, randomly sampled from a population at time t, has crossed a fitness valley of depth s during an interval [t − Δt, t] in the immediate past. We study populations of model genes evolving under equilibrium conditions consistent with those in mammalian mitochondria. From this data, we estimate that genes encoding small protein motifs navigate fitness valleys of depth 2Ns Δ 30 with probability P Δ 0.1 on a time scale of human evolution, where N is the (mitochondrial) effective population size. The results are consistent with recent findings for Watson–Crick switching in mammalian mitochondrial tRNA molecules.
KW - Epistasis
KW - Fitness valley crossing
KW - Molecular evolution
KW - Thermodynamic stability
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U2 - 10.3390/genes10040283
DO - 10.3390/genes10040283
M3 - Article
C2 - 30965625
AN - SCOPUS:85068373625
SN - 2073-4425
VL - 10
JO - Genes
JF - Genes
IS - 4
M1 - 283
ER -