Greenbury Sam F, Schaper Steffen, Ahnert Sebastian E, Louis Ard A
Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom.
Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol. 2016 Mar 3;12(3):e1004773. doi: 10.1371/journal.pcbi.1004773. eCollection 2016 Mar.
Mutational neighbourhoods in genotype-phenotype (GP) maps are widely believed to be more likely to share characteristics than expected from random chance. Such genetic correlations should strongly influence evolutionary dynamics. We explore and quantify these intuitions by comparing three GP maps-a model for RNA secondary structure, the HP model for protein tertiary structure, and the Polyomino model for protein quaternary structure-to a simple random null model that maintains the number of genotypes mapping to each phenotype, but assigns genotypes randomly. The mutational neighbourhood of a genotype in these GP maps is much more likely to contain genotypes mapping to the same phenotype than in the random null model. Such neutral correlations can be quantified by the robustness to mutations, which can be many orders of magnitude larger than that of the null model, and crucially, above the critical threshold for the formation of large neutral networks of mutationally connected genotypes which enhance the capacity for the exploration of phenotypic novelty. Thus neutral correlations increase evolvability. We also study non-neutral correlations: Compared to the null model, i) If a particular (non-neutral) phenotype is found once in the 1-mutation neighbourhood of a genotype, then the chance of finding that phenotype multiple times in this neighbourhood is larger than expected; ii) If two genotypes are connected by a single neutral mutation, then their respective non-neutral 1-mutation neighbourhoods are more likely to be similar; iii) If a genotype maps to a folding or self-assembling phenotype, then its non-neutral neighbours are less likely to be a potentially deleterious non-folding or non-assembling phenotype. Non-neutral correlations of type i) and ii) reduce the rate at which new phenotypes can be found by neutral exploration, and so may diminish evolvability, while non-neutral correlations of type iii) may instead facilitate evolutionary exploration and so increase evolvability.
基因型-表型（GP）图谱中的突变邻域被广泛认为比随机概率更有可能共享特征。这种遗传相关性应该会强烈影响进化动态。我们通过将三个GP图谱——一个RNA二级结构模型、蛋白质三级结构的HP模型和蛋白质四级结构的多聚体模型——与一个简单的随机空模型进行比较，来探索和量化这些直觉。这个随机空模型保持映射到每个表型的基因型数量，但随机分配基因型。在这些GP图谱中，一个基因型的突变邻域比随机空模型更有可能包含映射到相同表型的基因型。这种中性相关性可以通过对突变的鲁棒性来量化，其可能比空模型大许多个数量级，关键的是高于形成由突变连接的基因型的大型中性网络的临界阈值——这增强了探索表型新奇性的能力。因此，中性相关性增加了进化能力。我们还研究了非中性相关性：与空模型相比，i）如果在一个基因型的1步突变邻域中发现了一次特定的（非中性）表型，那么在这个邻域中多次发现该表型的机会比预期的要大；ii）如果两个基因型通过一个单一的中性突变相连，那么它们各自的非中性1步突变邻域更有可能相似；iii）如果一个基因型映射到一个折叠或自组装表型，那么它的非中性邻居不太可能是潜在有害的非折叠或非组装表型。i）型和ii）型的非中性相关性降低了通过中性探索发现新表型的速率，并因此可能降低进化能力，而iii）型非中性相关性可能反而促进进化探索，从而增加进化能力。
