MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.
Department of Structural Biology and Center of Excellence for Data-Driven Discovery, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
Sci Adv. 2023 Jul 28;9(30):eade2903. doi: 10.1126/sciadv.ade2903.
Natural selection can only operate on available genetic variation. Thus, determining the probability of accessing different sequence variants from a starting sequence can help predict evolutionary trajectories and outcomes. We define the concept of "variant accessibility" as the probability that a set of genotypes encoding a particular protein function will arise through mutations before subject to natural selection. This probability is shaped by the mutational biases of nucleotides and the structure of the genetic code. Using the influenza A virus as a model, we discuss how a more accessible but less fit variant can emerge as an adaptation rather than a more fit variant. We describe a genotype-accessibility landscape, complementary to the genotype-fitness landscape, that informs the likelihood of a starting sequence reaching different parts of genotype space. The proposed framework lays the foundation for predicting the emergence of adaptive genotypes in evolving systems such as viruses and tumors.
自然选择只能作用于现有遗传变异。因此,确定从起始序列获取不同序列变异的概率有助于预测进化轨迹和结果。我们将“变体可及性”的概念定义为,在受到自然选择之前,一组编码特定蛋白质功能的基因型通过突变出现的概率。该概率受核苷酸的突变偏性和遗传密码结构的影响。我们以甲型流感病毒为例,讨论了一个更容易获得但适应性较差的变异如何作为一种适应而不是更适合的变异出现。我们描述了一个基因型可及性景观,与基因型适应性景观互补,为起始序列到达基因型空间不同部分的可能性提供信息。该框架为预测病毒和肿瘤等进化系统中适应性基因型的出现奠定了基础。