Local fitness landscape of the green fluorescent protein

Abstract

Fitness landscapes1,2, depictions of how genotypes manifest at the phenotypic level, form the basis for our understanding of many areas of biology2–7 yet their properties remain elusive. Studies addressing this issue often consider specific genes and their function as proxy for fitness2,4, experimentally assessing the impact on function of single mutations and their combinations in a specific sequence2,5,8–15 or in different sequences2,3,5,16–18. However, systematic high-throughput studies of the local fitness landscape of an entire protein have not yet been reported. Here, we chart an extensive region of the local fitness landscape of the green fluorescent protein from Aequorea victoria (avGFP) by measuring the native function, fluorescence, of tens of thousands of derivative genotypes of avGFP. We find that its fitness landscape is narrow, with half of genotypes with two mutations showing reduced fluorescence and half of genotypes with five mutations being completely non-fluorescent. The narrowness is enhanced by epistasis, which was detected in up to 30% of genotypes with multiple mutations arising mostly through the cumulative impact of slightly deleterious mutations causing a threshold-like decrease of protein stability and concomitant loss of fluorescence. A model of orthologous sequence divergence spanning hundreds of millions of years predicted the extent of epistasis in our data, indicating congruence between the fitness landscape properties at the local and global scales. The characterization of the local fitness landscape of avGFP has important implications for a number of fields including molecular evolution, population genetics and protein design.

Mechanism and bottlenecks in strand photodissociation of split green fluorescent proteins (GFPs)

“…First, the mutant space is constricted (85), and therefore, different photochemical properties might not be independently tunable (86). This limitation was shown by the E222Q mutation, which was predicted to increase the photodissociation quantum yield.…”

A Statistical Guide to the Design of Deep Mutational Scanning Experiments

“…Note however, that DMS studies in which the functional capacity of a (mutant) protein (i.e., the protein fitness) cannot be directly related to organismal fitness (for a recent review on the topic see Boucher et al 2016) do not adhere to the statistical framework presented here. Examples include recent DMS studies, which were based on fluorescence (as in Sarkisyan et al 2016), antibiotic resistance (e.g., Jacquier et al 2013;Firnberg et al 2014), and binding selection using protein display technologies (Fowler et al 2010;Whitehead et al 2012;Olson et al 2014).…”

Physical epistatic landscape of antibody binding affinity

“…For instance, protein stability, which often determines fitness, is a nonlinear function of the folding free energy difference, which is expected to be roughly additive [12,[27][28][29][48][49][50]. This leads to both a law of diminishing returns [29] and robustness to mutations when the protein is very stable [48].…”

“…† Corresponding authors: awalczak@lpt.ens.fr, tmora@lps.ens. fr effects on sequence background [21], and statistical covariation of residues in large sequence datasets [22,23].Intragenic epistasis has mostly been studied either by measuring the fitness of all possible mutational intermediates between two variants [10,[24][25][26][27], or by comparing the effect of mutations in different backgrounds [21,28,29]. Many such studies rely on a particular measure of fitness rather than a well-defined physical phenotype.…”