Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature

Abstract

The last decade has seen a growing number of experiments aimed at systematically mapping the effects of mutations in different proteins, and of attempting to correlate their biophysical and biochemical effects with organismal fitness. While insightful, systematic laboratory measurements of fitness effects present challenges and difficulties. Here, we discuss the limitations associated with such measurements, and in particular the challenge of correlating the effects of mutations at the single protein level ("protein fitness") with their effects on organismal fitness. A variety of experimental setups are used, with some measuring the direct effects on protein function and others monitoring the growth rate of a model organism carrying the protein mutants. The manners by which fitness effects are calculated and presented also vary, and the conclusions, including the derived distributions of fitness effects of mutations, vary accordingly. The comparison of the effects of mutations in the laboratory to the natural protein diversity, namely to amino acid changes that have fixed in the course of millions of years of evolution, is also debatable. The results of laboratory experiments may, therefore, be less relevant to understanding long-term inter-species variations yet insightful with regard to short-term polymorphism, for example, in the study of the effects of human SNPs.

Mutation effects predicted from sequence co-variation

“…2; Supplementary Table 2). Since relationships between protein function and organismal fitness are not expected to be linear 31 , we focused on reporting rank correlations as the primary metric for evaluating predictive performance, but our results are robust to a variety of measures (Supplementary Table 3). …”

“…However, although high-throughput mutational scans have improved our understanding of the consequences of genetic variation, their relevance to organism fitness and physiology critically depends on the choice of the measured phenotype 29, 31 . It is reasonable that the relationship between a biochemical phenotype and organism fitness may be non-linear 32 and even non-monotonic 31 .…”

“…It is reasonable that the relationship between a biochemical phenotype and organism fitness may be non-linear 32 and even non-monotonic 31 . For the foreseeable future, scans are also limited in their scalability which limits researchers to focus either on a modest number of positions or alleles.…”

Functional Segregation of Overlapping Genes in HIV

“…Second, our analyses have made rough estimations of viral fitness and examined only single point mutations in one viral background in an artificial genomic locus, ignoring the potential for epistatic effects at multiple positions. However, positive epistasis is rare while negative epistasis is common (Bank et al, 2015), suggesting that our simplification is conservative and likely underestimates the severity of purged deleterious genotypes (Boucher et al, 2016), while missing only a few restorative double mutations. Regardless, further studies of overlaps in HIV-1 and other systems may shed more light on these aspects.…”

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).…”