A goal of biology is to predict how mutations combine to alter phenotypes, fitness and disease. It is often assumed that mutations combine additively or with interactions that can be predicted. Here, we show using simulations that, even for the simple example of the lambda phage transcription factor CI repressing a gene, this assumption is incorrect and that perfect measurements of the effects of mutations on a trait and mechanistic understanding can be insufficient to predict what happens when two mutations are combined. This apparent paradox arises because mutations can have different biophysical effects to cause the same change in a phenotype and the outcome in a double mutant depends upon what these hidden biophysical changes actually are. Pleiotropy and non-monotonic functions further confound prediction of how mutations interact. Accurate prediction of phenotypes and disease will sometimes not be possible unless these biophysical ambiguities can be resolved using additional measurements.

RNA virus high mutation rate is a double-edged sword. At the one side, most mutations jeopardize proteins functions; at the other side, mutations are needed to fuel adaptation. The relevant question then is the ratio between beneficial and deleterious mutations. To evaluate this ratio, we created a mutant library of the 6K2 gene of tobacco etch potyvirus that contains every possible single-nucleotide substitution. 6K2 protein anchors the virus replication complex to the network of endoplasmic reticulum membranes. The library was inoculated into the natural host Nicotiana tabacum , allowing competition among all these mutants and selection of those that are potentially viable. We identified 11 nonsynonymous mutations that remain in the viral population at measurable frequencies and evaluated their fitness. Some had fitness values higher than the wild-type and some were deleterious. The effect of these mutations in the structure, transmembrane properties, and function of 6K2 was evaluated in silico. In parallel, the effect of these mutations in infectivity, virus accumulation, symptoms development, and subcellular localization was evaluated in the natural host. The α-helix H1 in the N-terminal part of 6K2 turned out to be under purifying selection, while most observed mutations affect the link between transmembrane α-helices H2 and H3, fusing them into a longer helix and increasing its rigidity. In general, these changes are associated with higher within-host fitness and development of milder or no symptoms. This finding suggests that in nature selection upon 6K2 may result from a tradeoff between within-host accumulation and severity of symptoms.

A central challenge in genetics, evolutionary biology and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploid organisms, two mutations in the same gene can either combine on the same chromosome or on different chromosomes, with interactions between the mutations quantified as epistasis and dominance, respectively. However, a direct comparison of the extent, sign and stability of interactions within and between alleles is lacking. Here we show that, even in the simplest biophysical systems, interactions between mutations are frequent, context-dependent and different when variants are combined within and between alleles. Whereas protein folding alone generates epistasis, the addition of a single molecular interaction is sufficient to cause dominance. Epistasis and dominance interactions change quantitatively, qualitatively and differently as a system becomes more complicated or the conditions change. Altering the concentration of a ligand can, for example, switch an allele from dominant to recessive. Our results show that epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.