Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations

Abstract

Bridging the gap between the molecular properties of proteins and organismal/population fitness is essential for understanding evolutionary processes. This task requires the integration of the several physical scales of biological organization, each defined by a distinct set of mechanisms and constraints, into a single unifying model. The molecular scale is dominated by the constraints imposed by the physico-chemical properties of proteins and their substrates, which give rise to trade-offs and epistatic (non-additive) effects of mutations. At the systems scale, biological networks modulate protein expression and can either buffer or enhance the fitness effects of mutations. The population scale is influenced by the mutational input, selection regimes, and stochastic changes affecting the size and structure of populations, which eventually determine the evolutionary fate of mutations. Here, we summarize the recent advances in theory, computer simulations, and experiments that advance our understanding of the links between various physical scales in biology.

Figure 1. Biophysics as a stepping stone between sequence and phenotype

Closing the genotype-phenotype gap is facilitated by an intermediate projection of organismal fitness to biophysical properties of macromolecules. While the effect of sequence variation (panel a) on molecular traits (e.g. folding stability, binding affinity, catalytic activity, panel b) might be complex, the ensuing relation between variation of the observable biophysical traits (vertical axes in panel b) and their fitness effect on the organism might be simple and predictable in some cases (panel d and Figure 2). The effects of mutations are also modulated by a regulation of biological networks (panel c) that might also have simple integrative effect on fitness. On the level of populations, the probability of fixing a specific mutation is a function of the effect of a mutation (selection coefficient) and an effective population size (N_e). Note a different interpretation of fitness landscape in (panel d) from classical Wright’s where mean fitness of the population vs allele frequencies is usually plotted.

Figure 2. Experimentally derived biophysical fitness landscape in viruses and microorganisms

A, Fitness of influenza from strain Aichi/1968 to strain Brisbane/2007 is mapped to the thermal stability of its neuraminidase domain [41••]. Viral fitness is defined as the number of particles that are able to productively infect cells and transcribe high levels of GFP from viral RNA. B, Fitness of E. coli (defined as growth rate) is mapped to the effective functional capacity (Abundance*Kcat/Km) of dihydrofolate reductase, a core metabolic enzyme. Points correspond to strains where the chromosomal copy of DHFR was replaced with an ortholog to mimic xenologous horizontal gene transfer [73••]. C, Fitness of E. coli under antibiotic resistance (defined as IC50) is mapped to the rate of DHFR catalyzed reaction. Points correspond to strains/conditions where the E. coli DHFR has been mutated and/or its abundance has been titrated [4••].