Protein stability imposes limits on organism complexity and speed of molecular evolution

By Konstantin B. Zeldovich, Peiqiu Chen, Eugene I. Shakhnovich. 2007

266 citations recorded by the source corpus.

Classical population genetics a priori assigns fitness to alleles without considering molecular or functional properties of proteins that these alleles encode. Here we study population dynamics in a model where fitness can be inferred from physical properties of proteins under a physiological assumption that loss of stability of any protein encoded by an essential gene confers a lethal phenotype. Accumulation of mutations in organisms containing Γ genes can then be represented as diffusion within the Γ-dimensional hypercube with adsorbing boundaries determined, in each dimension, by loss of a protein's stability and, at higher stability, by lack of protein sequences. Solving the diffusion equation whose parameters are derived from the data on point mutations in proteins, we determine a universal distribution of protein stabilities, in agreement with existing data. The theory provides a fundamental relation between mutation rate, maximal genome size, and thermodynamic response of proteins to point mutations. It establishes a universal speed limit on rate of molecular evolution by predicting that populations go extinct (via lethal mutagenesis) when mutation rate exceeds approximately six mutations per essential part of genome per replication for mesophilic organisms and one to two mutations per genome per replication for thermophilic ones. Several RNA viruses function close to the evolutionary speed limit, whereas error correction mechanisms used by DNA viruses and nonmutant strains of bacteria featuring various genome lengths and mutation rates have brought these organisms universally ≈1,000-fold below the natural speed limit.

Subfields: Ecology, Genetics, Public Health, Environmental and Occupational Health

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Citations in the corpus

A biophysical protein folding model accounts for most mutational fitness effects in viruses (2011, methodology): “Although this phenomenon is grounded in simple population genetics principles, it clashes with several existing models of neutral networks (18, 21, 31, 32) in which a mutationally connected set of genomes is perfectly fit, whereas another set is completely lethal.”
Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin (2009, methodology): “Given our choice to consider only the case where the population is mostly monomorphic, we will adopt the mathematical formalism described in [31] for the limit when Nm%1 (the more compact diffusion-equation approach of Shakhnovich and coworkers [32] cannot be used since it only applies when Nm&1).”
Latent Effects of Hsp90 Mutants Revealed at Reduced Expression Levels (2013, background): “Under these conditions, selection results in stably-folded proteins [19,20], such that modestly destabilizing mutations can be tolerated without dramatic changes to the fraction of natively folded protein molecules and hence function.”
Predicting evolution (2017, background): “Stabilizing selection, for example, on protein fold stability or catalytic activity, is a general feature of protein evolution, which has been inferred for several microbial and viral systems 54–62 .”
Modeling the temperature effect on the specific growth rate of phytoplankton: a review (2017, background): “In addition to the temperature-dependent enzyme activity, protein thermal stability plays a key role in the microbial thermal growth curve (Johnson and Lewin 1946; Rosenberg et al. 1971; Zeldovich et al. 2007; Pena et al. 2010).”
The Evolution and Emergence of RNA Viruses (2009, background): “Although determining which evolutionary theory correctly explains lethal mutagenesis has proven controversial (Summers and Litwin 1996; Bull et al. 2005, 2007; Zeldovich et al. 2007), there is no doubt that its effects can be remarkable.”
Soluble oligomerization provides a beneficial fitness effect on destabilizing mutations (2011, methodology): “(3) Gibbs free energy difference between folded and unfolded states (ΔG), apparent midtransition temperature of unfolding (Tm app), and catalytic parameters (kcat, Km) were measured (Table 1 and SI Materials and Methods).”
Stability effects of mutations and protein evolvability. (2009, methodology): “Thus, although the initial stability of a protein can buffer some of the destabilizing effects of mutations (Figure 1a), stability appears to comprise the main factor (although clearly not the only one [6]) that dictates the rate of protein evolution [1 ,4 ], and possibly of whole organisms [14 ,15,”
Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics (2014, methodology): “This distribution is approximately a Gaussian with mean DD G mean = 1 kcal/mol and standard deviation DD G sd = 1.7 kcal/mol [9].”
Selection for Protein Stability Enriches for Epistatic Interactions (2018, background): “Our simulations (Figure 1) recapitulate the known qualitative features of protein evolution under 246 purifying selection for folding stability to a surprising degree, with the exception of matching the 247 observed stability margin, which is smaller in our simulations than for experimentally measur”