Session T42: Focus Session: The Physics of Evolution I

Insights into protein evolution landscapes from folding models

Off-lattice models of protein folding were employed to investigate the origins of the evolutionary rate distributions and fitness landscapes. For each robust folder, the network of sequences that share its native structure is identified. The fitness of a sequence is a simple function of the number of misfolded molecules produced to reach a characteristic protein abundance. Fixation probabilities of mutants are computed under a simple population dynamics model, and the fold-averaged evolution rate is computed a using a Markov chain on the fold network. The distribution of the logarithm of the evolution rates exhibits a peak with a long tail on the low rate side and resembles the universal empirical distribution of the evolutionary rates more closely than either distribution resembles the log-normal distribution. We next addressed the question of the extent of determinism in protein evolution. Limited empirical studies suggest that the fitness landscapes of protein evolution are significantly smoother, or more additive, than random landscapes. However, widespread sign epistasis seems to restrict evolution to a small fraction of available trajectories, thus making the evolutionary process substantially deterministic. Access to complete fitness landscapes within the model framework enables exhaustive analysis of evolutionary trajectories. The model landscapes were compared to a continuum of artificial landscapes of varying smoothness. In maximally smooth, fully additive landscapes, evolution cannot be predicted because all paths are accessible. However, a small amount of noise can make most paths inaccessible while preserving the overall structure of the landscape. Although the model landscapes are almost additive, most paths are non-monotonic with respect to fitness, so evolutionary trajectories can be approximately predicted. Thus, protein folding physics seems to dictate the universal distribution of the evolutionary rates of protein-coding genes and the quasi-deterministic character of evolution.   

A mechanism for the evolution of allosteric control in proteins

Co-evolution analysis indicates a general architecture for natural proteins in which sparse networks of physically contiguous amino acids underlie basic aspects of structure and function. These networks, termed sectors, are spatially organized such that active sites are linked to many surface sites distributed throughout the protein structure. Here, we propose that sectors represent an evolutionarily conserved ``wiring'' mechanism that effectively functionalizes a large but non-random fraction of the protein surface - that is, perturbation at sector-connected surface positions should preferentially yield coupling to the active site. To test this premise, we conducted a comprehensive ``domain insertion scan,'' and show that sector-connected surface sites are statistically significant locations for the emergence of allosteric control in vivo. This finding suggests practical guidelines for the engineering of new allosteric systems, and permits description of a plausible model for the evolution of intermolecular communication and regulation.   

Biophysical Aspects of Spindle Evolution

The continual propagation of genetic material from one generation to the next is one of the most basic characteristics of all organisms. In eukaryotes, DNA is segregated into the two daughter cells by a highly dynamic, self-organizing structure called the mitotic spindle. Mitotic spindles can show remarkable variability between tissues and organisms, but there is currently little understanding of the biophysical and evolutionary basis of this diversity. We are studying how spontaneous mutations modify cell division during nematode development. By comparing the mutational variation - the raw material of evolution - with the variation present in nature, we are investigating how the mitotic spindle is shaped over the course of evolution. This combination of quantitative genetics and cellular biophysics gives insight into how the structure and dynamics of the spindle is formed through selection, drift, and biophysical constraints.   

The relationship between relative solvent accessibility and evolutionary rate in protein evolution

Recent work with the yeast Saccharomyces cerevisiae shows a linear relationship between the evolutionary rate of sites and the relative solvent accessibility (RSA) of the corresponding residues in the folded protein. Here, we aim to develop a mathematical model that can reproduce this linear relationship. We first demonstrate that two models that both seem reasonable choices (a simple model in which selection strength correlates with RSA and a more complex model based on RSA-dependent amino-acid distributions) fail to reproduce the observed relationship. We then develop a model based on observed site-specific amino-acid distributions and show that this model behaves appropriately. We conclude that evolutionary rates are directly linked to the distribution of amino acids at individual sites. Because of this link, any future insight into the biophysical mechanisms that determine amino-acid distributions will improve our understanding of evolutionary rates.   

Quantum Darwinian Evolution Implies Tumor Origination

Quantum uncertainty limits operating on metastable amino DNA protons drive the arrangement, keto-amino ? enol-imine, which contributes to time-dependent stochastic mutations. Product enol-imine protons participate in coupled quantum oscillations at frequencies of about 1013 s-1 until ``measured by'' an evolutionarily selected quantum reader, the transcriptase. This introduces entanglement states between coherent protons and transcriptase components, which ultimately yield an ensemble of decohered, non-reequilibrated enol and imine isomers that participate in ``molecular clock'' base substitutions at G'-C' and *G-*C sites. This introduces a quantum Darwinian evolution model which (a) simulates incidence of cancer data and (b) implies insight into quantum origins of evolutionary extinction. Data identify an inherited ``genetic space,'' s, which is initially mutation-free and satisfies the inequality, 1 = s = 0.97. When accumulated stochastic mutations cause s-values to approach their evolutionarily allowed threshold limit, s 0.97 + e, age-related degenerative disease is manifested. This implies a gain in evolutionary advantage which protects the gene pool against acquiring unsafe levels of mutation. Data requiring coherent states imply that classical duplex DNA contains an embedded microphysical subset of electron lone-pairs and hydrogen bonded protons that govern time-dependent genetic specificity in terms of quantum probability laws.