APS March Meeting 2011
Volume 56, Number 1
Monday–Friday, March 21–25, 2011;
Dallas, Texas
Session T42: Focus Session: The Physics of Evolution I
2:30 PM–4:18 PM,
Wednesday, March 23, 2011
Room: A302/303
Sponsoring
Units:
DCP DBP
Chair: Eugene Shakhnovich, Harvard University
Abstract ID: BAPS.2011.MAR.T42.1
Abstract: T42.00001 : Insights into protein evolution landscapes from folding models
2:30 PM–3:06 PM
Preview Abstract
Abstract
Author:
Eugene Koonin
(National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health)
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.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2011.MAR.T42.1