20,825 research outputs found
Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein
Epistatic interactions between residues determine a protein's adaptability
and shape its evolutionary trajectory. When a protein experiences a changed
environment, it is under strong selection to find a peak in the new fitness
landscape. It has been shown that strong selection increases epistatic
interactions as well as the ruggedness of the fitness landscape, but little is
known about how the epistatic interactions change under selection in the
long-term evolution of a protein. Here we analyze the evolution of epistasis in
the protease of the human immunodeficiency virus type 1 (HIV-1) using protease
sequences collected for almost a decade from both treated and untreated
patients, to understand how epistasis changes and how those changes impact the
long-term evolvability of a protein. We use an information-theoretic proxy for
epistasis that quantifies the co-variation between sites, and show that
positive information is a necessary (but not sufficient) condition that detects
epistasis in most cases. We analyze the "fossils" of the evolutionary
trajectories of the protein contained in the sequence data, and show that
epistasis continues to enrich under strong selection, but not for proteins
whose environment is unchanged. The increase in epistasis compensates for the
information loss due to sequence variability brought about by treatment, and
facilitates adaptation in the increasingly rugged fitness landscape of
treatment. While epistasis is thought to enhance evolvability via
valley-crossing early-on in adaptation, it can hinder adaptation later when the
landscape has turned rugged. However, we find no evidence that the HIV-1
protease has reached its potential for evolution after 9 years of adapting to a
drug environment that itself is constantly changing.Comment: 25 pages, 9 figures, plus Supplementary Material including
Supplementary Text S1-S7, Supplementary Tables S1-S2, and Supplementary
Figures S1-2. Version that appears in PLoS Genetic
Formation of regulatory modules by local sequence duplication
Turnover of regulatory sequence and function is an important part of
molecular evolution. But what are the modes of sequence evolution leading to
rapid formation and loss of regulatory sites? Here, we show that a large
fraction of neighboring transcription factor binding sites in the fly genome
have formed from a common sequence origin by local duplications. This mode of
evolution is found to produce regulatory information: duplications can seed new
sites in the neighborhood of existing sites. Duplicate seeds evolve
subsequently by point mutations, often towards binding a different factor than
their ancestral neighbor sites. These results are based on a statistical
analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome,
and a comparison set of intergenic regulatory sequence in Saccharomyces
cerevisiae. In fly regulatory modules, pairs of binding sites show
significantly enhanced sequence similarity up to distances of about 50 bp. We
analyze these data in terms of an evolutionary model with two distinct modes of
site formation: (i) evolution from independent sequence origin and (ii)
divergent evolution following duplication of a common ancestor sequence. Our
results suggest that pervasive formation of binding sites by local sequence
duplications distinguishes the complex regulatory architecture of higher
eukaryotes from the simpler architecture of unicellular organisms
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
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