198 CATALYZING INQUIRY
virus vectors was modeled, each with different transmission efficiencies, rather than assuming a single
vector. The inclusion of such features, cou pled with exploitation of a wealth of data available on this
system, allowed Dwyer et al. to investigate questions that could not be addressed in the earlier model.
These questions included whether the system will continue to evolve antagonistically and whether the
virus will be able to control the rabbit population in the future.
More broadly, this example illustrates the important lesson that both time scales are equally signifi-
cant from an evolutionary perspective, and one is not more “fundamental” than the other when it comes
to understanding the dynamical behavior of the system. Furthermore, it demonstrates that pressures for
natural selection can operate at many different levels of complexity.
5.4.8.2.3 The Evolution of Proteins By making use of simple physical models of proteins, it is possible
to model evolution under different evolutionary, structural, and functional scenarios. For example,
cubic lattice models of proteins can be used to model enzyme evolution involving binding to two
hydrophobic substrates. Gene duplication coupled to subfunctionalization can be used to predict en-
zyme gene duplicate retention patterns and compare with genomic data.^113 This type of physical mod-
eling can be expanded to other evolutionary models, including those that incorporate positive selective
pressures or that vary population genetic parameters. At a structural level, they can be used to address
issues of protein surface-area-to-volume ratios or the evolvability of different folds. Ultimately, such
models can be extended to real protein shapes and can be correlated to the evolution of different folds
in real genomes.^114
The role of structure in evolution during potentially adaptive periods can also be analyzed. A
subset of positive selection will be dictated by structural parameters and intramolecular coevolution.
Common interactions, like RKDE ionic interactions can be detected in this manner. Similarly, less
common interactions, like cation-p interactions, can also be detected and the interconversion between
different modes of interactions can be assessed statistically.
One important tool underlying these efforts is the Adaptive Evolution Database (TAED), a phyloge-
netically organized database that gathers information related to coding sequence evolution.^115 This
database is designed to both provide high-quality gene families with multiple sequence alignments and
phylogenetic trees for chordates and embryophytes and to enable answers to the question, “What
makes each species unique at the molecular genomic level?”
Starting with GenBank, genes have been grouped into families, and multiple sequence alignments
and phylogenetic trees have been calculated. In addition to multiple sequence alignments and phyloge-
netic trees for all families of chordate and embryophyte sequences, TAED includes the ratio of
nonsynonymous to synonymous nucleotide substitution rates (Ka/Ks) for each branch of every phyloge-
netic tree. This ratio, when significantly greater than 1, is an indicator of positive selection and poten-
tially a change of function of the encoded protein in closely related species, and has been useful in the
construction of phylogenetic trees with probabilistic reconstructed ancestral sequences calculated using
both parsimony and maximum likelihood approaches. With a mapping of gene tree to species tree, the
branches whose ratio is significantly greater than 1 are collated together in a phylogenetic context.
(^113) F.N. Braun and D.A. Liberles, “Retention of Enzyme Gene Duplicates by Subfunctionalization;” International Journal of
Biological Macromolecules 33(1-3):19-22, 2003.
(^114) H. Hegyi, J. Lin, D. Greenbaum, and M. Gerstein, “Structural Genomics Analysis: Characteristics of Atypical, Common, and
Horizontally Transferred Folds,” Proteins 47(2):126-141, 2002.
(^115) D.A. Liberles, “Evaluation of Methods for Determination of a Reconstructed History of Gene Sequence Evolution.” Molecu-
lar Biology and Evolution 18(11):2040-2047, 2001; D.A. Liberles, D.R. Schreiber, S. Govindarajan, S.G. Chamberlin, and S.A. Benner,
“The Adaptive Evolution Database (TAED),” Genome Biology 2(8):research0028.1-0028.6, 2001; C. Roth, M.J. Betts, P. Steffansson,
G. Sælensminde, and D.A. Liberles, “The Adaptive Evolution Database (TAED): A Phylogeny-based Tool for Comparative
Genomics,” Nucleic Acids Research 33(Database issue):D495-D497, 2005.