  We propose an application of molecular information theory to analyze the
folding of single domain proteins. We analyze results from various areas of
protein science, such as sequence-based potentials, reduced amino acid
alphabets, backbone configurational entropy, secondary structure content,
residue burial layers, and mutational studies of protein stability changes. We
found that the average information contained in the sequences of evolved
proteins is very close to the average information needed to specify a fold ~2.2
$\pm$ 0.3 bits/(site operation). The effective alphabet size in evolved
proteins equals the effective number of conformations of a residue in the
compact unfolded state at around 5. We calculated an energy-to-information
conversion efficiency upon folding of around 50%, lower than the theoretical
limit of 70%, but much higher than human built macroscopic machines. We propose
a simple mapping between molecular information theory and energy landscape
theory and explore the connections between sequence evolution, configurational
entropy and the energetics of protein folding.
