  The understanding, and even the description of protein folding is impeded by
the complexity of the process. Much of this complexity can be described and
understood by taking a statistical approach to the energetics of protein
conformation, that is, to the energy landscape. The statistical energy
landscape approach explains when and why unique behaviors, such as specific
folding pathways, occur in some proteins and more generally explains the
distinction between folding processes common to all sequences and those
peculiar to individual sequences. This approach also gives new, quantitative
insights into the interpretation of experiments and simulations of protein
folding thermodynamics and kinetics. Specifically, the picture provides simple
explanations for folding as a two-state first-order phase transition, for the
origin of metastable collapsed unfolded states and for the curved Arrhenius
plots observed in both laboratory experiments and discrete lattice simulations.
The relation of these quantitative ideas to folding pathways, to
uni-exponential {\em vs.} multi-exponential behavior in protein folding
experiments and to the effect of mutations on folding is also discussed. The
success of energy landscape ideas in protein structure prediction is also
described. The use of the energy landscape approach for analyzing data is
illustrated with a quantitative analysis of some recent simulations, and a
qualitative analysis of experiments on the folding of three proteins. The work
unifies several previously proposed ideas concerning the mechanism protein
folding and delimits the regions of validity of these ideas under different
thermodynamic conditions.
