Over the last 10-15 years a general understanding of the chemical reaction of
protein folding has emerged from statistical mechanics. The lessons learned
from protein folding kinetics based on energy landscape ideas have benefited
protein structure prediction, in particular the development of coarse grained
models. We survey results from blind structure prediction. We explore how
second generation prediction energy functions can be developed by introducing
information from an ensemble of previously simulated structures. This procedure
relies on the assumption of a funnelled energy landscape keeping with the
principle of minimal frustration. First generation simulated structures provide
an improved input for associative memory energy functions in comparison to the
experimental protein structures chosen on the basis of sequence alignment