hypothesis for protein folding. Based on this hypothesis, we have designed a new

nearest-neighbor method for the prediction of protein secondary structures, in

which the reliability of each prediction is estimated based on the sequence

conservation and clustering in the databases of known structures. We have found

that the predictions with higher reliability scores were indeed correlated with

higher prediction accuracy. We also found that by selecting the top 20% of

residues based on reliability scores as nucleation residues, a clear pattern

emerged where hydrophobic amino acids were largely buried and hydrophilic amino

acids were more exposed. This was consistent with the widely accepted HP-model

for protein folding. These results were shown to be true for several choices of

databases, such as PDBSELECT (<25% sequence homology), SCOP-ASTRAL (<25%

sequence homology), and SCOP-ASTRAL unique fold classes, with 1300, 3956, and

762 proteins, respectively. Therefore, it is conceivable that the nucleation

residues function not only as initiation sites for folding, but also as the core

residues playing a primary role in determining the protein (thermodynamic)

stability. The occurrence of these two functions on one set of amino acid

residues in a protein is perhaps from the result of the biological evolution.

Finally, we have found power law behaviors in our results, whose scaling

properties were modeled using polymer physics and critical phenomena. It is

concluded that proteins behave like a real chain. A new physical picture for

protein folding derived from our nucleation hypothesis has been described, in

which there are two continuous phase transitions corresponding to the two stages

of protein folding: one is nucleation and the other collapse. By determining the

critical exponents of these two phase transitions, it has been found that the

nucleation is a dimensional-3 process, while the collapse a dimensional-2

process.

We will also

discuss a new method of simulating conformations of macromolecules, which

combines MC simulation with molecular dynamics simulation, and its potential

application in simulations of protein folding processes and intermediates as

well as protein structure prediction and protein-protein association. The

advantage of this method is its ability to sample a much larger conformational

space with a reasonable computing power. The method uses a novel representation

of protein structures and it could be applied to RNA and DNA as

well.

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秘书：唐琳