Folding funnel
Although energy landscapes may be "rough", with many non-native local minima in which partially folded proteins can become trapped, the folding funnel hypothesis assumes that the native state is a deep free energy minimum with steep walls, corresponding to a single well-defined tertiary structure.
The molten globule state predicted by the folding funnel theory as an ensemble of folding intermediates thus corresponds to a protein in which hydrophobic collapse has occurred but many native contacts, or close residue-residue interactions represented in the native state, have yet to form.
The theory's name derives from an analogy between the shape of the well and a physical funnel, in which dispersed liquid is concentrated into a single narrow area.
And (iii) is it possible to create a computer algorithm to predict a protein's native structure based on its amino acid sequence alone?
The native state of protein can be achieved through a folding process involving some small bias and random choices to speed up the search time.
It is suggested that because the landscape is encoded by the amino-acid sequence, natural selection has enabled proteins to evolve so that they are able to fold rapidly and efficiently.
[9] Ken A. Dill and Hue Sun Chan (1997)[6] illustrated a folding pathway design based on Levinthal's Paradox, named the "golf-course" landscape, where a random searching for the native states would prove impossible, due to the hypothetically "flat playing field" since the protein "ball" would take a really long time to find a fall into the native "hole".
Thus, an ideal funnel consists of a smooth multi-dimensional energy landscape where increasing interchain contacts correlate with decreasing degree of freedom and ultimately achievement of native state.
For those that are stuck in this trap, they would have to break away favorable contacts that do not lead to their native state before reaching their original starting point and find another different search downhill.
[6] It is different from the rugged landscape since there are no accidental kinetic traps but purposeful ones required for portions of protein to go through before reaching the final state.
On the other hand, the Champagne Glass landscape involves free energy barriers due to conformational entropy that partly resembles the random golf-course pathway in which a protein chain configuration is lost and has to spend time searching for the path downhill.
This situation can be applied to a conformational search of polar residues that will eventually connect two hydrophobic clusters.
Visualization of funnels creates a communicating tool between statistical mechanical properties of proteins and their folding kinetics.
[5] In contrast, funnel models aim to explain the kinetics in terms of underlying physical forces, to predict the microstate composition of those macrostates.
Nonetheless, it proves challenging for computer simulations (energy landscape) to reconcile the "macroscopic" view of mass-action models with "microscopic" understanding of the changes in protein conformation during the folding process.
