text: The protein folding problem is concerned with three questions, as stated by Ken A. Dill and Justin L. MacCallum: (i) How can an amino acid sequence determine the 3D native structure of a protein? (ii) How can a protein fold so quickly despite a vast number of possible conformations (the Levinthals Paradox)? How does the protein know what conformations not to search? And (iii) is it possible to create a computer algorithm to predict a proteins native structure based on its amino acid sequence alone? Auxiliary factors inside the living cell such as folding catalysts and chaperones assist in the folding process but do not determine the native structure of a protein. Studies during the 1980s focused on models that could explain the shape of the energy landscape, a mathematical function that describes the free energy of a protein as a function of the microscopic degrees of freedom.
  After introducing the term in 1987, Ken A. Dill surveyed the polymer theory in protein folding, in which it addresses two puzzles, the first one being the Blind Watchmakers Paradox in which biological proteins could not originate from random sequences, and the second one being Levinthals Paradox that protein folding cannot happen randomly. Dill pulled the idea from the Blind Watchmaker into his metaphor for protein folding kinetics. 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. That would mean even residues at very different positions in the amino acid sequence will be able to come into contact with each other. Yet, a bias during the folding process can change the folding time by tens to hundreds of orders of magnitude.
  As protein folding process goes through a stochastic search of conformations before reaching its final destination, the vast number of possible conformations is considered irrelevant, while the kinetic traps begin to play a role. The stochastic idea of protein intermediate conformations reveals the concept of an “energy landscape” or "folding funnel" in which folding properties are related to free energy and that the accessible conformations of a protein are reduced as it approaches native-like structure. The y-axis of the funnel represents the "internal free energy" of a protein: the sum of hydrogen bonds, ion-pairs, torsion angle energies, hydrophobic and solvation free energies. The many x-axes represent the conformational structures, and those that are geometrically similar to each other are close to one another in the energy landscape. The folding funnel theory is also supported by Peter G Wolynes, Zaida Luthey-Schulten and Jose Onuchic, that folding kinetics should be considered as progressive organization of partially folded structures into an ensemble (a funnel), rather than a serial linear pathway of intermediates.
  Native states of proteins are shown to be thermodynamically stable structures that exist in physiological conditions, and are proven in experiments with ribonuclease by Christian B. Anfinsen (see Anfinsens dogma). 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. In a native low-energy structure, theres no competition among conflicting energy contributions, leading to a minimal frustration. This notion of frustration is further measured quantitatively in spin glasses, in which the folding transition temperature T is compared to the glass transition temperature T. T represents the native interactions in the folded structure and T represents the strength of non-native interactions in other configurations. A high T/T ratio indicates a faster folding rate in a protein and fewer intermediates compared to others. In a system with high frustration, mild difference in thermodynamic condition can lead to different kinetic traps and landscape ruggedness.