Gō -ing for the prediction of protein folding mechanisms

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Abstract
Protein folding has been a long-lived problem in biophysics. Much important progress has been made in the 90s by focusing on small single-domain proteins (1). In particular, ( i ) site-resolved measurement of the folding transition state ensemble, quantified as φ-values (2), made it possible to understand folding mechanisms relatively unambiguously, stimulating interaction between experimentalists and theoreticians; ( ii ) the energy-landscape theory (3) gave us a general framework based on statistical physics; and ( iii ) the finding of a significant correlation between folding rates and native structure topology (4), which suggested that the native topology is a key determinant of folding mechanisms, all lead us to believe that the underlying physics could be relatively simple. These three ingredients are linked together with an almost one-line free energy equation in three papers (5–7), which appeared in a recent issue of PNAS, as well as some previous work (8, 9). The surprise of the three papers is that apparently one can have both simplicity and fair predictability. Papers by Galzitskaya and Finkelstein (5), Alm and Baker (6), and Muñoz and Eaton (7), which are independent but resemble each other greatly, report that even highly simplified theories based on energy-landscape ideas can predict trends in the folding rates for many fast folding proteins. The calculations of the three papers are easy to summarize. In all three papers, each amino acid (or a few adjacent amino acids grouped) is taken to be either in a native configuration (n) or in a completely random set (r). A reduced protein configuration, or microstate , is represented as a sequence of n and r, such as rrrr-nnnnnn-rrrrrr-nnnnnnn. The (globally) native and denatured states correspond to the microstate in which all amino acids are in n and r, respectively. The authors introduce simple free energies for microstates given the …