The use of free-energy landscapes rationalizes a wide range of aspects of protein behavior by providing a clear illustration of the different states accessible to these molecules, as well as of their populations and pathways of interconversion. The determination of the free-energy landscapes of proteins by computational methods is, however, very challenging as it requires an extensive sampling of their conformational spaces. We describe here a technique to achieve this goal with relatively limited computational resources by incorporating nuclear magnetic resonance (NMR) chemical shifts as collective variables in metadynamics simulations. As in this approach the chemical shifts are not used as structural restraints, the resulting free-energy landscapes correspond to the force fields used in the simulations. We illustrate this approach in the case of the third Ig-binding domain of protein G from streptococcal bacteria (GB3). Our calculations reveal the existence of a folding intermediate of GB3 with nonnative structural elements. Furthermore, the availability of the free-energy landscape enables the folding mechanism of GB3 to be elucidated by analyzing the conformational ensembles corresponding to the native, intermediate, and unfolded states, as well as the transition states between them. Taken together, these results show that, by incorporating experimental data as collective variables in metadynamics simulations, it is possible to enhance the sampling efficiency by two or more orders of magnitude with respect to standard molecular dynamics simulations, and thus to estimate free-energy differences among the different states of a protein with a k B T accuracy by generating trajectories of just a few microseconds.NMR spectroscopy | protein folding | protein structure determination | bias-exchange metadynamics | enhanced sampling I n the past two decades, a series of experimental and theoretical advances has made it possible to obtain a detailed understanding of the molecular mechanisms underlying the folding process (1-6). With the increasing power of computers (7), as well as the improvements in force fields (8, 9), atomistic simulations are also becoming increasingly important because they can generate highly detailed descriptions of the motions of proteins (10-12). A supercomputer specifically designed to integrate Newton's equations of motion of proteins (7) recently broke the millisecond time barrier. This achievement has allowed the direct calculation of repeated folding events for several fastfolding proteins (13) and the characterization of molecular mechanisms underlying protein dynamics and function (14). Reliable descriptions of the folding process have also been obtained by exploiting enhanced sampling techniques (15, 16), including replica-exchange molecular dynamics (17), metadynamics (18, 19), and distributed computing (20).It has also been realized that by bringing together experimental measurements and computational methods, it is possible to expand the range of problems that may be addressed (4,...