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The biological functions of proteins are ultimately governed by the dynamical properties of their large conformational transitions rooted on multidimensional free energy landscapes. Straightforward molecular dynamics simulation provides a mechanistically detailed picture of conformational transitions, but is hampered by the long timescales of these processes, which are rare events compared to the molecular timescales. In order to overcome these difficulties, we present in this thesis a new method, path-metadynamics, for the study of rare events. Path-metadynamics aims to explore high dimensional free energy landscapes and determine local likely transition pathways. The formalism works within the framework of a history-dependent bias potential applied to a flexible path-variable. Control of the sampling of the orthogonal modes recovers the average path and the minimum free energy path as limiting cases. Simultaneously the bias potential estimates the free energy profile along the path. The method has trivial scaling with the number of order parameters and this makes it suitable for the study of complex transitions. We have applied path-metadynamics to investigate the partial unfolding of a relevant photoreceptor, the photoactive yellow protein, and the formation/dissociation mechanism of a coiled coil, the leucine zipper domain. Our results demonstrate that path-metadynamics enables the calculation of rate constants, the localization of transition states, and the mapping of the free energy along a transition path described on a high-dimensional space. The likely transition paths obtained provide unique molecular insight about the protein conformational changes investigated. This approach opens a new way for studying complex rare events transitions.