Growing databases of protein sequences in the post-genomic era call for computational methods to extract structure and function from a protein sequence. In flexible molecules like proteins, function cannot be reliably extracted from a few structures. The amino-acid chain assumes various spatial arrangements (conformations) to modulate biological function. Characterizing the flexibility of a protein under physiological (native) conditions remains an open problem in computational biology. This thesis addresses the problem of characterizing the native flexibility of a protein by computing conformations populated under native conditions. Such computation involves locating free-energy minima in a high-dimensional conformational space. The methods proposed in this thesis search for native conformations using systematically less information from experiment: first employing an experimental structure, then using only a closure constraint in cyclic cysteine-rich peptides, and finally employing only the amino-acid sequence of small- to medium-size proteins. A novel method is proposed to compute structural fluctuations of a protein around an experimental structure. The method combines a robotics-inspired exploration of the conformational space with a statistical mechanics formulation. Thermodynamic quantities measured over generated conformations reproduce experimental data of broad time scales on small (∼ 100 amino acids) proteins with non-concerted motions. Capturing concerted motions motivates the development of the next methods. A second method is proposed that employs a closure constraint to generate native conformations of cyclic cysteine-rich peptides. The method first explores the entire conformational space, then explores in present energy minima until no lower-energy minima emerge. The method captures relevant features of the native state also observed in experiment for 20–30 amino-acid long peptides. A final method is proposed that implements a similar exploration but for longer proteins and employing only amino-acid sequence. In its first stage, the method explores the entire conformational space at a coarse-grained level of detail. A second stage focuses the exploration to low-energy regions in more detail. All-atom conformational ensembles are obtained for proteins that populate various functional states through large-scale concerted motions. These ensembles capture well the populated functional states of proteins up to 214 amino-acids long.