Proteins are biological macromolecules that play essential roles in living organisms. Furthermore, the study of proteins and their function is of interest in other fields in addition to biology, such as pharmacology and biotechnology. Understanding the relationship between protein structure, dynamics and function is indispensable for advances in all these areas. This requires a combination of experimental and computational methods, whose development is the object of very active interdisciplinary research. In such a context, this paper presents a technique to enhance conformational sampling of proteins carried out with computational methods such as molecular dynamics simulations or Monte Carlo methods. Our approach is based on a mechanistic representation of proteins that enables the application of efficient methods originating from robotics. The paper explains the generalities of the approach, and gives details on its application to devise Monte Carlo move classes. Results show the good performance of the method for sampling the conformational space of different types of proteins.