Proteins take an essential place in live-organisms functioning. Without proteins in the body human would not be able to move his muscles, defend against infections, or even process emotions. The ability of proteins to carry out its functions depends on its spatial shape and the way this shape changes. Analyze proteins motions is not an easy task. There are several experimental ways to identify a protein's conformation; the most widely used are X-ray crystallography and NMR spectroscopy. These methods are extremely time consuming, require very high precision equipment, and at the end they just give a snapshot of one particular protein conformation; they can not capture protein dynamics. Therefore, there is a huge need in computational simulations for modeling proteins motions. Proteins are very high dimensional structures, which makes it extremely computationally expensive to simulate their motions using standard physical approaches, such as Molecular Dynamics. Current work represents a sampling-based technique for modeling a protein motion inspired by the field of robotic motion planning.