In protein structure prediction, backbone fragment bias information can narrow down the conformational space of the whole polypeptide chain significantly. Unlike existing methods that use fragments as building blocks, the paper presents a probabilistic sampling approach for protein backbone torsion angles by modelling angular correlation of (phi, psi) with a directional statistics distribution. Given a protein sequence and secondary structure information, this method samples backbone fragments conformations by using a backtrack sampling algorithm for the hidden Markov model with multiple inputs and a single output. The proposed approach is applied to a fragment library, and some well-known structural motifs are sampled very well on the optimal path. Computational results show that the method can help to obtain native-like backbone fragments conformations.