Today, statisticians are trying to use geometrical information of proteins to model alignment of two or more proteins probabilistically. Although superimposing is based on similarity, the main challenges are in proposing the sensible inferential methods and proper prediction algorithms for structural biomolecules to facilitate identification of the function of a protein. Directional statistics, which exploits statistical modeling in non-Euclidean space, deals with data structures that cannot be modeled with regular statistical methods. For example, optimal superimposition of protein sets on each other by rigid body transformations should be modeled in non-Euclidean shape space for proteins. In doing so, we enrich the Bayesian model for the matching problem in statistical shape analysis by using the geometric and sequence information of proteins (Najibi et. al 2015). We proposed a Delaunay tetrahedralization method using sequence and amino acid type of proteins  as an adaptive empirical prior in our Bayesian model and achieved significant improvement in convergence rate compared to previous statistical models.