Bayesian comparison of protein structures using partial Procrustes distance

An important topic in bioinformatics is the protein structure alignment. Some statistical methods have been proposed for this problem, but most of them align two protein structures based on the global geometric information without considering the effect of neighbourhood in the structures. In this paper, we provide a Bayesian model to align protein structures, by considering the effect of both local and global geometric information of protein structures. Local geometric information is incorporated to the model through the partial Procrustes distance of small substructures. These substructures are composed of β-carbon atoms from the side chains. Parameters are estimated using a Markov chain Monte Carlo (MCMC) approach. We evaluate the performance of our model through some simulation studies. Furthermore, we apply our model to a real dataset and assess the accuracy and convergence rate. Results show that our model is much more efficient than previous approaches.

Probabilistic Model for Robust Affine and Non-Rigid Point Set Matching

In this work, we propose a combinative strategy based on regression and clustering for solving point set matching problems under a Bayesian framework, in which the regression estimates the transformation from the model to the sceneand the clustering establishes the correspondence between two point sets. The point set matching model is illustrated by a hierarchical directed graph, and the matching uncertainties are approximated by a coarse-to-fine variational inference algorithm. Furthermore, two Gaussian mixtures are proposed for the estimation of heteroscedastic noise and spurious outliers, and an isotropic or anisotropic covariance can be imposed on each mixture in terms of the transformed model points. The experimental results show that the proposed approach achieves comparable performance to state-of-the-art matching or registration algorithms in terms of both robustness and accuracy.