Bayesian comparison of protein structures using partial Procrustes distance

Ejlali Nasim (), Faghihi Mohammad Reza and Sadeghi Mehdi
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Ejlali Nasim: Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, G. C. Evin, 1983963113, Tehran, Iran
Faghihi Mohammad Reza: Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, G. C. Evin, 1983963113, Tehran, Iran
Sadeghi Mehdi: National Institute of Genetic Engineering and Biotechnology (NIGEB), Pajoohesh Blvd, 17 Km Tehran-Karaj Highway, P. O. Box 14965/161, Tehran, Iran

Statistical Applications in Genetics and Molecular Biology, 2017, vol. 16, issue 4, 243-257

Abstract: 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.

Keywords: Bayesian structural alignment; Markov chain Monte Carlo; partial Procrustes distance; statistical shape analysis (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/sagmb-2016-0014

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