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Bayesian comparison of protein structures using partial Procrustes distance

Author

Listed:
  • Ejlali Nasim
  • 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)

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.

Suggested Citation

  • Ejlali Nasim & Faghihi Mohammad Reza & Sadeghi Mehdi, 2017. "Bayesian comparison of protein structures using partial Procrustes distance," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(4), pages 243-257, September.
    • Handle: RePEc:bpj:sagmbi:v:16:y:2017:i:4:p:243-257:n:1
    DOI: 10.1515/sagmb-2016-0014
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    References listed on IDEAS

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    1. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    2. Peter J. Green & Kanti V. Mardia, 2006. "Bayesian alignment using hierarchical models, with applications in protein bioinformatics," Biometrika, Biometrika Trust, vol. 93(2), pages 235-254, June.
    3. S.M. Najibi & M.R. Faghihi & M. Golalizadeh & S.S. Arab, 2015. "Bayesian alignment of proteins via Delaunay tetrahedralization," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 1064-1079, May.
    4. Ian L. Dryden & Jonathan D. Hirst & James L. Melville, 2007. "Statistical Analysis of Unlabeled Point Sets: Comparing Molecules in Chemoinformatics," Biometrics, The International Biometric Society, vol. 63(1), pages 237-251, March.
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