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Statistical inference of protein structural alignments using information and compression

James H. Collier, Lloyd Allison, Arthur M. Lesk, Peter J. Stuckey, Maria Garcia de la Banda, Arun S. Konagurthu
doi: https://doi.org/10.1101/056598
James H. Collier
1Faculty of Information Technology, Monash University, Clayton, VIC 3800 Australia
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Lloyd Allison
1Faculty of Information Technology, Monash University, Clayton, VIC 3800 Australia
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Arthur M. Lesk
2Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802 USA
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Peter J. Stuckey
3Department of Computing and Information Systems, University of Melbourne, Parkville, VIC 3010 Australia
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Maria Garcia de la Banda
1Faculty of Information Technology, Monash University, Clayton, VIC 3800 Australia
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Arun S. Konagurthu
1Faculty of Information Technology, Monash University, Clayton, VIC 3800 Australia
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  • For correspondence: arun.konagurthu@monash.edu
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Abstract

Structural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates.) Despite its importance, the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. To overcome these difficulties, we present here a framework for precise inference of structural alignments, built on the Bayesian and information-theoretic principle of Minimum Message Length (MML). The quality of any alignment is measured by its explanatory power - the amount of lossless compression achieved to explain the protein coordinates using that alignment. We have implemented this approach in the program MMLigner http://lcb.infotech.monash.edu.au/mmligner to distinguish statistically significant alignments, not available elsewhere. We also demonstrate the reliability of MMLigner’s alignment results compared with the state of the art. Importantly, MMLigner can also discover different structural alignments of comparable quality, a challenging problem for oligomers and protein complexes.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 02, 2016.
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Statistical inference of protein structural alignments using information and compression
James H. Collier, Lloyd Allison, Arthur M. Lesk, Peter J. Stuckey, Maria Garcia de la Banda, Arun S. Konagurthu
bioRxiv 056598; doi: https://doi.org/10.1101/056598
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Statistical inference of protein structural alignments using information and compression
James H. Collier, Lloyd Allison, Arthur M. Lesk, Peter J. Stuckey, Maria Garcia de la Banda, Arun S. Konagurthu
bioRxiv 056598; doi: https://doi.org/10.1101/056598

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