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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Volume 10, Issue 1 (Jan 2011)

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Log-Linear Modelling of Protein Dipeptide Structure Reveals Interesting Patterns of Side-Chain-Backbone Interactions

Kerstin Hommola / Walter R. Gilks / Kanti V. Mardia
Published Online: 2011-01-25 | DOI: https://doi.org/10.2202/1544-6115.1579

It has long been known that the amino-acid sequence of a protein determines its 3-dimensional structure, but accurate ab initio prediction of structure from sequence remains elusive. We gain insight into local protein structure conformation by studying the relationship of dihedral angles in pairs of residues in protein sequences (dipeptides). We adopt a contingency table approach, exploring a targeted set of hypotheses through log-linear modelling to detect patterns of association of these dihedral angles in all dipeptides considered. Our models indicate a substantial association of the side-chain conformation of the first residue with the backbone conformation of the second residue (side-to-back interaction) as well as a weaker but still significant association of the backbone conformation of the first residue with the side-chain conformation of the second residue (back-to-side interaction). To compare these interactions across different dipeptides, we cluster the parameter estimates for the corresponding association terms. This reveals a striking pattern. For the side-to-back term, all dipeptides which have the same first residue jointly appear in distinct clusters whereas for the back-to-side term, we observe a much weaker pattern. This suggests that the conformation of the first residue affects the conformation of the second.

This article offers supplementary material which is provided at the end of the article.

Keywords: amino-acid sequence; bioinformatics; circular variables; cluster analysis; contingency table; dipeptide; protein structure; side-chain interactions

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Published Online: 2011-01-25


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1579.

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©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

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