Journal Article

Understanding hydrogen-bond patterns in proteins using network motifs

Ofer Rahat, Uri Alon, Yaakov Levy and Gideon Schreiber

in Bioinformatics

Volume 25, issue 22, pages 2921-2928
Published in print November 2009 | ISSN: 1367-4803
Published online September 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp541
Understanding hydrogen-bond patterns in proteins using network motifs

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Summary: Protein structures can be viewed as networks of contacts (edges) between amino-acid residues (nodes). Here we dissect proteins into sub-graphs consisting of six nodes and their corresponding edges, with an edge being either a backbone hydrogen bond (H-bond) or a covalent interaction. Six thousand three hundred and twenty-two such sub-graphs were found in a large non-redundant dataset of high-resolution structures, from which 35 occur much more frequently than in a random model. Many of these significant sub-graphs (also called network motifs) correspond to sub-structures of α helices and β-sheets, as expected. However, others correspond to more exotic sub-structures such as 310 helix, Schellman motif and motifs that were not defined previously. This topological characterization of patterns is very useful for producing a detailed differences map to compare protein structures. Here we analyzed in details the differences between NMR, molecular dynamics (MD) simulations and X-ray structures for Lysozyme, SH3 and the lambda repressor. In these cases, the same structures solved by NMR and simulated by MD showed small but consistent differences in their motif composition from the crystal structures, despite a very small root mean square deviation (RMSD) between them. This may be due to differences in the pair-wise energy functions used and the dynamic nature of these proteins.

Availability: A web-based tool to calculate network motifs is available at http://bioinfo.weizmann.ac.il/protmot/.

Contact: gideon.schreiber@weizmann.ac.il; koby.levy@weizmann.ac.il

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6176 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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