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Open Access Research article

Dissecting protein loops with a statistical scalpel suggests a functional implication of some structural motifs

Leslie Regad12, Juliette Martin3 and Anne-Claude Camproux12*

Author Affiliations

1 INSERM, U973, Paris F-75013, France

2 Université Paris 7 - Paris Diderot,UMR-S973, MTi, F-75013 Paris, France

3 Université Lyon 1, Univ Lyon, France; CNRS, UMR 5086; Bases Moléculaires et Structurales des Systèmes Infectieux, IBCP 7 passage du vercors, F-69367, France

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BMC Bioinformatics 2011, 12:247  doi:10.1186/1471-2105-12-247

Published: 20 June 2011

Abstract

Background

One of the strategies for protein function annotation is to search particular structural motifs that are known to be shared by proteins with a given function.

Results

Here, we present a systematic extraction of structural motifs of seven residues from protein loops and we explore their correspondence with functional sites. Our approach is based on the structural alphabet HMM-SA (Hidden Markov Model - Structural Alphabet), which allows simplification of protein structures into uni-dimensional sequences, and advanced pattern statistics adapted to short sequences. Structural motifs of interest are selected by looking for structural motifs significantly over-represented in SCOP superfamilies in protein loops. We discovered two types of structural motifs significantly over-represented in SCOP superfamilies: (i) ubiquitous motifs, shared by several superfamilies and (ii) superfamily-specific motifs, over-represented in few superfamilies. A comparison of ubiquitous words with known small structural motifs shows that they contain well-described motifs as turn, niche or nest motifs. A comparison between superfamily-specific motifs and biological annotations of Swiss-Prot reveals that some of them actually correspond to functional sites involved in the binding sites of small ligands, such as ATP/GTP, NAD(P) and SAH/SAM.

Conclusions

Our findings show that statistical over-representation in SCOP superfamilies is linked to functional features. The detection of over-represented motifs within structures simplified by HMM-SA is therefore a promising approach for prediction of functional sites and annotation of uncharacterized proteins.