Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Open Badges Research article

Automatic classification of protein structures relying on similarities between alignments

Guillaume Santini1*, Henry Soldano12 and Joël Pothier2

Author affiliations

1 , Université Paris 13, Sorbonne Paris Cité, Laboratoire d’Informatique de Paris-Nord (LIPN), CNRS(, UMR 7030), Villetaneuse, F-93430, France

2 UPMC, Université Paris 06, Atelier de BioInformatique, F-75005 Paris, France

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2012, 13:233  doi:10.1186/1471-2105-13-233

Published: 14 September 2012



Identification of protein structural cores requires isolation of sets of proteins all sharing a same subset of structural motifs. In the context of an ever growing number of available 3D protein structures, standard and automatic clustering algorithms require adaptations so as to allow for efficient identification of such sets of proteins.


When considering a pair of 3D structures, they are stated as similar or not according to the local similarities of their matching substructures in a structural alignment. This binary relation can be represented in a graph of similarities where a node represents a 3D protein structure and an edge states that two 3D protein structures are similar. Therefore, classifying proteins into structural families can be viewed as a graph clustering task. Unfortunately, because such a graph encodes only pairwise similarity information, clustering algorithms may include in the same cluster a subset of 3D structures that do not share a common substructure. In order to overcome this drawback we first define a ternary similarity on a triple of 3D structures as a constraint to be satisfied by the graph of similarities. Such a ternary constraint takes into account similarities between pairwise alignments, so as to ensure that the three involved protein structures do have some common substructure. We propose hereunder a modification algorithm that eliminates edges from the original graph of similarities and gives a reduced graph in which no ternary constraints are violated. Our approach is then first to build a graph of similarities, then to reduce the graph according to the modification algorithm, and finally to apply to the reduced graph a standard graph clustering algorithm. Such method was used for classifying ASTRAL-40 non-redundant protein domains, identifying significant pairwise similarities with Yakusa, a program devised for rapid 3D structure alignments.


We show that filtering similarities prior to standard graph based clustering process by applying ternary similarity constraints i) improves the separation of proteins of different classes and consequently ii) improves the classification quality of standard graph based clustering algorithms according to the reference classification SCOP.