The SuMo server: 3D search for protein functional sites

Jambon, Martin; Andrieu, Olivier; Combet, Christophe; Deléage, Gilbert; Delfaud, François; Geourjon, Christophe

doi:10.1093/bioinformatics/bti645

Cited by 82 publications

(65 citation statements)

References 9 publications

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“…For example, no structural or functional similarities are detected between an actin protein bound to ATP (PDB code: 1S22) and a myosin protein bound to ATP (PDB code: 1FMW). 60,61,63,64 The classification tool MED-SMA was implemented to use this ability to classify datasets of binding sites. 63 It operates through three main steps: (i) comparison of all the binding sites of a dataset using a pairwise comparison system, (ii) detection of matching regions in the binding sites to build a similarity graph, and (iii) classification of this graph with the Markov clustering algorithm (MCL).…”

Section: Resultsmentioning

confidence: 99%

“…16,60,61,64 Its main advantage is to detect binding sites with similar or related binding modes which could not be identified using rigid (or even flexible) superimposition approaches. Its heuristic is based on a 3D representation of macromolecule structures using precise Structural Chemical Features (SCFs).…”

Section: Med-sumo Algorithmmentioning

confidence: 99%

“…The full surface database which contains whole surfaces of the protein structures and the MED-Portions database also containing small protein regions but where characterized by chemical fragments detected in the ligands or peptides from the PDB. 16,17 The original version of SuMo is available on the internet, 61 but the latest improvements are only included in the MED-SuMo software distributed by MEDIT SA. 62 These improvements concern the definition and conception of protein databases and as well as the heuristic itself.…”

Section: Med-sumo Algorithmmentioning

confidence: 99%

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Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Doppelt-Azeroual

Delfaud²,

Moriaud³

et al. 2010

Protein Science

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Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.

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Section: Resultsmentioning

confidence: 99%

Section: Med-sumo Algorithmmentioning

confidence: 99%

Section: Med-sumo Algorithmmentioning

confidence: 99%

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Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Doppelt-Azeroual

Delfaud²,

Moriaud³

et al. 2010

Protein Science

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“…Protein function annotation using local structure features is known to be more accurate than using only sequence or structure alignments [1]. The following methods have been proposed for finding local structural motifs in protein families, and known functional sites in protein structures: -Depth-first search starting from simple geometric patterns such as triangles, and progressively finding larger patterns [27,29,8,17]. -Geometric hashing can compare two protein struc-tures [24] or a structure to a database [6].…”

Section: Related Workmentioning

confidence: 99%

Functional Neighbors: Inferring Relationships between Non-Homologous Protein Families Using Family-Specific Packing Motifs

Bandyopadhyay

Huan

Liu

et al. 2008

2008 IEEE International Conference on Bioinformatics and Biomedicine

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We describe a new approach for inferring the functional relationships between non-homologous protein families by looking at statistical enrichment of alternative function predictions in classification hierarchies such as Gene Ontology (GO) and Structural Classification of Proteins (SCOP). Protein structures are represented by robust graphs, and the Fast Frequent Subgraph Mining algorithm is applied to protein families to generate sets of family-specific packing motifs, i.e. amino acid residue packing patterns shared by most family members but infrequent in other proteins. The function of a protein is inferred by identifying in it motifs characteristic of a known family. We employ these familyspecific motifs to elucidate functional relationships between families in the GO and SCOP hierarchies. Specifically, we postulate that two families are functionally related if one family is statistically enriched by motifs characteristic of another family, i.e. if the number of proteins in a family containing a motif from another family is greater than expected by chance. This function inference method can help annotate proteins of unknown function, establish functional neighbors of existing families, and help specify alternate functions for known proteins.

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“…A variety of techniques exist for protein similarity searching, ranging in computational complexity from global structural superposition methods [1] to more complex substructure or fingerprint searches. [2][3][4][5] The ProBiS algorithm of Konc and Janezic [6] belongs to the latter class of algorithms and detects pairwise local similarities in proteins by computing the similarity of protein graphs, which are representations of specific proteins. It runs in the ProBiS web server [7] at http:// probis.cmm.ki.si and the parallel version was used to create the ProBiS-Database, [8] a repository of over 420 million precalculated binding site similarities and local pairwise alignments of protein database (PDB) structures at http://probis.cmm.ki.si/ database.…”

Section: Introductionmentioning

confidence: 99%

Parallel‐ProBiS: Fast parallel algorithm for local structural comparison of protein structures and binding sites

et al. 2012

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The ProBiS algorithm performs a local structural comparison of the query protein surface against the nonredundant database of protein structures. It finds proteins that have binding sites in common with the query protein.Here, we present a new parallelized algorithm, Parallel-ProBiS, for detecting similar binding sites on clusters of computers. The obtained speedups of the parallel ProBiS scale almost ideally with the number of computing cores up to about 64 computing cores. Scaling is better for larger than for smaller query proteins. For a protein with almost 600 amino acids, the maximum speedup of 180 was achieved on two interconnected clusters with 248 computing cores. Source code of Parallel-ProBiS is available for download free for academic users at

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The SuMo server: 3D search for protein functional sites

Abstract: mjambon@burnham.org.

Cited by 82 publications

References 9 publications

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Functional Neighbors: Inferring Relationships between Non-Homologous Protein Families Using Family-Specific Packing Motifs

Parallel‐ProBiS: Fast parallel algorithm for local structural comparison of protein structures and binding sites

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