Joël Pothier

YAKUSA: A fast structural database scanning method

YAKUSA is a program designed for rapid scanning of a structural database with a query protein structure. It searches for the longest common substructures called SHSPs (structural high‐scoring pairs) existing between a query structure and every structure in the structural database. It makes use of protein backbone internal coordinates (α angles) in order to describe protein structures as sequences of symbols. The structural similarities are established in 5 steps, the first 3 being analogous to those used in BLAST: (1) building up a deterministic finite automaton describing all patterns identical or similar to those in the query structure; (2) searching for all these patterns in every structure in the database; (3) extending the patterns to longer matching substructures (i.e., SHSPs); (4) selecting compatible SHSPs for each query–database structure pair; and (5) ranking the query–database structure pairs using 3 scores based on SHSP similarity, on SHSP probabilities, and on spatial compatibility of SHSPs. Structural fragment probabilities are estimated according to a mixture transition distribution model, which is an approximation of a high‐order Markov chain model. With regard to sensitivity and selectivity of the structural matches, YAKUSA compares well to the best related programs, although it is by far faster: A typical database scan takes about 40 s CPU time on a desktop personal computer. It has also been implemented on a Web server for real‐time searches. Proteins 2005.

Syntons, metabolons and interactons: an exact graph-theoretical approach for exploring neighbourhood between genomic and functional data

MOTIVATIONnModern comparative genomics does not restrict to sequence but involves the comparison of metabolic pathways or protein-protein interactions as well. Central in this approach is the concept of neighbourhood between entities (genes, proteins, chemical compounds). Therefore there is a growing need for new methods aiming at merging the connectivity information from different biological sources in order to infer functional coupling.nnnRESULTSnWe present a generic approach to merge the information from two or more graphs representing biological data. The method is based on two concepts. The first one, the correspondence multigraph, precisely defines how correspondence is performed between the primary data-graphs. The second one, the common connected components, defines which property of the multigraph is searched for. Although this problem has already been informally stated in the past few years, we give here a formal and general statement together with an exact algorithm to solve it.nnnAVAILABILITYnThe algorithm presented in this paper has been implemented in C. Source code is freely available for download at: http://www.inrialpes.fr/helix/people/viari/cccpart.

Swelfe: a detector of internal repeats in sequences and structures.

Summary: Intragenic duplications of genetic material have important biological roles because of their protein sequence and structural consequences. We developed Swelfe to find internal repeats at three levels. Swelfe quickly identifies statistically significant internal repeats in DNA and amino acid sequences and in 3D structures using dynamic programming. The associated web server also shows the relationships between repeats at each level and facilitates visualization of the results. Availability: http://bioserv.rpbs.jussieu.fr/swelfe Contact: annela@abi.snv.jussieu.fr Supplementary information: Supplementary data are available at Bioinformatics online.

FROST: A filter‐based fold recognition method

To assess the reliability of fold assignments to protein sequences, we developed a fold recognition method called FROST (Fold Recognition‐Oriented Search Tool) based on a series of filters and a database specifically designed as a benchmark for this new method under realistic conditions. This benchmark database consists of proteins for which there exists, at least, another protein with an extensively similar 3D structure in a database of representative 3D structures (i.e., more than 65% of the residues in both proteins can be structurally aligned). Because the testing of our method must be carried out under conditions similar to those of real fold recognition experiments, no protein pair with sequence similarity detectable using standard sequence comparison methods such as FASTA is included in the benchmark database. While using FROST, we achieved a coverage of 60% for a rate of error of 1%. To obtain a baseline for our method, we used PSI‐BLAST and 3D‐PSSM. Under the same conditions, for a 1% error rate, coverages for PSI‐BLAST and 3D‐PSSM were 33 and 56%, respectively. Proteins 2002;49:493–509.

Pairwise and Multiple Identification of Three-Dimensional Common Substructures in Proteins

In this paper, we present an algorithm to find three-dimensional substructures common to two or more molecules. The basic algorithm is devoted to pairwise structural comparison. Given two sets of atomic coordinates, it finds the largest subsets of atoms which are similar in the sense that all internal distances are approximately conserved. The basic idea of the algorithm is to recursively build subsets of increasing sizes, combining two sets of size k to build a set of size k + 1. The algorithm can be used as is for small molecules or local parts of proteins (about 30 atoms). When a high number of atoms is involved, we use a two step procedure. First we look for common local fragments by using the previous algorithm, and then we gather these fragments by using a Branch and Bound technique. We also extend the basic algorithm to perform multiple comparisons, by using one of the structures as a reference point (pivot) to which all other structures are compared. The solution is the largest subsets of atoms common to the pivot and at least q other structures. Although both algorithms are theoretically exponential in the number of atoms, experiments performed on biological data and using realistic parameters show that the solution is obtained within a few minutes. Finally, an application to the determination of the structural core of seven globins is presented.

Automated multiple analysis of protein structures: Application to homology modeling of cytochromes P450

A computational strategy for homology modeling, using several protein structures comparison, is described. This strategy implies a formalized definition of structural blocks common to several protein structures, a new program to compare these structures simultaneously, and the use of consensus matrices to improve sequence alignment between the structurally known and target proteins. Applying this method to cytochromes P450 led to the definition of 15 substructures common to P450cam, P450BM3, and P450terp, and to proposing a 3D model of P450eryF. Proteins 28:388–404, 1997

Automatic classification of protein structures relying on similarities between alignments

BackgroundIdentification 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.ResultsWhen 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.ConclusionsWe 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.

Protein evolution driven by symmetric structural repeats

s - A single P DF containing all abstracts in this Supplement is available her e . http://www. biomedcentral.co m/content/pdf/14 71-2105-9-S10-in fo.pdf

Use of ternary similarities in graph based clustering for protein structural family classification

Classification of proteins 3D structures into structural families is reformulated in terms of graph based clustering of objects which are modular as similarities between two 3D structures relies on the local similarities of their matching substructures. Similarities between 3D structures are then represented as edges connecting objects in a graph.n Applying clustering algorithms to such a graph results in the following drawback: subsets of more than two 3D structures belonging to the same cluster may share no similar substructure. To overcome this drawback we propose to introduce constraints about ternary similarities, i.e. constraints on triples of objects. The 3D structures graph is first transformed into its line graph, that represents the adjacencies between the graph edges. The ternary constraints are applied on the line graph, and a maximal line graph is then extracted from the modified line graph. The corresponding 3D structures graph now satisfies the above mentioned ternary constraints. In our experiments applying clustering on the new graph results in a more stable classification which is coherent with the expert classification SCOP.