Modeling macro–molecular interfaces with <tt>Intervor</tt>

Loriot, Sébastien; Cazals, Frédéric

doi:10.1093/bioinformatics/btq052

Cited by 23 publications

(27 citation statements)

References 11 publications

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“…The two numbers decorating an edge, respectively, refer to the number of atoms involved at that interface, and to the number of patches (connected components) of the interface. Interfaces were computed with the program INTERVOR, which implements the Voronoi model from (15). Note that a given subunit makes from three (e.g.…”

Section: Resultsmentioning

confidence: 99%

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Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

Agarwal

Caillouet

Coudert

et al. 2015

Molecular & Cellular Proteomics

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Consider a set of oligomers listing the subunits involved in subcomplexes of a macromolecular assembly, obtained e.g. using native mass spectrometry or affinity purification. Given these oligomers, connectivity inference (CI) consists of finding the most plausible contacts between these subunits, and minimum connectivity inference (MCI) is the variant consisting of finding a set of contacts of smallest cardinality. MCI problems avoid speculating on the total number of contacts but yield a subset of all contacts and do not allow exploiting a priori information on the likelihood of individual contacts. In this context, we present two novel algorithms, MILP-W and MILP-W B . The former solves the minimum weight connectivity inference (MWCI), an optimization problem whose criterion mixes the number of contacts and their likelihood. The latter uses the former in a bootstrap fashion to improve the sensitivity and the specificity of solution sets.Experiments on three systems (yeast exosome, yeast proteasome lid, human eIF3), for which reference contacts are known (crystal structure, cryo electron microscopy, cross-linking), show that our algorithms predict contacts with high specificity and sensitivity, yielding a very significant improvement over previous work, typically a twofold increase in sensitivity.The software accompanying this paper is made available and should prove of ubiquitous interest whenever connectivity inference from oligomers is faced. Molecular & Cellular

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Section: Resultsmentioning

confidence: 99%

“…The ideal situation is that where a high-resolution crystal structure is known since then all pairwise contacts can be inferred (15). This reference set together with the pool Pool E ( ) define positive (P), negative (N), and missed contacts (M) (Fig.…”

Section: Remark 1-assume That Each Edge Has a Default Weight D Insteamentioning

confidence: 99%

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

Agarwal

Caillouet

Coudert

et al. 2015

Molecular & Cellular Proteomics

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“…Given such a crystal structure, all pairs of molecules are tested to check whether they dene a contact. A pair denes a contact provided that in the solvent accessible (SAS) model of the assembly 1 , two atoms from these partners dene an edge in the α-complex of the assembly for α = 0, as classically done to dene macro-molecular interfaces [8,11,16].) This protocol actually calls for one comment.…”

Section: Pairwise Contacts Within Macro-molecular Complexesmentioning

confidence: 99%

Connectivity Inference in Mass Spectrometry Based Structure Determination

Agarwal

Araújo

Caillouet

et al. 2013

Lecture Notes in Computer Science

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Abstract:We consider the following Minimum Connectivity Inference problem (MCI), which arises in structural biology: given vertex sets V i ⊆ V, i ∈ I, nd the graph G = (V, E) minimizing the size of the edge set E, such that the sub-graph of G induced by each V i is connected. This problem arises in structural biology, when one aims at nding the pairwise contacts between the proteins of a protein assembly, given the lists of proteins involved in sub-complexes. We present four contributions. First, using a reduction of set cover, we establish that MCI is APX-hard. Second, we show how to solve the problem to optimality using a mixed integer linear programming formulation (MILP). Third, we develop a greedy algorithm based on union-nd data structures (Greedy), yielding a 2(log 2 |V | + log 2 κ)-approximation, with κ the maximum number of subsets V i a vertex belongs to. Fourth, application-wise, we use the MILP and the greedy heuristic to solve the aforementioned connectivity inference problem in structural biology. We show that the solutions of MILP and Greedy are more parsimonious than those reported by the algorithm initially developed in biophysics, which are not qualied in terms of optimality. Since MILP outputs a set of optimal solutions, we introduce the notion of consensus solution. Using assemblies whose pairwise contacts are known exhaustively, we show an almost perfect agreement between the contacts predicted by our algorithms and the experimentally determined ones, especially for consensus solutions. Key-words: Connectivity Inference Connected induced sub-graphs, network design, APX-hard, Mixed integer linear program, Greedy algorithm, Mass spectrometry, Protein assembly, Structural biology, Biophysics, Molecular machines * INRIA Sophia-Antipolis -Méditerranée † Univ. Nice Sophia Antipolis, CNRS, I3S, UMR 7271, 06900 Sophia Antipolis, France ‡ Correspondence to Frederic.Cazals@inria.fr or to David.Coudert@inria.fr Inférence de la connectivité pour la détermination de structure en spectrométrie de masse Résumé : Nous considérons le problème d'Inférence de Connectivité Minimale (Minimum Connectivity Inference ou MCI) qui se pose en biologie structurale: étant donnés des ensembles de sommets V i ⊆ V, i ∈ I, trouver le graphe G = (V, E) minimisant la taille de l'ensemble des arêtes E, de telle sorte que le sous-graphe de G induit par chaque ensemble V i soit connexe. Ce problème se pose en biologie structurale pour la determination des contacts plausibles entre les protéines d'un assemblage à partir des listes de protéines présentes dans des sous-complexes. Nous présentons quatre contributions.Premièrement, nous montrons que le problème MCI est APX-hard en utilisant une réduction de set cover. Deuxièmement, nous présentons une formulation en programme linéaire mixte (MILP) permettant de résoudre MCI de façon optimale. Troisièmement, nous proposons un algorithme glouton (Greedy) basé sur des structures de données Union-Find. Nous montrons que cet algorithme est une 2(log 2 |V | + log 2 κ)-approximation de l'op...

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“…Consider a protein complex, and assume that its interface atoms have been identified. This process consists of spotting the atoms of each partner facing one-another, and a reference method to do so consists of resorting to the α-complex of the protein complex, which is a simplicial complex contained in the dual of the Voronoi (power) diagram of the atoms in the SAS model [18]. Having identified the interface atoms, the computation of shelling forests/trees requires three main steps for each partner, namely (i) computing the cell complex representing the SAS of the partner, (ii) computing the SO of interface atoms, and (iii) building the shells, the shelling graph, and the shelling forest/trees.…”

Section: Shelling Binding Patches Of Protein Complexes: Outlinementioning

confidence: 99%

Shape Matching by Localized Calculations of Quasi-Isometric Subsets, with Applications to the Comparison of Protein Binding Patches

Cazals

Malod-Dognin

2011

Pattern Recognition in Bioinformatics

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Abstract. Given a protein complex involving two partners, the receptor and the ligand, this paper addresses the problem of comparing their binding patches, i.e. the sets of atoms accounting for their interaction. This problem has been classically addressed by searching quasi-isometric subsets of atoms within the patches, a task equivalent to a maximum clique problem, a NP-hard problem, so that practical binding patches involving up to 300 atoms cannot be handled.We extend previous work in two directions. First, we present a generic encoding of shapes represented as cell complexes. We partition a shape into concentric shells, based on the shelling order of the cells of the complex. The shelling order yields a shelling tree encoding the geometry and the topology of the shape. Second, for the particular case of cell complexes representing protein binding patches, we present three novel shape comparison algorithms. These algorithms combine a Tree Edit Distance calculation (TED) on shelling trees, together with Edit operations respectively favoring a topological or a geometric comparison of the patches. We show in particular that the geometric TED calculation strikes a balance, in terms of accuracy and running time, between purely geometric and topological comparisons, and we briefly comment on the biological findings reported in a companion paper.

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Modeling macro–molecular interfaces with `Intervor`

Abstract: Intervor can be run from the web site http://cgal.inria.fr/abs/Intervor or upon downloading the binary file. Plugins are also made available for VMD and Pymol.

Cited by 23 publications

References 11 publications

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

Connectivity Inference in Mass Spectrometry Based Structure Determination

Shape Matching by Localized Calculations of Quasi-Isometric Subsets, with Applications to the Comparison of Protein Binding Patches

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Modeling macro–molecular interfaces with Intervor

Abstract: Intervor can be run from the web site http://cgal.inria.fr/abs/Intervor or upon downloading the binary file. Plugins are also made available for VMD and Pymol.

Cited by 23 publications

References 11 publications

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems*

Connectivity Inference in Mass Spectrometry Based Structure Determination

Shape Matching by Localized Calculations of Quasi-Isometric Subsets, with Applications to the Comparison of Protein Binding Patches

Contact Info

Product

Resources

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Modeling macro–molecular interfaces with `Intervor`