Modeling Macro-Molecular Interfaces with Intervor

Intervor is a software computing a parameter free representation of macro-molecular interfaces, based on the alpha-complex of the atoms. Given two interacting partners, possibly with water molecules squeezed in-between them, Intervor computes an interface model which has the following characteristics: (i) it identifies the atoms of the partners which are in direct contact and those whose interaction is water mediated, (ii) it defines a geometric complex separating the partners, the Voronoi interface, whose geometric and topological descriptions are straightforward (surface area, number of patches, curvature), (iii) it allows the definition of the depth of atoms at the interface, thus going beyond the traditional dissection of an interface into a core and a rim. These features can be used to investigate correlations between structural parameters and key properties such as the conservation of residues, their polarity, the water dynamics at the interface, mutagenesis data, etc. Intervor can be run from the web site http://cgal.inria.fr/abs/Intervor , or in stand-alone mode upon downloading the binary file. Plugins are also made available for Visual Molecular Dynamics (VMD) and Pymol

Modeling Macro-Molecular Interfaces with Intervor

International audienceIntervor is a software computing a parameter-free representation of macro-molecular interfaces, based on the a-complex of the atoms. Given two interacting partners, possibly with water molecules squeezed in-between them, Intervor computes an interface model which has the following characteristics: (i) it identifies the atoms of the partners which are in direct contact and those whose interaction is water mediated, (ii) it defines a geometric complex separating the partners, the Voronoi interface, whose geometric and topological descriptions are straightforward (surface area, number of patches, curvature), (iii) it allows the definition of the depth of atoms at the interface, thus going beyond the traditional dissection of an interface into a core and a rim. These features can be used to investigate correlations between structural parameters and key properties such as the conservation of residues, their polarity, the water dynamics at the interface, mutagenesis data, etc

Boosting the analysis of protein interfaces with Multiple Interface String Alignments: illustration on the spikes of coronaviruses

International audienceWe introduce multiple interface string alignment (MISA), a visualization tool to display coherently various sequence and structure based statistics at protein–protein interfaces (SSE elements, buried surface area, ΔASA, B factor values, etc). The amino acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to collate annotated sequences of (homologous) chains found in different biological contexts, that is, bound with different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/ΔASA, and so forth, make it trivial to identify commonalities and differences between chains, to infer key interface residues, and to understand where conformational changes occur upon binding. As such, they should prove of key relevance for knowledge-based annotations of protein databases such as the Protein Data Bank. Illustrations are provided on the receptor binding domain of coronaviruses, in complex with their cognate partner or (neutralizing) antibodies. MISA computed with a minimal number of structures complement and enrich findings previously reported. The corresponding package is available from the Structural Bioinformatics Library (http://sbl.inria.frand https://sbl.inria.fr/doc/Multiple_interface_string_alignment-user-manual.html)

PALSE: Python Analysis of Large Scale (Computer) Experiments

A tenet of Science is the ability to reproduce the results, and a related issue is the possibility to archive and interpret the raw results of (computer) experiments. This paper presents an elementary python framework addressing this latter goal. Consider a computing pipeline consisting of raw data generation, raw data parsing, and data analysis i.e. graphical and statistical analysis. palse addresses these last two steps by leveraging the hierarchical structure of XML documents. More precisely, assume that the raw results of a program are stored in XML format, possibly generated by the serialization mechanism of the boost C++ libraries. For raw data parsing, palse imports the raw data as XML documents, and exploits the tree structure of the XML together with the XML Path Language to access and select specific values. For graphical and statistical analysis, palse gives direct access to ScientificPython, R, and gnuplot. In a nutshell, palse combines standards languages ( python, XML, XML Path Language) and tools (Boost serialization, ScientificPython, R, gnuplot) in such a way that once the raw data have been generated, graphical plots and statistical analysis just require a handful of lines of python code. The framework applies to virtually any type of data, and may find a broad class of applications

The Structural Bioinformatics Library: modeling in biomolecular science and beyond

Motivation: Software in structural bioinformatics has mainly been application driven. To favor practitionersseeking off-the-shelf applications, but also developers seeking advanced building blocks to develop novelapplications, we undertook the design of the Structural Bioinformatics Library (SBL, http://sbl.inria.fr), a generic C++/python cross-platform software library targeting complex problems in structuralbioinformatics. Its tenet is based on a modular design offering a rich and versatile framework allowing thedevelopment of novel applications requiring well specified complex operations, without compromisingrobustness and performances.Results: The SBL involves four software components (1-4 thereafter). For end-users, the SBL providesready to use, state-of-the-art (1) applications to handle molecular models defined by unions of balls, todeal with molecular flexibility, to model macro-molecular assemblies. These tools can also be combined totackle integrated analysis problems. For developers, the SBL provides a broad C++ toolbox with modulardesign, involving core (2) algorithms, (3) biophysical models, and (4) modules, the latter being especiallysuited to develop novel applications. The SBL comes with a thorough documentation consisting of userand reference manuals, and a bugzilla platform to handle community feedback.Availability: The SBL is available fro

Novel structural parameters of Ig -Ag complexes yield a quantitative description of interaction specificity and binding affinity

Antibody-antigen complexes challenge our understanding, as analyses to datefailed to unveil the key determinants of binding affinity and interaction specificity. We par-tially fill this gap based on novel quantitative analyses using two standardized databases, theIMGT/3Dstructure-DB and the structure affinity benchmark.First, we introduce a statistical analysis of interfaces which enables the classification of ligand types(protein, peptide, chemical; cross-validated classification error of 9.6%), and yield binding affinitypredictions of unprecedented accuracy (median absolute error of 0.878 kcal/mol). Second, weexploit the contributions made by CDRs in terms of position at the interface and atomic packingproperties to show that in general, VH CDR3 and VL CDR3 make dominant contributions tothe binding affinity, a fact also shown to be consistent with the enthalpy - entropy compensationassociated with pre-configuration of CDR3.Our work suggests that the affinity prediction problem could be solved from databases of highresolution crystal structures of complexes with known affinity

Analyse des Interfaces de complexes Anticorps - Antigène: des caractéristiques propres au type de ligand à la prédiction d'affinité de liaison

Adaptive immunity is based on antigen-specific lymphocyte responses, with inparticular B cells secreting seric immunoglobulins (IG) involved in the opsonization of bacteriaand the neutralization of viruses. At the heart of these mechanisms is the formation of IG -Ag complexes, which challenge our understanding in terms of binding affinity and interactionspecificity.In this work, we dissect the interfaces of IG - Ag complexes with high resolution crystal structures,making a stride towards a better understanding of binding affinity and interaction specificity. First,we present global interface statistics clearly distinguishing ligand types (proteins, peptides, chem-ical compounds), and stressing the role of side chains. Second, we analyze the relative positions ofCDR with and without antigen, exhibiting a remarkably conserved pattern involving seven seamsbetween CDR. We also show that this generic pattern exhibits specific properties as a function ofthe ligand type. Finally, we present binding affinity predictions of unprecedented accuracy, with amedian absolute error of 1.02 kcal/mol.We anticipate that our findings will be of broad interest, not only in studying immune responsesat the structural level, but also in bio-engineering and IG design, with IG used extensively indiagnostics and as well as therapeutic agents

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

International audienceGiven 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 a purely geometric and topological comparisons, and we briefly comment on the biological findings reported in a companion paper.Étant donné un complexe protéique impliquant deux partenaires, un récepteur et un ligand, ce papier étudie le problème de comparer leur patchs de liaison, i.e. les ensembles d'atomes participant à leur interaction. Ce problème est classiquement formulé comme une recherche de sous-ensembles d'atomes quasi-isométriques entre les deux patchs, une tâche qui est équivalente à une recherche de cliques maximums. Ce problème étant NP-difficile, des patchs de liaison impliquant plus de 300 atomes ne peuvent-être traités. Nous étendons les travaux précédant dans deux directions. Premièrement, nous présentons un encodage générique pour les formes représentées par des complexes cellulaires. Nous partitionnons une forme en couches concentriques, basées sur ''l'ordre de couche'' des cellules du complexe. L'ordre des couches produisant un arbre de couches qui encode la géométrie et la topologie de la forme. Deuxièmement, pour le cas particulier de complexes cellulaires représentant des patchs de liaison de complexes protéiques, nous proposons trois algorithmes de comparaison de formes. Ces algorithmes combinent une distance d'édition d'arbre (TED, pour tree-edit-distance) sur les arbres de couches, avec des opérations d'éditions favorisant respectivement la comparaison topologique ou géométrique des patchs. Nous montrons en particulier que la TED géométrique établit un équilibre, en termes de précision et de temps de calculs, entre des comparaisons purement géométriques ou purement topologiques, et nous commentons brièvement les résultats biologiques qui sont détaillés dans un article compagnon

Multi-scale Geometric Modeling of Ambiguous Shapes with Toleranced Balls and Compoundly Weighted alpha-shapes

Dealing with ambiguous data is a challenge in Science in general and geometry processing in particular. One route of choice to extract information from such data consists of replacing the ambiguous input by a continuum, typically a one-parameter family, so as to mine stable geometric and topological features within this family. This work follows this spirit and introduces a novel framework to handle 3D ambiguous geometric data which are naturally modeled by balls. First, we introduce {\em toleranced balls} to model ambiguous geometric objects. A toleranced ball consists of two concentric balls, and interpolating between their radii provides a way to explore a range of possible geometries. We propose to model an ambiguous shape by a collection of toleranced balls, and show that the aforementioned radius interpolation is tantamount to the growth process associated with an additively-multiplicatively weighted Voronoi diagram (also called compoundly weighted or CW). Second and third, we investigate properties of the CW diagram and the associated CW $\alpha$-complex, which provides a filtration called the $\lambda$-complex. Fourth, we propose a naive algorithm to compute the CW VD. Finally, we use the $\lambda$-complex to assess the quality of models of large protein assemblies, as these models inherently feature ambiguities

Characterizing the Morphology of Protein Binding Patches

International audienceLet the patch of a partner in a protein complex be the collection of atoms accounting for the interaction. To improve our understanding of the structure-function relationship, we present a patch model decoupling the topological and geometric properties. While the geometry is classically encoded by the atomic positions, the topology is recorded in a graph encoding the relative position of concentric shells partitioning the interface atoms. The topological-geometric duality provides the basis of a generic dynamic programming-based algorithm comparing patches at the shell level, which may favor topological or geometric features. On the biological side, we address four questions, using 249 cocrystallized heterodimers organized in biological families. First, we dissect the morphology of binding patches and show that Nature enjoyed the topological and geometric degrees of freedom independently while retaining a finite set of qualitatively distinct topological signatures. Second, we argue that our shell-based comparison is effective to perform atomic-level comparisons and show that topological similarity is a less stringent than geometric similarity. We also use the topological versus geometric duality to exhibit topo-rigid patches, whose topology (but not geometry) remains stable upon docking. Third, we use our comparison algorithms to infer specificity-related information amidst a database of complexes. Finally, we exhibit a descriptor outperforming its contenders to predict the binding affinities of the affinity benchmark. The softwares developed with this article are available from http://team.inria.fr/abs/vorpatch_compatch/