3.1.1 Context

The scientific discovery process is very often based on cycles of measurement, classification, and generalisation. It is easy to argue that this is especially true in the biological sciences. The proteins that exist today represent the molecular product of some three billion years of evolution. Therefore, comparing protein sequences and structures is important for understanding their functional and evolutionary relationships 69, 50. There is now overwhelming evidence that all living organisms and many biological processes share a common ancestry in the tree of life. Historically, much of bioinformatics research has focused on developing mathematical and statistical algorithms to process, analyse, annotate, and compare protein and DNA sequences because such sequences represent the primary form of information in biological systems. However, there is growing evidence that structure-based methods can help to predict networks of protein-protein interactions (PPIs) with greater accuracy than those which do not use structural evidence 54, 74. Therefore, developing techniques which can mine knowledge of protein structures and their interactions is an important way to enhance our knowledge of biology 41.

3.1.2 Formalising and Exploiting Domain Knowledge

Concerning protein structure classification, we aim to explore novel classification paradigms to circumvent the problems encountered with existing hierarchical classifications of protein folds and domains. In particular it will be interesting to set up fuzzy clustering methods taking advantage of our previous work on gene functional classification 45, but instead using Kpax domain-domain similarity matrices. A non-trivial issue with fuzzy clustering is how to handle similarity rather than mathematical distance matrices, and how to find the optimal number of clusters, especially when using a non-Euclidean similarity measure. We will adapt the algorithms and the calculation of quality indices to the Kpax similarity measure. More fundamentally, it will be necessary to integrate this classification step in the more general process leading from data to knowledge called Knowledge Discovery in Databases (KDD) 48.

Another example where domain knowledge can be useful is during result interpretation: several sources of knowledge have to be used to explicitly characterise each cluster and to help decide its validity. Thus, it will be useful to be able to express data models, patterns, and rules in a common formalism using a defined vocabulary for concepts and relationships. Existing approaches such as the Molecular Interaction (MI) format  51 developed by the Human Genome Organization (HUGO) mostly address the experimental wet lab aspects leading to data production and curation  60. A different point of view is represented in the Interaction Network Ontology (INO), a community-driven ontology that aims to standardise and integrate data on interaction networks and to support computer-assisted reasoning  61. However, this ontology does not integrate basic 3D concepts and structural relationships. Therefore, extending such formalisms and symbolic relationships will be beneficial, if not essential, when classifying the 3D shapes of proteins at the domain family level.

Domain family classification is also relevant for studying domain-domain interactions (DDI). Our previous work on Knowledge-Based Docking (KBDOCK, 6, 4) will be updated and extended using newly published DDIs. Methods for inferring new DDIs from existing protein-protein interactions (PPIs) will be developed. Efforts should be made for validating such inferred DDIs so that they can be used to enrich DDI classification and predict new PPIs.

In parallel, we also intend to design algorithms for leveraging information embedded in biological knowledge graphs (also known as complex networks). Knowledge graphs mostly represent PPIs, integrated with various properties attached to proteins, such as pathways, drug binding or relation with diseases. Setting up similarity measures for proteins in a knowledge graph is a difficult challenge. Our objective is to extract useful knowledge from such graphs in order to better understand and highlight the role of multi-component assemblies in various types of cell or organisms. Ultimately, knowledge graphs can be used to model and simulate the functioning of such molecular machinery in the context of the living cell, under physiological or pathological conditions.

3.1.3 Function Annotation in Large Protein Graphs

Knowledge of the functional properties of proteins can shed considerable light on how they might interact. However, huge numbers of protein sequences in public databases such as UniProt/TrEMBL lack any functional annotation, and the functional annotation of such sequences is a highly challenging problem. We are developing graph-based and machine learning techniques to annotate automatically the available unannotated sequences with functional properties such as EC numbers and Gene Ontology (GO) terms (note that these terms are organised hierarchically allowing generalization/specialization reasoning). The idea is to transfer annotations from expert-reviewed sequences present in the UniProt/SwissProt database (about 560 thousands entries) to unreviewed sequences present in the UniProt/TrEMBL database (about 80% of 180 millions entries). For this, we have to learn from the UniProt/SwissProt database how to compute the similarity of proteins sharing identical or similar functional annotations. Various similarity measures can be tested using cross-validation approches in the UniProt/SwissProt database. For instance, we can use primary sequence or domain signature similarities. More complex similarities can be computed with graph-embedding techniques.

This work is in progress with Bishnu Sarker's PhD project and a first approach called GrAPFI (Graph-based Automatic Protein Function Inference) was presented at conferences in 2018 15, 14.

3.2.1 Context

At the molecular level, each biomolecular interaction is embodied by a physical 3D interface. Therefore, if the 3D structures of a pair of interacting protein/NA molecules are known, it should in principle be possible for a docking algorithm to use this knowledge to predict the structure of the complex. However, modeling protein and even more RNA flexibility accurately during docking is very computationally expensive. This is due to the very large number of internal degrees of freedom in each molecule, associated with twisting motions around covalent bonds. Therefore, it is highly impractical to use detailed force-field or geometric representations in a brute-force docking search. Instead, most docking algorithms use fast heuristic methods to perform an initial rigid-body search in order to locate a relatively small number of candidate binding orientations, and these are then refined using a more expensive interaction potential or force-field model, which might also include flexible refinement using molecular dynamics (MD), for example.

3.2.2 Polar Fourier Docking Correlations

In our Hex protein docking program 63, the shape of a protein molecule is represented using polar Fourier series expansions of the form

where $\sigma \left(\underline{x}\right)$ is a 3D shape-density function, $a_{nlm}$ are the expansion coefficients, $R_{nl}\left(r\right)$ are orthonormal Gauss-Laguerre polynomials and $y_{lm}(\theta ,\varphi )$ are the real spherical harmonics. The electrostatic potential, $\varphi \left(\underline{x}\right)$, and charge density, $\rho \left(\underline{x}\right)$, of a protein may be represented using similar expansions. Such representations allow the in vacuo electrostatic interaction energy between two proteins, A and B, to be calculated as 53

This equation can be demonstrated using the notion of overlap between 3D scalar quantities to give a physics-based scoring function. If the aim is to find the configuration that gives the most favourable interaction energy, then it is necessary to perform a six-dimensional search in the space of available rotational and translational degrees of freedom. By re-writing the polar Fourier expansions using complex spherical harmonics, we showed previously that fast Fourier transform (FFT) techniques may be used to accelerate the search in up to five of the six degrees of freedom 64. Furthermore, we also showed that such calculations may be accelerated dramatically on modern graphics processor units 11, 8. Consequently, we are continuing to explore new ways to exploit the polar Fourier approach.

3.2.3 Assembling Symmetrical Protein Complexes

Although protein-protein docking algorithms are improving 65, 55, it still remains challenging to produce a high resolution 3D model of a protein complex using ab initio techniques. This is mainly due to the problem of structural flexibility described above. However, with the aid of even just one simple constraint on the docking search space, the quality of docking predictions can improve considerably 11, 64. In particular, many protein complexes involve symmetric arrangements of one or more sub-units, and the presence of symmetry may be exploited to reduce the search space considerably 40, 62, 68. For example, using our operator notation (in which $\widehat{R}$ and $\widehat{T}$ represent 3D rotation and translation operators, respectively), we have developed an algorithm which can generate and score candidate docking orientations for monomers that assemble into cyclic ($C_n$) multimers using 3D integrals of the form

where the identical monomers A and B are initially placed at the origin, and ${\omega }_n=2\pi /n$ is the rotation about the principal $n$-fold symmetry axis. This example shows that complexes with cyclic symmetry have just 4 rigid body degrees of freedom (DOFs), compared to $6(n-1)$ DOFs for non-symmetrical $n$-mers. We have generalised these ideas in order to model protein complexes that crystallise into any of the naturally occurring point group symmetries ($C_n$, $D_n$, $T$, $O$, $I$). This approach was published in 2016 10, and was subsequently applied to several symmetrical complexes from the “CAPRI” blind docking experiment 47. Although we currently use shape-based FFT correlations, the symmetry operator technique may equally be used to build and refine candidate solutions using a more accurate coarse-grained (CG) force-field scoring function.

3.2.4 Coarse-Grained Models

Many approaches have been proposed in the literature to take into account protein (and more recently RNA/DNA) flexibility during docking. The most thorough methods rely on expensive atomistic simulations using MD. However, much of a MD trajectory is unlikely to be relevant to a docking encounter unless it is constrained to explore a putative protein-protein/NA interface. Consequently, MD is normally only used to refine a small number of candidate rigid body docking poses. A much faster, but more approximate method is to use "coarse-grained" (CG) normal mode analysis (NMA) techniques to reduce the number of flexible degrees of freedom to just one or a handful of the most significant vibrational modes 59, 46, 56, 57. In our experience, docking ensembles of NMA conformations do not give much improvement over basic FFT-based soft docking 72, and it is very computationally expensive to use side-chain repacking to refine candidate soft docking poses 5.

In the last few years, CG force-field models have become increasingly popular in the MD community because they allow very large biomolecular systems to be simulated using conventional MD programs 39. Typically, a CG force-field representation replaces the atoms in each amino acid with from 2 to 4 “pseudo-atoms”, and it assigns each pseudo-atom a small number of parameters to represent its chemo-physical properties. By directly attacking the quadratic nature of pair-wise energy functions, coarse-graining can speed up MD simulations by up to three orders of magnitude. Nonetheless, such CG models can still produce useful models of very large multi-component assemblies 67. Furthermore, this kind of CG model effectively integrates out many of the internal DOFs to leave a smoother but still physically realistic energy surface 52. We are currently developing a CG scoring function for RNA-protein docking by fragments assembly. This work is part of the PhD project of Anna Kravchenko.

3.2.5 Assembling Multi-Component Complexes and Integrative Structure Modelling

We also want to develop related approaches for integrative structure modeling using cryo-electron microscopy (cryo-EM). Thanks to recent developments in cryo-EM instruments and technologies, it is now feasible to capture low resolution images of very large macromolecular machines. However, while such developments offer the intriguing prospect of being able to trap biological systems in unprecedented levels of detail, there will also come with an increasing need to analyse, annotate, and interpret the enormous volumes of data that will soon flow from the latest instruments. In particular, a new challenge that is emerging is how to fit previously solved high resolution protein structures into low resolution cryo-EM density maps. However, the problem here is that large molecular machines will have multiple sub-components, some of which will be unknown, and many of which will fit each part of the map almost equally well. Thus, the general problem of building high resolution 3D models from cryo-EM data is like building a complex 3D jigsaw puzzle in which several pieces may be unknown or missing, and none of which will fit perfectly. We wish to proceed firstly by putting more emphasis on the single-body terms in the scoring function 44, and secondly by using fast CG representations and knowledge-based distance restraints to prune large regions of the search space. This work has made much progress during the PhD project of Maria Elisa Ruiz Echartea but still requires further efforts.

3.2.6 Protein-Nucleic Acid Interactions

As well as playing an essential role in the translation of DNA into proteins, RNA molecules carry out many other essential biological functions in cells, often through their interactions with proteins. A critical challenge in modeling such interactions computationally is that the RNA is often highly flexible, especially in single-stranded (ssRNA) regions of its structure. These flexible regions are often very important because it is through their flexibility that the RNA can adjust its 3D conformation in order to bind to a protein surface. However, conventional protein-protein docking algorithms generally assume that the 3D structures to be docked are rigid, and so are not suitable for modeling protein-RNA interactions. There is therefore much interest in developing dedicated protein-RNA docking algorithms which can take RNA flexibility into account. This research topic has been initiated with the recruitement of Isaure Chauvot de Beauchêne in 2016 and is becoming a major activity in the team. A novel flexible docking algorithm is currently under development in the team. It first docks small fragments of ssRNA (typically three nucleotides at a time) onto a protein surface, and then combinatorially reassembles those fragments in order to recover a contiguous ssRNA structure on the protein surface 43, 42.

As the correctness of the initial docking of the fragments settles an upper limit to the correctness of the full model, we are now focusing on improving that step. A key component of our docking tool is the energy function of the protein-fragment interactions that is used both to drive the sampling (positioning of the fragments) by minimization, and to discriminate the correct final positions from decoys (i.e., false positives). We are developing a new approach to create knowledge-based parameters for coarse-grain energy functions from public structural data, in collaboration with Sjoerd de Vries (INSERM). Such approach will be applied first to ssRNA-protein complexes, then to other types of complexes such as protein-peptides.

Another key requirement for this approach is an exhaustive but non-redundant library of possible internal conformations of RNA fragments. Our library is built by clustering hundreds of thousands of experimentally known RNA structures, based on an approximate geometric similarity criteria. We are currently developping new exact approaches for the clustering of 3D conformations based on internal coordinates, in order to optimise the representativity of the library. This is part of the PhD subject of Antoine Moniot, co-supervised by Yann Guermeur (ABC LORIA team).

In the future, we will improve the combinatorial algorithm used for reassembling the docked fragments using both experimental constraints (PhD project Anna Kravchenko) and machine-learning approaches (PhD project Hrishikesh Dondge).

7.1.1 ProtNAff

• Name: Protein - Nucleic Acids Filters and Fragments
• Keywords: Structural alphabet, Structural Biology, Nucleic Acids
• Scientific Description: The modeling of nucleic acids (NA) - protein interactions can greatly help the design of therapeutic NA. Atomistic models of NA fragments can be used to model the 3D structures of NA-protein complexes, to subdivide the handling of RNAs great flexibility. One way to obtain relevant RNA fragments is to extract them from existing 3D structures of interactions corresponding to the context one wants to model (surface area surrounding NA 2D structures, specific protein families, specific sequences) and to learn from them.
• Functional Description: ProtNAff is a python-based software for (i) the automated parsing, correction and annotation of all protein-nucleic acid structures in the public Protein Data Bank, (ii) the creation of libraries of non-redundant RNA/DNA structural fragments, (iii) the selection of sets of structures by customised queries, and (iv) the computation of statistics on sets of RNA/DNA - protein structures.
• URL: https://github.com/isaureCdB/NAfragDB
• Publication: hal-02393039
• Contacts: Isaure Chauvot de Beauchêne, Antoine Moniot, Sjored Jacob De Vries
• Participants: Isaure Chauvot de Beauchêne, Antoine Moniot, Sjored Jacob De Vries
• Partner: INSERM

7.1.2 InteR3Mdb

• Name: Database for interactions between RNA and RRM (RNA Recognition Motif)
• Keywords: Databases, Proteins, Nucleic Acids, 3D interaction, Biological sequences
• Functional Description: InteR3Mdb is a comprehensive database gathering and interconnecting public data on the 3D structures and sequences of a very conserved protein domain, the RNA Recognition Motif (RRM), and its interaction with RNA. It comprises a web-interface, a query interface and an API interface.
• Release Contributions: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 813239. It will be confidential, for usage within the H2020 ITN project called RNAct, for the whole duration of the project (2019-2023). After the project, it will be made publicly available.
• URL: https://inter3mdb.loria.fr
• Contact: Hrishikesh Dhondge

7.1.3 PPIDM

• Name: Protein-protein interactions (PPI) domain miner
• Keywords: Proteins, 3D interaction, Protein domain, Link prediction, Graph
• Functional Description: PPIDM exploits a formalization of a tripartite graph between a source of PPIs (protein-protein interactions) and two sets of domains present in the proteins involved in these PPIs. The software performs link inference between the two sets of domains by calculating a probability score for these links. A threshold is determined by learning from positive and negative examples. The DDIs (domain-domain interactions) identified by PPIDM are made available as a pool of plausible DDIs that can be the subject of subsequent analyses.
• URL: https://ppidm.loria.fr
• Contact: Marie-Dominique Devignes

8.1.1 Biomedical Knowledge Discovery

In collaboration with clinicians at the CHRU Nancy in the framework of the RHU FIGHT-HF program and of the Contrat d'Interface, we have developed the GKBox (Graph Knowledge Box) which integrates, in the Neo4J graph format, a huge amount of data from over 20 public biological databases such as UniProt, DisGeNET or String. In parallel we have developed automated querying mechanisms to retrieve constrained paths from the GKBox, such paths restricted to protein-protein interactions or paths following a protein-pathway-protein pattern, and involving only the proteins in a list of interest. Such functionality has been used to better understand the mechanisms underpinning lists of proteins associated with particular phenotypes in the cohorts, as in 21, 22, 28.

Linked RDF data constitute another type of knowledge graphs that we addressed in collaboration with the Orpailleur Team in the frame of the PraktikPharma ANR project coordinated by Adrien Coulet. Our participation led to two co-signed publications: on a manually annotated corpus for pharmacogenomics on the one hand, and on a scalable method for mining path patterns from knowledge graphs on the other hand 24, 32. A third publication was submitted at the end of 2020 on investigating adverse drug reaction mechanisms with knowledge graph mining. The PhD project of Kamrul Islam addresses the link prediction problem in knowledge graphs using deep learning approaches while taking into consideration domain knowledge and integrity constraints.

Finally we also participated in a preliminary study carried out in the Orpailleur team to explain multicriteria decision making with formal concept analysis 29, leveraging concept lattices as yet another type of knowledge graph.

8.1.2 Stochastic Random Trees for Similarity Computation

We proposed a method to compute similarities on unlabeled data, based on extremely randomised trees called Unsupervised Extremely Randomised Trees (UET) 20. The main idea is to randomly split the data in an iterative fashion until a stopping criterion is met, and subsequently compute similarity values based on the co-occurrence of samples in the leaves of each generated tree. We evaluated our method on synthetic and real-world datasets using metrics similar to intracluster and intercluster similarities. Such metrics allow us to assess the computed similarities instead of a clustering algorithm's results. We also assessed some interesting properties of UET such as invariance under monotone transformations of variables and robustness to correlated variables and noise. Finally, we performed hierarchical agglomerative clustering on synthetic and real-world homogeneous and heterogeneous datasets using UET versus standard similarity measures. The experiments showed that the algorithm outperforms existing methods in several cases, and reduces the amount of preprocessing needed with many real-world datasets.

As an extension of the UET approach, we wanted to address the current challenge of clustering graph vertices especially in complex networks, i.e., graphs with attributed vertices or edges 30. Thus we proposed GraphTrees, a novel method that relies on random decision trees to compute pairwise dissimilarities between vertices in a graph. We show that combining different types of trees, it is possible to extend this framework to graphs where the vertices have attributes. GraphTrees can handle heterogeneous types of node attributes as well. Here again, unlike other approaches, the attributes do not need to be preprocessed. We also show that our approach is competitive with state of the art methods in the case of non-attributed graphs in terms of quality of clustering, and provides promising results in the case of vertex-attributed graphs. By extending the use of an already well established approach – the random trees – to graphs, our approach opens interesting research directions.

8.1.3 Graph-based Approaches for Machine Learning and Protein Annotation

One of the methods developed by Bishnu Sarker for protein function annotation is based on label propagation in graphs. GrAPFI (Graph-based Automatic Protein Function annotation) relies on a protein graph in which edges are weighted by the domain similarity between proteins 26. When applied to function annotations taken from Gene Ontology, the method was improved by post-processing of the predicted annotations, leveraging the hierarchical structure of the ontology. Moreover it was shown that the post-processing step also improves other methods of protein function annotation 33.

Collaborative work on the development of a distributed algorithm for large-scale graph clustering has led to a technical report in 2020 36 which contributed to the completion of Wissem Inoubli's PhD thesis (defended early January 2021).

Our work on large-scale distributed graphs led us to participate in the coordination of a special issue on "Advances on large evolving graphs" in Future Generation Computer Systems 17.

8.2.1 Docking of ssRNA on Proteins

An RNA hairpin is a particular RNA structure made of a double-strand helix closed on one side by a single-strand (ss) loop. We have adapted our fragment-based docking method to model the interactions of an RNA hairpin of known 2D structure and sequence to a protein of known 3D structure. Compared to the original method for linear single-stranded RNA (ssRNA), the new version integrates the constraint of loop closure in the combinatorial assembly of the ssRNA fragments docked on the protein surface. A proof of principle of this new method on a known hairpin-protein complex has been presented in a talk by Antoine Moniot (PhD) at the JOBIM2020 conference 31.

We also developed a new version of NAfragDB, renamed ProtNAff (Protein-Nucleic Acid filters and fragments), for the parsing and structural anotation of protein-NA 3D structures from the public Protein Data Bank (PDB), the selection of sets of structures based on multiple criteria (sequence, structure, type of interactions), the creation of libraries of fragment conformations, and the statistical analysis of the fragments properties. With this tool, we have created new specific benchmarks for testing hairpin-protein docking, ssRNA-protein docking and ssDNA-protein docking. We also showed that around half of the local RNA conformations in the PDB are induced by the RNA binding to the protein. ProtNAff is available on https://github.com/isaureCdB/ProtNAff.

As fragment libraries are a key component of our fragment-based docking approach, we are currently developing new clustering criteria to create libraries as representative as possible of the diversity of local RNA/DNA conformations. A new representation of RNA in internal coordinates, developed by Alix Delannoy (3A Mines research training), is showing very promising results.

In the frame of our H2020 ITN project RNAct, which aims at designing new RNA-binding proteins based on the well-conserved protein domain called RNA Recognition Motif (RRM), we created InteR3Mdb (see section 7). With this tool, we created the first general alignment of all sequences of RRMs that have a 3D structure available. These results will serve as the basis to apply ML methods on the prediction of RRMs structure from sequence and of RRM-RNA interaction from structure. We also created a pipeline for the homology modeling of RRM structure from sequence, which will be enriched by the outcome of the ML application on InteR3Mdb. All this is part of the PhD project of Hrishikesh Dhondge.

Finally, in collaboration with Sjoerd de Vries (INSERM), we have created a largely automated reactive pipeline for the modeling of protein-ssRNA complexes, starting from RNA sequence, protein structure and experimental identification of some protein residues at the binding interface. This has been applied to the study of the SARS-COV-2 nsp9 protein in the frame of a collaborative biohackathon involving students from the CAPSID team and from the RNAct project. The pipeline is available on https://github.com/sjdv1982/biohackathon-covid.

8.2.2 3D Modeling and Virtual Screening

The docking method named EROS-Dock, developed by Maria Elisa Ruiz-Echartea during her PhD thesis, has been improved by the use of restraints that lead to higher quality models in pairwise and multi-component protein docking 25.

Virtual screening of small molecules libraries is an essential part of the team's expertise that is often called upon by external partners in biology. Success has been obtained in the discovery of a new type of inhibitor of the MET kinase domain 18, and in the understanding of the catalytic mechanism and inhibitor binding of aminopeptidase A 19. This approach is also currently experimented in the search for inhibitors of a galactosyltransferase involved in mucopolysaccharidoses 37. Reciprocally, in silico methods have been used in a collaborative work with a Brazilian team to search for new protein targets in the fight against a plant disease affecting coffee 34.

The use of chemoinformatics methods to help antibacterial discovery has been reviewed in an Inria technical report 35.

Finally, a great deal of efforts has gone in 2020 into finding Covid-19 inibitors, in the form of either peptides mimicking the binding receptor of the virus spike protein, or chemical molecules meeting a certain number of expert criteria and capable of interfering with the binding of the spike to its receptor. Collaborations have been established with a mathematics team at the Université Savoie Mont-Blanc for analysing amino acid contacts during molecular dynamic trajectories, and with a biology team at the CNRS-Université de Montpellier for performing biological assays to test all the candidate inhibitors. Some promising results now await full biological confirmation.

9.1.1 Participation in other international programs

Project Tempographs: Analysing big data with temporal graphs and machine learning. Application to urban traffic analysis and protein function annotation.

Participants: Sabeur Aridhi, Marie-Dominique Devignes, Malika Smaïl-Tabbone, Bishnu Sarker, Wissem Inoubli.

Partners: LORIA/Inria NGE, Federal University of Ceará (UFC).

Value: 20 k€.

Duration: 2017–2020.

Description: This project aims to investigate and propose solutions for both urban traffic-related problems and protein annotation problems. In the case of urban traffic analysis, problems such as traffic speed prediction, travel time prediction, traffic congestion identification and nearest neighbors identification will be tackled. In the case of protein annotation problem, protein graphs and/or protein–protein interaction (PPI) networks will be modeled using dynamic time-dependent graph representations.

9.2.1 FP7 & H2020 Projects

9.2.2 Collaborations with major European organizations

ELIXIR: 3D-bioinfo Community. We are members of the new ELIXIR 3D-bioinfo community. More specifically, Isaure Chauvot de Beauchêne is member of the sub-section known as Tools to describe, analyse, annotate, and predict nucleic acid structures of this community. ELIXIR Communities enable the participation of communities of practice in different areas of the life sciences in the activities of ELIXIR. The goal is to underpin the evolution of data, tools, interoperability, compute and training infrastructures for European life science informatics (see https://www.elixir-europe.org/use-cases).

ELIXIR: Interoperability Platform Marie-Dominique Devignes is collaborating with the ELIXIR Interoperability Platform as a member of the IFB (Institut Français de Bioinformatique), the ELIXIR French Node (ELIXIR FR). She coordinates and reviews projects in the field of FAIR data, Data Management Plans and Recommended Interoperability Resources (RIR).

9.3.1 FEDER – SB-Server

Participants: Marie-Dominique Devignes, Bernard Maigret, Isaure Chauvot de Beauchêne, Sabeur Aridhi.

Project title: Structural bioinformatics server; PI: David Ritchie, Capsid (Inria Nancy – Grand Est); Value: 24 k€; Duration: 2015–2020. Description: This funding provides a small high performance computing server for structural bioinformatics research at the Inria Nancy – Grand Est centre.

9.3.2 ANR

10.1.1 Scientific Events: Organisation

10.1.2 Scientific Events: Selection

10.1.3 Journal

10.1.4 Scientific Expertise

Marie-Dominique Devignes reviewed a grant application for the Netherlands eScience Center (eTEC call 2020 : Innovative eScience Technologies)

10.1.5 Research Administration

Marie-Dominique Devignes is responsible for the transverse Axis Numeric Health at the LORIA.

Malika Smaïl-Tabbone and Isaure Chauvot de Beauchêne are members of the Conseil de Pôle AM2I at the Université de Lorraine.

10.2.1 Teaching

• Sabeur Aridhi and Malika Smaïl-Tabbone are enseignants-chercheurs with a full service. Sabeur Aridhi is responsible for the major in IAMD (Ingénierie et Applications des Masses de Données) at TELECOM Nancy (Université de Lorraine),
• Marie-Dominique Devignes teaches about 34h at Telecom Nancy (1A) and 10h in the Cursus Master Ingenieur at the Université de Lorraine.
• Isaure Chauvot de Beauchêne and Sjoerd de Vries organised and gave an online course on "Macromolecular Docking with ATTRACT", that took place in 4 sessions of 2 hours, between 8/03/2020 and 13/05/2020.

10.2.2 Supervision

• PhD in progress: Kévin Dalleau, Complex graph analysis for classification: application to disease nosography, 01/12/2016, Malika Smaïl-Tabbone, Miguel Couceiro.
• PhD in progress: Bishnu Sarker, Developing distributed graph-based approaches for large-scale protein function annotation and knowledge discovery, 01/11/2017, Marie-Dominique Devignes, Sabeur Aridhi.
• PhD in progress: Antoine Moniot, Modeling protein / nucleic acid complexes by combinatorial structural fragment assembly, 01/11/2018, Yann Guermeur, Isaure Chauvot de Beauchêne.
• PhD in progress: Athénaïs Vaginay, Model selection and analysis for biological networks: use of domain knowledge and application to networks disturbed in diseases, 01/11/2018, Taha Boukhobza, Malika Smaïl-Tabbone.
• PhD in progress: Anna Kravchenko, Fragment-based modeling of protein-RNA complexes for protein design, 01/10/2019, Malika Smaïl-Tabbone, Isaure Chauvot de Beauchêne.
• PhD in progress: Hrishikesh Dhondge, A new knowledge base for modeling and design of RNA-binding proteins, 01/10/2019, Marie-Dominique Devignes, Isaure Chauvot de Beauchêne.
• PhD in progress: Diego Amaya Ramirez, HLA genetic system and organ transplantation: understanding the basics of immunogenicity to improve donor / receptor compatibility when assigning grafts to recipients, 01/10/2019, Marie-Dominique Devignes, Jean-Luc Taupin.
• PhD in progress: Kamrul Islam, Distributed link prediction in large complex graphs: application to biomolecule interactions, 01/11/2019, Malika Smail-Tabbone, Sabeur Aridhi.

10.2.3 Juries

• Malika Smaïl-Tabbone was member of the PhD jury of Carlos Bermejo Das Neves (Université Strasbourg)

10.3.1 Internal or External Inria Responsibilities

Malika Smaïl-Tabbone was a member of the national Inria SRP/ARP jury in 2020.

International journals

• 17 articleSabeurS. Aridhi, JoséJ. Macedo, Engelbert MephuE. Nguifo and KarineK. Zeitouni. Special issue on “Advances on Large Evolving Graphs”Future Generation Computer Systems110September 2020, 310
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• 18 articleEmmanuelE. Bresso, AlessandroA. Furlan, PhilippeP. Noel, VincentV. Leroux, FlavioF. Maina, RosannaR. Dono and BernardB. Maigret. Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor TypeMolecules254February 2020, 938
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• 19 articlePierreP. Couvineau, HugoH. de Almeida, VincentV. Leroux, BernardB. Roques, BernardB. Maigret, CatherineC. Llorens-Cortes and XavierX. Iturrioz. Structural insight into the catalytic mechanism and inhibitor binding of aminopeptidase ABiochemical Journal47721November 2020, 4133-4148
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• 20 articleKevinK. Dalleau, MiguelM. Couceiro and MalikaM. Smaïl-Tabbone. Unsupervised Extra Trees: a stochastic approach to compute similarities in heterogeneous data.International Journal of Data Science and AnalyticsLecture Notes in Computer Science12080March 2020, 132-144
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• 21 articleJoao PedroJ. Ferreira, AnneA. Pizard, Jean-LoupJ.-L. Machu, EmmanuelE. Bresso, Hans-PeterH.-P. Brunner-LaRocca, NicolasN. Girerd, CélineC. Leroy, ArantxaA. González, JavierJ. Díez, StephaneS. Heymans, Marie-DominiqueM.-D. Devignes, PatrickP. Rossignol and FaiezF. Zannad. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO‑TARGETS case–control analysesClinical Research in Cardiology10912020, 22–33
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• 22 articleNicolasN. Girerd, EmmanuelE. Bresso, Marie-DominiqueM.-D. Devignes and PatrickP. Rossignol. Insulin-like growth factor binding protein 2: A prognostic biomarker for heart failure hardly redundant with natriuretic peptidesInternational Journal of Cardiology300February 2020, 252-254
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• 23 article FrançoisF. Inizan, MyriamM. Hanna, MaximM. Stolyarchuk, IsaureI. Chauvot de Beauchêne and LubaL. Tchertanov. The First 3D Model of the Full-Length KIT Cytoplasmic Domain Reveals a New Look for an Old Receptor Scientific Reports March 2020
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• 24 article JoëlJ. Legrand, RomainR. Gogdemir, CedricC. Bousquet, KevinK. Dalleau, Marie-DominiqueM.-D. Devignes, WilliamW. Digan, Chia-JuC.-J. Lee, Ndeye-CoumbaN.-C. Ndiaye, NadineN. Petitpain, PatriceP. Ringot, MalikaM. Smaïl-Tabbone, YannickY. Toussaint and AdrienA. Coulet. PGxCorpus, a manually annotated corpus for pharmacogenomics Scientific Data 7 3 January 2020
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• 25 articleMaria ElisaM. Ruiz­ Echartea, DavidD. Ritchie and IsaureI. Chauvot de Beauchêne. Using Restraints in EROS-Dock Improves Model Quality in Pairwise and Multicomponent Protein DockingProteins - Structure, Function and Bioinformatics888August 2020, 1121-1128
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• 26 article BishnuB. Sarker, DavidD. Ritchie and SabeurS. Aridhi. GrAPFI: predicting enzymatic function of proteins from domain similarity graphs BMC Bioinformatics April 2020
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• 27 articleMalikaM. Smaïl-Tabbone and BastienB. Rance. Contributions from the 2019 Literature on Bioinformatics and Translational InformaticsIMIA Yearbook of Medical Informatics2901August 2020, 188-192
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• 28 articleSusanS. Stienen, Joao PedroJ. Ferreira, MasatakeM. Kobayashi, GregoireG. Preud'homme, DanielaD. Dobre, Jean-LoupJ.-L. Machu, KévinK. Duarte, EmmanuelE. Bresso, Marie-DominiqueM.-D. Devignes, NataliaN. López Andrés, NicolasN. Girerd, SvendS. Aakhus, GiuseppeG. Ambrosio, Hans-PeterH.-P. Brunner-La Rocca, RicardoR. Fontes-Carvalho, Alan GA. Fraser, LoekL. van Heerebeek, StephaneS. Heymans, GillesG. de Keulenaer, PaoloP. Marino, KennethK. McDonald, AlexandreA. Mebazaa, ZoltànZ. Papp, RiccardoR. Raddino, CarstenC. Tschöpe, WalterW. Paulus, FaiezF. Zannad and PatrickP. Rossignol. Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)Biomarkers252February 2020, 201-211
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International peer-reviewed conferences

• 29 inproceedings AlexandreA. Bazin, MiguelM. Couceiro, Marie-DominiqueM.-D. Devignes and AmedeoA. Napoli. Explaining Multicriteria Decision Making with Formal Concept Analysis Concept Lattices and Applications 2020 2668 CEUR Workshop Proceedings Tallinn, Estonia June 2020
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• 30 inproceedingsKevinK. Dalleau, MiguelM. Couceiro and MalikaM. Smaïl-Tabbone. Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in GraphsIDA 2020 -18th International Symposium on Intelligent Data Analysis12080Lecture Notes in Computer ScienceKonstanz / Virtual, Germanyhttps://ida2020.org/June 2020, 132-144
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• 31 inproceedings AntoineA. Moniot, RohitR. Roy, YannY. Guermeur and IsaureI. Chauvot de Beauchêne. Docking of RNA Hairpin on Protein Using a Fragment-Based Method Actes JOBIM 2020 JOBIM Montpellier, France July 2020
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• 32 inproceedings PierreP. Monnin, EmmanuelE. Bresso, MiguelM. Couceiro, MalikaM. Smaïl-Tabbone, AmedeoA. Napoli and AdrienA. Coulet. Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study 1st international conference "Algebras, graphs and ordered sets" (Algos 2020) Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) Nancy, France https://algos2020.loria.fr/ August 2020
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• 33 inproceedingsBishnuB. Sarker, NavyaN. Khare, Marie-DominiqueM.-D. Devignes and SabeurS. Aridhi. Graph Based Automatic Protein Function Annotation Improved by Semantic SimilarityIWBBIO 2020 - 8th International Work-Conference on Bioinformatics and Biomedical Engineering12108GRANADA, SpainApril 2020, 261-272
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Scientific book chapters

• 34 inbookJonathanJ. Lima, BernardB. Maigret, DianaD. Fernandez, JenniferJ. Decloquement, DaniloD. Pinho, Erika V.S.E. Albuquerque, MarceloM. Rodrigues and NataliaN. Martins. Searching in Silico Novel Targets for Specific Coffee Rust Disease ControlLecture Notes in Computer Science, vol 11347. SpringerApril 2020, 109-115
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Reports & preprints

• 35 report ClémentC. Bellanger, JaneJ. Hung, NyomanN. Juniarta, VincentV. Leroux, BernardB. Maigret and AmedeoA. Napoli. Chemoinformatics approaches to help antibacterial discovery Inria Nancy - Grand Est May 2020
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• 36 misc WissemW. Inoubli, SabeurS. Aridhi, Haithem ·H. Mezni, MondherM. Maddouri and EngelbertE. Mephu Nguifo. A Distributed and Incremental Algorithm for Large-Scale Graph Clustering June 2020
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Other scientific publications

• 37 misc SandrineS. Gulberti, IsabelleI. Bertin-Jung, IsaureI. Chauvot de Beauchêne, ChristelC. Valencia-Schmitt, PascalP. Villa, BernardB. Maigret and SylvieS. Fournel-Gigleux. Vers un nouveau traitement des mucopolysaccharidoses ? Recherche d’inhibiteurs de la β1,4-galactosyltransférase 7 (β4GalT7) par des approches combinées de criblage expérimental et virtuel Strasbourg, France February 2020
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