Didier Devaurs

Didier Devaurs
The University of Edinburgh | UoE · MRC Institute of Genetics and Cancer

PhD in Artificial Intelligence
Working on cross-disciplinary projects in quantitative biomedical research

About

49
Publications
11,595
Reads
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949
Citations
Introduction
Researcher in computer science, with some background in biology, chemistry, physics and mathematics. Experiences as a software developer in the industry and as a mathematics teacher. Interested in interdisciplinary research in the fields of biomedical computing, structural bioinformatics and computational structural biology.
Additional affiliations
December 2018 - August 2020
Université Grenoble Alpes
Position
  • PostDoc Position
Description
  • Worked on molecular docking, molecular caging, as well as molecular modeling, analysis and shape reconstruction using sparse experimental data, with various collaborators
November 2014 - October 2018
Rice University
Position
  • PostDoc Position
Description
  • Member of Prof. Lydia Kavraki's lab. Conceived efficient and scalable computational methods for the structural modeling and analysis of peptides, proteins, and their complexes
July 2013 - August 2013
George Mason University
Position
  • Researcher
Description
  • Member of Prof Amarda Shehu's lab. Combined probabilistic search techniques from different fields to explore the energy landscape of peptides exhaustively and efficiently
Education
November 2011 - October 2014
Institut National Polytechnique de Toulouse
Field of study
  • Artificial intelligence
October 2004 - June 2006
Claude Bernard University Lyon 1
Field of study
  • Computer science
October 2003 - June 2004
Université Clermont Auvergne
Field of study
  • Computer science

Publications

Publications (49)
Article
Full-text available
Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining...
Article
Full-text available
Background: Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a ch...
Article
Full-text available
The complement system plays a major role in human immunity, but its abnormal activation can have severe pathological impacts. By mimicking a natural mechanism of complement regulation, the small peptide compstatin has proven to be a very promising complement inhibitor. Over the years, several compstatin analogs have been created, with improved inhi...
Article
Full-text available
An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had...
Article
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Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large ma...
Article
Full-text available
Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those co...
Article
Full-text available
PURPOSE HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunothera...
Article
Full-text available
We define a molecular caging complex as a pair of molecules, where one molecule (a "host", or a "cage") possesses a cavity that can encapsulate the other molecule (a "guest") and prevent it from escaping. Molecular caging complexes can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in material...
Article
Full-text available
Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of recepto...
Conference Paper
Full-text available
The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies a...
Poster
Full-text available
Immunotherapy is an innovative cancer treatment leveraging the immune system to eliminate tumor cells. Since all cells' surface present numerous peptides, a cancer patient's immune system can be "trained" to identify tumor cells by recognizing specific tumor-derived peptides. Peptides are presented by human leukocyte antigen (HLA) proteins that bin...
Article
Full-text available
The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies a...
Article
Full-text available
Hydrogen/deuterium exchange detected by mass spectrometry (HDX-MS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analy...
Article
Full-text available
Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decay...
Article
Full-text available
Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between...
Article
Full-text available
Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from...
Article
Full-text available
Sampling-based algorithms for path planning, such as RRT, have achieved great success, thanks to their ability to efficiently solve complex high-dimensional problems. However, standard versions of these algorithms cannot guarantee optimality or even high-quality for the produced paths. In recent years, variants of these methods, such as T-RRT, have...
Conference Paper
Full-text available
Sampling-based motion planning algorithms from the field of robotics have been very successful in exploring the conformational space of proteins. However, studying the flexibility of large proteins with hundreds or thousands of Degrees of Freedom (DoFs) remains a big challenge. Large proteins are also highly-constrained systems, which makes them mo...
Article
Full-text available
Introduction: Protein-ligand interactions play key roles in various metabolic pathways, and the proteins involved in these interactions represent major targets for drug discovery. Molecular docking is widely used to predict the structure of protein-ligand complexes, and protein flexibility stands out as one of the most important and challenging is...
Article
Full-text available
Obtaining accurate representations of energy landscapes of biomolecules such as proteins and peptides is central to the study of their physicochemical properties and biological functions. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling re...
Chapter
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Sampling-based algorithms for path planning have achieved great success during the last 15 years, thanks to their ability to efficiently solve complex high-dimensional problems. However, standard versions of these algorithms cannot guarantee optimality or even high-quality for the produced paths. In recent years, variants of these methods, taking c...
Conference Paper
Full-text available
Obtaining accurate representations of energy landscapes of biomolecules such as proteins and peptides is central to structure-function studies. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling remains challenging due to the complexity of t...
Thesis
Full-text available
Planning a path for a robot in a complex environment is a crucial issue in robotics. So-called probabilistic algorithms for path planning are very successful at solving difficult problems and are applied in various domains, such as aerospace, computer animation, and structural biology. However, these methods have traditionally focused on finding pa...
Conference Paper
Full-text available
The Transition-based RRT (T-RRT) is a variant of RRT developed for path planning on a continuous cost space, i.e. a configuration space featuring a continuous cost function. It has been used to solve complex, high-dimensional problems in robotics and structural biology. In this paper, we propose a multiple-tree variant of T-RRT, named Multi-T-RRT....
Conference Paper
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We propose a new approach for the reliable 6-dimensional quasi-static manipulation with aerial towed-cable systems. The novelty of this approach lies in the combination of results deriving from the static analysis of cable-driven manipulators with a cost-based motion-planning algorithm to solve manipulation queries. Such a combination of methods is...
Conference Paper
Full-text available
Exploring the conformational energy landscape of a molecule is an important but challenging problem because of the inherent complexity of this landscape. As part of this theme, various methods have been developed to compute transition paths between stable states of a molecule. Besides the methods classically used in biophysics/biochemistry, a recen...
Conference Paper
Full-text available
Performing aerial 6-dimensional manipulation using flying robots is a challenging problem, to which only little work has been devoted. This paper proposes a motion planning approach for the reliable 6-dimensional quasi-static manipulation with an aerial towed-cable system. The novelty of this approach lies in the use of a cost-based motion-planning...
Conference Paper
Full-text available
The Transition-based RRT (T-RRT) algorithm enables to solve motion planning problems involving configuration spaces over which cost functions are defined, or cost spaces for short. T-RRT has been successfully applied to diverse problems in robotics and structural biology. In this paper, we aim at enhancing T-RRT to solve ever more difficult problem...
Article
Full-text available
Protein–ligand interactions taking place far away from the active site, during ligand binding or release, may determine molecular specificity and activity. However, obtaining information about these interactions with experimental or computational methods remains difficult. The computational tool presented in this article, MoMA-LigPath, is based on...
Article
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This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical paralleliza-tion schemes. We evaluate them on different motion planning problems and analyze the variou...
Article
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Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction cont...
Conference Paper
Full-text available
This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. W...
Article
Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool th...
Article
Full-text available
In the context detection field, an important challenge is automatically detecting the user's task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task de...
Conference Paper
Full-text available
Supporting learning activities during work has gained momentum for organizations since work-integrated learning (WIL) has been shown to increase productivity of knowledge workers. WIL aims at fostering learning at the workplace, during work, for enhancing task performance. A key challenge for enabling task-specific, contextualized, personalized lea...
Article
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We have developed an individual-based evolving predator-prey ecosystem simulation that integrates, for the first time, a complex individual behaviour model, an evolutionary mechanism and a speciation process, at an acceptable computational cost. In this article, we analyse the species abundance patterns observed in the communities generated by our...
Article
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Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user's current work task, i.e. the current work context a user resides in. In this work we present and evaluate machine learning based work task detection methods. By viewing a work task as sequence of digital interaction patterns of mous...
Article
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We present an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a fuzzy cognitive map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g., distance to predator or prey, distance to potential breeding pa...
Conference Paper
Full-text available
Detecting the current task of a user is essential for providing her with contextualized and personalized support, and using Contextual Attention Metadata (CAM) can help doing so. Some recent approaches propose to perform automatic user task detection by means of task classifiers using such metadata. In this paper, we show that good results can be a...
Conference Paper
Full-text available
'Understanding context is vital' [1] and 'context is key' [2] signal the key interest in the context detection field. One important challenge in this area is automatically detecting the user's task because once it is known it is possible to support her better. In this paper we propose an ontology-based user interaction context model (UICO) that enh...
Conference Paper
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In this demonstration we present our KnowSe framework, developed for observing, storing, analyzing and leveraging Contextual Attention Metadata, utilizing our ontology-based user interactions context model (UICO). It includes highly contextualized knowledge services for supporting learners in a personalized and adaptive way, by exploiting the learn...
Conference Paper
Full-text available
Web service business protocols are of importance to both clients and providers, as they model the external behaviour of services. However, the business protocol is not always pub- lished together with the service interface, and this hinders automatic management. When conversation logs are available, a solution is to discover the business protocol f...
Conference Paper
Full-text available
La connaissance du protocole de conversation d’un service Web est importante, pour les utilisateurs et les fournisseurs de services car il en modélise le comportement externe. Cependant, ce protocole n’est souvent pas spécifié pendant la conception. Notre travail s’inscrit dans une thématique d’extraction du protocole de conversation temporisé d’un...
Conference Paper
Full-text available
De très divers algorithmes sont dédiés à la découverte de motifs fréquents dans les bases de données de transactions. Des initiatives collectives visant à effectuer des comparaisons de performances rigoureuses et impartiales ont vu récemment le jour. Curieusement, cette tâche est rendue difficile par le manque de jeux d'essais publics disponibles,...

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Projects

Projects (6)
Project
Developing tools for ensemble docking with SARS-CoV-2 proteins. Study PPI and protein-ligand interactions.
Project
Scientific contributions having a connection with research on the SARS-CoV-2 virus and the implications of developing COVID-19.
Project
We develop a computational screening method that predicts whether or not given pairs of molecules form a caging complex. Our method is based on a caging verification algorithm that was designed for robotic manipulation. Molecular caging prediction can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in material science.