Molecular Modeling
The BioCIS molecular modeling group develops computational methods for studying biomolecular structures, dynamics, and interactions to support drug discovery and development. Our research involves structural modeling of proteins and protein-ligand interactions, simulations of biomolecular condensates and polymeric nanocarriers, and the design of peptide-derived inhibitors of protein-protein interactions. We also integrate deep-learning approaches with physics-based modeling to characterize biomolecular conformational ensembles, identify interaction interfaces, and support target identification.
Research areas of the molecular modeling group
Axis 1 — Modeling protein structural ensembles and protein-ligand interactions
Structure-based drug design is the main strategy used in BioCIS molecular modeling group to support the research and development of new therapeutic compounds. Thus, one of our major activities is to investigate the three-dimensional structures of drug targets which are mainly proteins and protein-protein interactions (PPIs). We are particularly interested in intrinsically disordered proteins (IDPs) and their complexes which conformational ensembles are often difficult to be characterized experimentally. For this purpose, we generally use classical or enhanced molecular dynamics (MD) simulations, as well as protein-protein docking calculations.
Axis 2 — Simulations of biomolecular condensates and polymeric nanoparticles
Many intrinsically disordered proteins play a role in forming membrane-less organelles, which are involved in spatiotemporal compartmentalization and regulation of several biochemical reactions. These biomolecular condensates are formed through a liquid-liquid phase separation (LLPS) process, which is finely controlled by environmental conditions, such as temperature, pH, and/or ionic strength. To gain insight into the physicochemical factors that govern the LLPS of IDPs, our group is developing and using biomolecular coarse-grained (CG) models to enable simulations of multichain phase separation within various conditions.
Related to our interest in biomolecular condensates, our group has established strong collaborations with Institute Galien Paris-Saclay to help the development of polymers for efficient drug loading into nanocarriers, controlled release in targeted tissues, and biodegradability. By using CG MD simulations, we study the self-assembly and dissolution processes of thermo-sensitive polymers and investigate the supramolecular organization and dynamics of drug polymer nanoparticles.
Axis 3 — Design of peptide-derived inhibitors of protein-protein interactions
As protein-protein interactions (PPIs) are involved in many physio-pathological processes, searching for PPIs inhibitors is a promising but challenging strategy in drug design. As part of the FLUOPEPIT team, the molecular modeling group develops and applies computational methods to design and study peptide-derived compounds that (i) would likely bind a targeted protein-protein interface with high affinity and (ii) would have good proteolytic stability and membrane permeability. Our strategy for this purpose is a fragment-based approach that identifies the optimal sequences of canonical or non-canonical macrocyclic peptides for binding to a specific protein surface. In addition, we use enhanced MD simulations to characterize conformational ensembles of various (cyclic, fluorinated, aza… ) peptide derivatives, as well as their membrane permeability.
Axis 4 — Deep-learning approaches to model biomolecules and their complexes
A more recent research axis focuses on the integration of artificial intelligence approaches for biophysical modeling of biomolecules and their interactions. On the one hand, we are interested in developing novel deep-learning methods to characterize structural ensembles for intrinsically disordered proteins, integrating Axis 1. On the other hand, we also combine deep-learning approaches with physics-based methods used in Axis 2 to investigate complexes and aggregates of biomolecules and identify interaction interfaces, specifically in biomolecular condensates. Finally, in relation to Axis 3, we aim to address the identification of biological targets of active compounds by building curated datasets and appropriate machine-learning models.
Recent publications
- Temperature-Dependent Coarse-Grained Model for Simulations of Intrinsically Disordered Protein LCST and UCST Liquid-Liquid Phase Separations. Jiang Y, Ha-Duong T. J Chem Theory Comput. 2025 ; 21 : 4939-4952.
- Composition and Conformation of Hetero- versus Homo-Fluorinated Triazolamers Influence their Activity on Islet Amyloid Polypeptide Aggregation. Laxio Arenas J, Lesma J, Ha-Duong T, Ranjan Sahoo B, Ramamoorthy A, Tonali N, Soulier JL, Halgand F, Giraud F, Crousse B, Kaffy J, Ongeri S. 2024 ; 30 : e202303887.
- Coarse-Grained Model-Assisted Design of Polymer Prodrug Nanoparticles with Enhanced Cytotoxicity: A Combined Theoretical and Experimental Study. Gao P, Ha-Duong T, Nicolas J. Angew Chem Int Ed Engl. 2024 ; 63 : e202316056.
- Polyfluoroalkyl Chain-Based Assemblies for Biomimetic Catalysis. Liu N, Gao P, Lu HY, Fang L, Nicolas J, Ha-Duong T, Shen JS. Chemistry. 2024 ; 30 : e202302669.
- Proteolytically Stable Diaza-Peptide Foldamers Mimic Helical Hot Spots of Protein-Protein Interactions and Act as Natural Chaperones. Shi C, Kaffy J, Ha-Duong T, Gallard JF, Pruvost A, Mabondzo A, Ciccone L, Ongeri S, Tonali N. J Med Chem. 2023 ; 66 : 12005-12017.
- Introduction of constrained Trp analogs in RW9 modulates structure and partition in membrane models. Lozada C, Gonzalez S, Agniel R, Hindie M, Manciocchi L, Mazzanti L, Ha-Duong T, Santoro F, Carotenuto A, Ballet S, Lubin-Germain N. Bioorg Chem. 2023 ; 139 : 106731.
- Understanding Passive Membrane Permeation of Peptides : Physical Models and Sampling Methods Compared. Mazzanti L, Ha-Duong T. Int J Mol Sci. 2023; 24 : 5021.
- Computational design of cyclic peptides to inhibit protein-peptide interactions. Delaunay M, Ha-Duong T. Biophys Chem. 2023 ; 296 : 106987.
- Des3PI : a fragment-based approach to design cyclic peptides targeting protein-protein interactions. Delaunay M, Ha-Duong T. J Comput Aided Mol Des. 2022 ; 36 : 605-621.
- β-Hairpin Peptide Mimics Decrease Human Islet Amyloid Polypeptide (hIAPP) Aggregation. Lesma J, Bizet F, Berardet C, Tonali N, Pellegrino S, Taverna M, Khemtemourian L, Soulier JL, van Heijenoort C, Halgand F, Ha-Duong T, Kaffy J, Ongeri S. Front Cell Dev Biol. 2021; 9 : 729001.
- Fluorinated Triazole Foldamers : Folded or Extended Conformational Preferences. Laxio-Arenas J, Xu Y, Milcent T, Van Heijenoort C, Giraud F, Ha-Duong T, Crousse B, Ongeri S. 2021 ; 86 : 241-251.
- Supramolecular Organization of Polymer Prodrug Nanoparticles Revealed by Coarse-Grained Simulations. Gao P, Nicolas J, Ha-Duong T. J Am Chem Soc. 2021; 143 : 17412-17423.
- Simulations of the Upper Critical Solution Temperature Behavior of Poly(ornithine-co-citrulline)s Using MARTINI-Based Coarse-Grained Force Fields. Molza AE, Gao P, Jakpou J, Nicolas J, Tsapis N, Ha-Duong T. J Chem Theory Comput. 2021; 17 : 4499-4511.
- Structural ensemble and biological activity of DciA intrinsically disordered region. Chan-Yao-Chong M, Marsin S, Quevillon-Cheruel S, Durand D, Ha-Duong T. J Struct Biol. 2020 ; 212 : 107573.
Members
- Tâp Ha-Duong (Full Professor)
- Liuba Mazzanti (Associate Professor)
- Çagla Okyay (Postdoc)
- Maysem Hachim (PhD)
- Zichen Feng (PhD)
- Yingmin Jang (PhD)
Alumni
- Romain Launay (Postdoc)
- Maxence Delaunay (Postdoc)
- Anne-Elisabeth Molza (Postdoc)
- Ping Gao (PhD)
- Maud Chan-Yao-Chong (PhD)
- Linh Tran (PhD)