Doctorant F/H Deciphering long-range communications within macromolecular complexes

Doctorant F/H Deciphering long-range communications within macromolecular complexesFonction : DoctorantThis 3-year PhD position is funded by the prestigious Programme Inria Quadrant (PIQ) for the project DynaNova, which aims to advance our understanding of conformational dynamics and allosteric communication in macromolecular complexes. The successful candidate will develop novel co-operative message-passing graph transformer architecture that learns conformational heterogeneity from molecular dynamics (MD) simulations by encoding the underlying dynamics of atomic interactions and correlations in macromolecular complexes. You will join the Delta team at Inria (Université de Lorraine), working closely with Dr. Yasaman Karami and Dr. Hamed Khakzad, experts in conformational dynamics, allostery, and deep learning for structural biology. The team is growing and offers a highly interdisciplinary environment that brings together researchers in structural bioinformatics, computational chemistry, biophysics, and machine learning. We have access to major national HPC facilities (Grid5000, Jean Zay, GENCI allocations), including large-scale GPU resources.Biomolecular function is driven by both structure and dynamics. Understanding long-range communication within macromolecular complexes is essential for deciphering molecular mechanisms and for developing therapeutic strategies. While deep learning has revolutionized structural prediction (e.g., AlphaFold2), allosteric signaling remains poorly understood, largely due to the scarcity of dynamic data. Our group recently developed:DynaRepo, a database of molecular dynamics trajectories of more than 700 macromolecular complexes (~5.5 bilions of frames) [1]. DynaRepo is the first MDDB node in France: https://dynarepo.inria.fr/.ComPASS, a graph-based method for identifying communication networks in protein–protein and protein–nucleic-acid assemblies [2].DynamicGT, a dynamic-aware graph transformer for predicting binding sites in flexible and disordered regions [3].Building on these foundations, DynaNova will leverage a large MD dataset (DynaRepo) and advanced GNN/Transformer models to uncover long-range communication pathways within macromolecular complexes. The PhD candidate will lead the development of an innovative deep learning framework to learn conformational heterogeneity and decode long-range communications within macromolecular complexes.References:[1] Mokhtari, O., Bignon, E., Khakzad, H., & Karami, Y. (2025). DynaRepo: the repository of macromolecular conformational dynamics. Nucleic Acids Research, gkaf1130.[2] Bheemireddy, S., González-Alemán, R., Bignon, E., & Karami, Y. (2025). Communication pathway analysis within protein-nucleic acid complexes. Journal of Chemical Theory and Computation.[3] Mokhtari, O., Grudinin, S., Karami, Y., & Khakzad, H. (2025). DynamicGT: a dynamic-aware geometric transformer model to predict protein binding interfaces in flexible and disordered regions. bioRxiv.ResponsibilitiesDesign and implement novel graph neural network architectures (Message-passing GNNs, Graph Transformers, graph community-aware latent spaces).Integrate dynamic, geometric, and biophysical features extracted from large MD simulations.Train and benchmark models on state-of-the-art datasets.Collaborate with PhD and Master students, contributing to supervision and scientific guidance.Contribute to publications, open-source software development, and conference presentations.Participate in the preparation of a webserver and tools to disseminate the developed methods.QualificationsMaster in Computer Science, Machine Learning, Bioinformatics, Computational Biology, or related fields.Strong experience in deep learning, ideally with PyTorch.Proven experience with graph neural networks, geometric deep learning, or transformers is a major advantage.Practical knowledge of Python, clean coding practices, and reproducible ML workflows.Familiarity with protein structure, biophysics, or molecular modeling (MD, docking, etc.) is highly desirable.Ability to work independently, collaborate in a multidisciplinary environment, and communicate effectively in English.Applications without a strong machine learning and/or computer science component cannot be considered. #J-18808-Ljbffr