Sergei Grudinin's webpage

Sergei Grudinin

CNRS Researcher in Structural Bioinformatics and Modeling of Life Systems. Jean Kunzmann Laboratory, Grenoble Alpes University.

Member of the DAO optimization team, 2021-.
Head of the GruLab team, 2021-.

Accreditation to supervise research (HDR, 2024) from Grenoble Alpes Université. Head of the Nano-D Inria team (2018-2021). Researcher grant (2023-2026, CellModeling project) from the French National Research Agency and National Science Foundation, USA. Researcher grant (2011-2016, PEPSI project) from the French National Research Agency. Postdoc (2007-2009) in Inria Grenoble . Postdoc (2006-2007) at the Theory II Institute at FZJ Juelich. PhD in Computational Biophysics (2002-2005) at FZJ Juelich. See my resume.

Team

Team and collaborators

Florian Echelard

PhD student. Development of novel methods in structural bioinformatics for peripheral proteins. Co-supervised with Nathalie Reuter. Funded by the Research Council of Norway.

Dmitrii Zhemchuzhnikov

PhD student. Volumetric data processing. Funded by the Ministry grant.

Khan-Chi Nguyen-Pham

PhD student. Development of language models for virtual drug screening. Co-supervised with Yung-Sing Wong. Funded by Département de Pharmacochimie Moléculaire.

Davy Darankoum

PhD student. Development of deep-learning methods for EEG signals. Co-supervised with Julien Volle. Funded by SynapCell.

Elodie Laine

Professor at Sorbonne University, Paris, France. Protein sequence to structures and functions. Co-advises V. Lombard and Julien Nguyen Van.

Valentin Lombard

PhD student. Geometric deep manifold learning combined with Natural Language Processing for protein movies. Co-supervised with Elodie Laine. SCAI doctoral grant.

Julien Nguyen Van

PhD student. Deciphering the complexity of proteoform interactions with evolutionary- and physically-informed protein language models. Co-supervised with Elodie Laine. Funded by the ERC.

Emmanuel Jehanno

PhD student. Development of deep-learning methods for material science. Co-supervised with Julien Mairal. Funded by the ERC.

Roman Klypa

PhD student. Novel Generative Models with Equivariant Properties for 3D Biological Data. Co-supervised with Alberto Bietti. Funded by École Polytechnique.

Rémi Vuillemot

PostDoc. Novel methods for integrative structural bioinformatics. Funded by Grenoble University.

Kliment Olechnovič

PostDoc. Development of novel tessellation algorithms. Funded by Marie Skłodowska-Curie Actions.

Pablo Chacon

Research Staff Scientist at Institute of Physical Chemistry (IQFR-CSIC), Group leader (IQF Madrid, Spain).

Nathalie Reuter

Professor at Bergen University (Norway), Group leader.

Mikael Lund

Professor at Lund University (Sweden), Group leader.

Eric Deeds

Professor at UCLA (USA), Group leader.

Julien Mairal

Researcher at Inria, Group leader.

Yung-Sing Wong

CNRS Researcher, Group leader.

Dina Schneidman

Professor at Hebrew University of Jerusalem, Group leader.
Community efforts - CASP (Zoom), CAPRI, Elixir 3D Bioinfo, ML4NGP, MASIM.

We are hiring!

We are looking for highly talented and motivated PhDs and Post-doctoral fellows, with backgrounds in applied mathematics and physics, algorithms and computer science, and structural bioinformatics.

Get in touch!

Publications

Full list here

Vuillemot, R., Pellequer, J.-L., & Grudinin, S. (2024). AFMfit: Deciphering conformational dynamics in AFM data using fast nonlinear NMA and FFT-based search. bioRxiv, 2024–2006.

Grudinin, S. (2024). New Challenges in Structural Bioinformatics: When Physics Meets Big Data. HDR thesis. Université Grenoble Alpes.

Olechnovic, K., & Grudinin, S. (2024). Voronota-LT: efficient, flexible and solvent-aware tessellation-based analysis of atomic interactions. bioRxiv, 2024–2002.

Lombard, V., Grudinin, S., & Laine, E. (2024). Explaining Conformational Diversity in Protein Families through Molecular Motions. bioRxiv, 2024–2002.

Zhemchuzhnikov, D., & Grudinin, S. (2024). ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D. arXiv Preprint arXiv:2403. 19612.

Zhemchuzhnikov, D., & Grudinin, S. (2024). On the Fourier analysis in the SO (3) space: EquiLoPO Network. arXiv Preprint arXiv:2404. 15979.

Golub, M., Moldenhauer, M., Matsarskaia, O., Martel, A., Grudinin, S., Soloviov, D., … Pieper, J. (2023). Stages of OCP--FRP Interactions in the Regulation of Photoprotection in Cyanobacteria, Part 2: Small-Angle Neutron Scattering with Partial Deuteration. The Journal of Physical Chemistry B, 127(9), 1901–1913.

Appasamy, S. D., Berrisford, J., Gaborova, R., Nair, S., Anyango, S., Grudinin, S., … Others. (2023). Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data. Scientific Data, 10(1), 853.

Vakser, I. A., Grudinin, S., Jenkins, N. W., Kundrotas, P. J., & Deeds, E. J. (2022). Docking-based long timescale simulation of cell-size protein systems at atomic resolution. Proceedings of the National Academy of Sciences, 119(41), e2210249119.

Zhemchuzhnikov, D., Igashov, I., & Grudinin, S. (2022). 6DCNN with roto-translational convolution filters for volumetric data processing. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 4707–4715.

Fidelis, K., & Grudinin, S. (2021). Session introduction: AI-driven Advances in Modeling of Protein Structure. Pacific Symposium on Biocomputing 2022, 1–9.

Pierré, W., Hervé, L., Paviolo, C., Mandula, O., Remondiere, V., Morales, S., … Others. (2022). 3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework. Applied Optics, 61(12), 3337–3348.

Igashov, I., Olechnovič, K., Kadukova, M., Venclovas, Č., & Grudinin, S. (2021). VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures. Bioinformatics, 37(16), 2332–2339.

Kadukova, Maria, Machado, K. dos S., Chacón, P., & Grudinin, S. (2021). KORP-PL: a coarse-grained knowledge-based scoring function for protein--ligand interactions. Bioinformatics, 37(7), 943–950.

Igashov, I., Pavlichenko, N., & Grudinin, S. (2021). Spherical convolutions on molecular graphs for protein model quality assessment. Machine Learning: Science and Technology, 2(4), 045005.

Laine, E., & Grudinin, S. (2021). HOPMA: Boosting protein functional dynamics with colored contact maps. The Journal of Physical Chemistry B, 125(10), 2577–2588.

Laine, E., Eismann, S., Elofsson, A., & Grudinin, S. (2021). Protein sequence-to-structure learning: Is this the end (-to-end revolution)? Proteins: Structure, Function, and Bioinformatics, 89(12), 1770–1786.

Software

Try them out

All
Scattering
Symmetries
Motions
Proteins
Drugs
Algorithms
ML
DL

AFMFit

How to reconstruct molecular motions using low-res AFM data?

R. Vuillemot, J.L. Pellequer, and S. Grudinin* (2024) Submitted

DANCE

From sparse static structures to movies!

V. Lombard, S. Grudinin* and E. Laine* (2024) Scientific Data

6DCNN

6D roto-translational convolution filters for volumetric data processing

D. Zhemchuzhnikov, I. Igashov, S Grudinin* (2022)

ILPONet

Invariance to rotations and translations of local patterns in volumetric data

D. Zhemchuzhnikov, S Grudinin (2024)

EquiLoPO

Roto-translational equivariance and activation in the Fourier space

D. Zhemchuzhnikov, S Grudinin (2024)

S-GCN

Spherical graph convolutional networks

I. Igashov, N. Pavlichenko, and S. Grudinin* (2021)

VoroCNN

Convolutional neural network trained on Voronoi tessellation of 3D protein structures

I. Igashov, K. Olechnovič, M. Kadukova, Česlovas Venclovas, and S. Grudinin* (2021)

HOPMA

More freedom to the proteins!

E. Laine* and S. Grudinin* (2021) J. Phys. Chem. B

NOLB Normal Modes[Video]

How does my favourite protein move?

S. Grudinin+, E. Laine+, and A. Hoffmann (2020) Biophys. J. A. Hoffmann and S. Grudinin (2017) J. Chem. Th. Comput.

KORP-PL

Virtual screening pipeline

M. Kadukova, K. dos Santos Machado, P. Chacón*, S. Grudinin* (2021)

Knodle

KNOwledge-Driven Ligand Extractor

M. Kadukova, S. Grudinin* (2016)

Convex-PL

Knowledge-based scoring function for protein-ligand interactions

M. Kadukova, S. Grudinin* (2011-2020)

AnAnaS

Analytical Analyzer of Symmetries

G. Pagès & S. Grudinin* (2018)

Sam

Ultra-fast FFT-based protein symmetry asembler

D. Ritchie & S. Grudinin (2015)

Pepsi Suite

Multipole-based SAXS and SANS profile computation method

S. Grudinin (2016-now)

DeepSymmetry

3DCNN to detect structural repetitions in proteins and their density maps

G. Pagès, and S. Grudinin* (2019)

Ornate

3DCNN built on oriented local protein frames

G. Pagès, B. Charmettant, and S. Grudinin* (2019)

Sbrod

Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

M. Karasikov, and S. Grudinin* (2018)

RapidRMSD

Rapid computations of the root mean square deviations (RMSD) of flexible molecules

E. Neveu, P. Popov, A. Hoffmann, and S. Grudinin* (2018)

RigidRMSD

Constant-time calculation of the root mean square deviations (RMSD) for rigid molecules

P. Popov, and S. Grudinin* (2014)

Conferences

Scientific event organization

Communications for the general public

Le Figaro

2018/12, 2023/03

Bataille d’IA sur la géométrie des protéines

DeepMind, l’IA de Google, est championne en biologie moléculaire

La Recherche

2024/03, 2022/08, 2021/07

La quasi-totalité des structures de protéines désormais prédites

Sciences et Avenir

2022/01, 2021/04

La vague déclenchée par l'exploit de l'algorithme de DeepMind Alphafold n'est pas retombée

Une intelligence artificielle prédit la forme des protéines

Sciences et Vie

2021/08, 2021/11 (Junior)

Intelligence artificielle : la biologie moléculaire entre dans l’ère de la prédiction

Data Analytics Post

2021/01

L’apprentissage profond replie les protéines « in silico »

Contact

Location:

LJK, UMR 5224
CNRS - Grenoble Alpes University
Bâtiment IMAG - first floor
150 place du Torrent
38401 Saint Martin d'Hères, FRANCE

Email:

sergei.grudinin@univ-grenoble-alpes.fr