petrpopov

Petr Popov

Dr. Petr Popov received PhD in Applied Math and Computer Science at Inria – Grenoble Rhone-Alpes, where he was developing algorithms for molecular modelling and computational chemistry in the Nano-D team. Later Dr. Popov joined Moscow Institute of Physics and Technology and the Bridge Institute of University of Southern California, where he developed structure analysis and machine learning tools to study structure and function of transmembrane proteins. Dr. Popov contributed to the structure determination of G-protein coupled receptors, which are one of the most important pharmacological targets, resulting in publications in top-ier scientific journals. His research mainly focuses on machine learning techniques applied for structure-based drug discovery.
  • Computational design of molecules with target properties
  • ML-based virtual screening of molecular complexes
  • ML-based prediction models for molecular properties
  • Molecular modelling

Yang, Shifan, et al. “Crystal structure of the Frizzled 4 receptor in a ligand-free state.” Nature 560.7720 (2018): 666.
https://www.nature.com/articles/s41586-018-0447-x

Popov, Petr, et al. “Computational design of thermostabilizing point mutations for G protein-coupled receptors.” eLife 7 (2018): e34729.
https://cdn.elifesciences.org/articles/34729/elife-34729-v1.pdf

Peng, Yao, et al. “5-HT2C receptor structures reveal the structural basis of GPCR polypharmacology.” Cell 172.4 (2018): 719-730.
https://www.sciencedirect.com/science/article/pii/S0092867418300011

Popov, Petr et al. “Eurecon: Equidistant uniform rigid-body ensemble constructor.” Journal of Molecular Graphics and Modelling 80 (2018): 313-319.
https://www.sciencedirect.com/science/article/pii/S1093326317308148

Neveu, Emilie, et al. “RapidRMSD: Rapid determination of RMSDs corresponding to motions of flexible molecules.” Bioinformatics (2018).
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty160/4938487

Batyuk, Alexander, et al. “Native phasing of x-ray free-electron laser data for a G protein–coupled receptor.” Science Advances 2.9 (2016): e1600292.
http://advances.sciencemag.org/content/2/9/e1600292.abstract

Neveu, Emilie, et al. “PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation.” Bioinformatics 32.17 (2016): i693-i701.
http://bioinformatics.oxfordjournals.org/content/32/17/i693.short

Lensink, Marc F., et al. “Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment.” Proteins: Structure, Function, and Bioinformatics (2016).
http://onlinelibrary.wiley.com/doi/10.1002/prot.25007/full

S Grudinin et al., Predicting binding poses and affinities in the CSAR 2013-2014 docking exercises using the knowledge-based Convex-PL potential, Journal of Chemical Information and Modeling, 2015.
http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00339

P Popov et al., Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates, Journal of Chemical Information and Modeling, 2015.
http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00372

P Popov et al., Rapid determination of RMSDs corresponding to macromolecular rigid body motions, Journal of computational chemistry, 2014.
http://onlinelibrary.wiley.com/doi/10.1002/jcc.23569/full

P Popov et al., DockTrina: Docking triangular protein trimers, Proteins: Structure, Function, and Bioinformatics, 2014.
http://onlinelibrary.wiley.com/doi/10.1002/prot.24344/full

Moretti, Rocco, et al. “Community‐wide evaluation of methods for predicting the effect of mutations on protein–protein interactions.” Proteins: Structure, Function, and Bioinformatics 81.11 (2013): 1980-1987.
https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.24356