Publications

1. Morozov, Alexander, Davide Zgyatti, and Petr Popov. “Equidistant and Uniform Data Augmentation for 3D Objects.” IEEE Access (2022).
   https://ieeexplore.ieee.org/abstract/document/9662385
2. Kozlovskii, Igor, and Petr Popov. “Structure-based deep learning for binding site detection in nucleic acid macromolecules.” NAR genomics and bioinformatics 3.4 (2021): lqab111.
   https://academic.oup.com/nargab/article/3/4/lqab111/6441762
3. Kozlovskii, Igor, and Petr Popov. “Protein–peptide binding site detection using 3D convolutional neural networks.” Journal of chemical information and modeling 61.8 (2021): 3814-3823.
   https://pubs.acs.org/doi/full/10.1021/acs.jcim.1c00475
4. Michaelian, Nairie, et al. “Structural insights on ligand recognition at the human leukotriene B4 receptor 1.” Nature communications 12.1 (2021): 1-12.
   https://www.nature.com/articles/s41467-021-23149-1
5. Popov, Petr, et al. “Rationalizing Viral Drug Target Identification Using Computational Approaches: The SARS-CoV-2 Spike Glycoprotein S Case Study.” (2020). 
   https://chemrxiv.org/articles/preprint/Rationalizing_Viral_Drug_Target_Identification_Using_Computational_Approaches_The_SARS-CoV-2_Spike_Glycoprotein_S_Case_Study/13251560
   
6. Kozlovskii, Igor, and Petr Popov. “Spatiotemporal identification of druggable binding sites using deep learning.” Nature Communications Biology (2020).
   https://www.nature.com/articles/s42003-020-01350-0
7. Zaucha, Jan, et al. “Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins.” Briefings in Bioinformatics (2020).
   https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbaa132/5872174
8. Karlov, Dmitry S., et al. “graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes.” ACS omega 5.10 (2020): 5150-5159.
   https://pubs.acs.org/doi/abs/10.1021/acsomega.9b04162
9. Wang, Jingjing, et al. “The structural study of mutation-induced inactivation of human muscarinic receptor M4.” IUCrJ 7.2 (2020).
   https://scripts.iucr.org/cgi-bin/paper?jt5041
10. Gusach, Anastasiia, et al. “Structural basis of ligand selectivity and disease mutations in cysteinyl leukotriene receptors.” Nature Communications 10.1 (2019): 1-9.
    https://www.nature.com/articles/s41467-019-13348-2
11. Luginina, Aleksandra, et al. “Structure-based mechanism of cysteinyl leukotriene receptor inhibition by antiasthmatic drugs.” Science advances (2019).
    https://advances.sciencemag.org/content/5/10/eaax2518/tab-figures-data
12. Popov, Petr, et al. “Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.” PloS one 14.7 (2019): e0219452.
    https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0219452
13. Popov, Petr, et al. “Controlled‐advancement rigid‐body optimization of nanosystems.” Journal of computational chemistry (2019).
    https://onlinelibrary.wiley.com/doi/full/10.1002/jcc.26016
14. Popov, Petr, et al. “Computational design for thermostabilization of GPCRs.” Current Opinion in Structural Biology 55 (2019): 25-33
    https://www.sciencedirect.com/science/article/pii/S0959440X18301374
15. Li, Xiaoting, et al. “Crystal Structure of the Human Cannabinoid Receptor CB2.” Cell 176.3 (2019): 459-467.
    https://www.sciencedirect.com/science/article/pii/S0092867418316258
16. Audet, Martin, et al. “Crystal structure of misoprostol bound to the labor inducer prostaglandin E 2 receptor.” Nature chemical biology (2019): 1.
    https://www.nature.com/articles/s41589-018-0160-y
    
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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