Ivan Anishchenko is a researcher in the field of computational biology, currently based at the University of Washington, Seattle, as a postdoctoral researcher and acting instructor.
Anishchenko received his Doctor of Philosophy from the University of Kansas.
Anishchenko's previous affiliations include the University of Kansas and the National Academy of Sciences of Belarus. He has also worked at the United Institute of Informatics Problems, the University of Kansas Center for Computational Biology, and the University of Washington Department of Biochemistry.
Anishchenko's research focuses on protein structure prediction and design, macromolecular docking, and deep learning. He has co-authored 50 publications, with over 5,000 citations, and has an h-index of 18.
Ivan Anishchenko Vilya N/A Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, … Science 373 (6557), 871-876, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:O3NaXMp0MMsC Cited by: 3650
Improved protein structure prediction using predicted interresidue orientations J Yang, I Anishchenko, H Park, Z Peng, S Ovchinnikov, D Baker Proceedings of the National Academy of Sciences 117 (3), 1496-1503, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:e5wmG9Sq2KIC Cited by: 1312
Robust deep learning–based protein sequence design using ProteinMPNN J Dauparas, I Anishchenko, N Bennett, H Bai, RJ Ragotte, LF Milles, … Science 378 (6615), 49-56, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:g5m5HwL7SMYC Cited by: 646
De novo protein design by deep network hallucination I Anishchenko, SJ Pellock, TM Chidyausiku, TA Ramelot, S Ovchinnikov, … Nature 600 (7889), 547-552, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:TFP_iSt0sucC Cited by: 439
The trRosetta server for fast and accurate protein structure prediction Z Du, H Su, W Wang, L Ye, H Wei, Z Peng, I Anishchenko, D Baker, … Nature protocols 16 (12), 5634-5651, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:vV6vV6tmYwMC Cited by: 379
Computed structures of core eukaryotic protein complexes IR Humphreys, J Pei, M Baek, A Krishnakumar, I Anishchenko, … Science 374 (6573), eabm4805, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:lSLTfruPkqcC Cited by: 374
Scaffolding protein functional sites using deep learning J Wang, S Lisanza, D Juergens, D Tischer, JL Watson, KM Castro, … Science 377 (6604), 387-394, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:pqnbT2bcN3wC Cited by: 265
Protein interaction networks revealed by proteome coevolution Q Cong, I Anishchenko, S Ovchinnikov, D Baker Science 365 (6449), 185-189, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:HDshCWvjkbEC Cited by: 236
Improved protein structure refinement guided by deep learning based accuracy estimation N Hiranuma, H Park, M Baek, I Anishchenko, J Dauparas, D Baker Nature communications 12 (1), 1340, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:JV2RwH3_ST0C Cited by: 212
De novo design of luciferases using deep learning AHW Yeh, C Norn, Y Kipnis, D Tischer, SJ Pellock, D Evans, P Ma, … Nature 614 (7949), 774-780, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3s1wT3WcHBgC Cited by: 195
Origins of coevolution between residues distant in protein 3D structures I Anishchenko, S Ovchinnikov, H Kamisetty, D Baker Proceedings of the National Academy of Sciences 114 (34), 9122-9127, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:Wp0gIr-vW9MC Cited by: 182
Protein sequence design by conformational landscape optimization C Norn, BIM Wicky, D Juergens, S Liu, D Kim, D Tischer, B Koepnick, … Proceedings of the National Academy of Sciences 118 (11), e2017228118, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:YFjsv_pBGBYC Cited by: 169
Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: a CASP‐CAPRI experiment MF Lensink, S Velankar, A Kryshtafovych, SY Huang, … Proteins: Structure, Function, and Bioinformatics 84, 323-348, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3fE2CSJIrl8C Cited by: 168
Generalized biomolecular modeling and design with RoseTTAFold All-Atom R Krishna, J Wang, W Ahern, P Sturmfels, P Venkatesh, I Kalvet, GR Lee, … Science 384 (6693), eadl2528, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:zA6iFVUQeVQC Cited by: 110
Protein contact prediction using metagenome sequence data and residual neural networks Q Wu, Z Peng, I Anishchenko, Q Cong, D Baker, J Yang Bioinformatics 36 (1), 41-48, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:mB3voiENLucC Cited by: 96
Dockground: a comprehensive data resource for modeling of protein complexes PJ Kundrotas, I Anishchenko, T Dauzhenka, I Kotthoff, D Mnevets, … Protein Science 27 (1), 172-181, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:9ZlFYXVOiuMC Cited by: 86
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA M Baek, R McHugh, I Anishchenko, H Jiang, D Baker, F DiMaio Nature methods 21 (1), 117-121, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:fPk4N6BV_jEC Cited by: 60
ProteinGCN: Protein model quality assessment using graph convolutional networks S Sanyal, I Anishchenko, A Dagar, D Baker, P Talukdar BioRxiv, 2020.04. 06.028266, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:RHpTSmoSYBkC Cited by: 51
Efficient and accurate prediction of protein structure using RoseTTAFold2 M Baek, I Anishchenko, IR Humphreys, Q Cong, D Baker, F DiMaio BioRxiv, 2023.05. 24.542179, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:rO6llkc54NcC Cited by: 50
Computational model of the HIV-1 subtype A V3 loop: study on the conformational mobility for structure-based anti-AIDS drug design AM Andrianov, IV Anishchenko Journal of Biomolecular Structure and Dynamics 27 (2), 179-193, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:u5HHmVD_uO8C Cited by: 50
David Baker UKqIqRsAAAAJ
Sergey Ovchinnikov 8KJ9gf4AAAAJ
Petras Kundrotas JvYJSc4AAAAJ
Justas Dauparas jlgADF8AAAAJ
Alexander Tuzikov EL7ilZEAAAAJ
Minkyung Baek HNPzCLoAAAAJ
Hahnbeom Park Y8Tqu4MAAAAJ
Christoffer Norn vd_VO7UAAAAJ
Sam Pellock tyR_l8IAAAAJ
Jianyi Yang OXry1e0AAAAJ
Qian Cong bD4hIqcAAAAJ
Gyu Rie Lee HA-B4T4AAAAJ
Хадарович, Анна Юрьевна (Hadarovi… DoRlAQoAAAAJ
Hetu K FXs0ZQoAAAAJ
Ivan Anishchenko Vilya N/A Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, … Science 373 (6557), 871-876, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:O3NaXMp0MMsC Cited by: 3650
Improved protein structure prediction using predicted interresidue orientations J Yang, I Anishchenko, H Park, Z Peng, S Ovchinnikov, D Baker Proceedings of the National Academy of Sciences 117 (3), 1496-1503, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:e5wmG9Sq2KIC Cited by: 1312
Robust deep learning–based protein sequence design using ProteinMPNN J Dauparas, I Anishchenko, N Bennett, H Bai, RJ Ragotte, LF Milles, … Science 378 (6615), 49-56, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:g5m5HwL7SMYC Cited by: 646
De novo protein design by deep network hallucination I Anishchenko, SJ Pellock, TM Chidyausiku, TA Ramelot, S Ovchinnikov, … Nature 600 (7889), 547-552, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:TFP_iSt0sucC Cited by: 439
The trRosetta server for fast and accurate protein structure prediction Z Du, H Su, W Wang, L Ye, H Wei, Z Peng, I Anishchenko, D Baker, … Nature protocols 16 (12), 5634-5651, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:vV6vV6tmYwMC Cited by: 379
Computed structures of core eukaryotic protein complexes IR Humphreys, J Pei, M Baek, A Krishnakumar, I Anishchenko, … Science 374 (6573), eabm4805, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:lSLTfruPkqcC Cited by: 374
Scaffolding protein functional sites using deep learning J Wang, S Lisanza, D Juergens, D Tischer, JL Watson, KM Castro, … Science 377 (6604), 387-394, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:pqnbT2bcN3wC Cited by: 265
Protein interaction networks revealed by proteome coevolution Q Cong, I Anishchenko, S Ovchinnikov, D Baker Science 365 (6449), 185-189, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:HDshCWvjkbEC Cited by: 236
Improved protein structure refinement guided by deep learning based accuracy estimation N Hiranuma, H Park, M Baek, I Anishchenko, J Dauparas, D Baker Nature communications 12 (1), 1340, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:JV2RwH3_ST0C Cited by: 212
De novo design of luciferases using deep learning AHW Yeh, C Norn, Y Kipnis, D Tischer, SJ Pellock, D Evans, P Ma, … Nature 614 (7949), 774-780, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3s1wT3WcHBgC Cited by: 195
Origins of coevolution between residues distant in protein 3D structures I Anishchenko, S Ovchinnikov, H Kamisetty, D Baker Proceedings of the National Academy of Sciences 114 (34), 9122-9127, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:Wp0gIr-vW9MC Cited by: 182
Protein sequence design by conformational landscape optimization C Norn, BIM Wicky, D Juergens, S Liu, D Kim, D Tischer, B Koepnick, … Proceedings of the National Academy of Sciences 118 (11), e2017228118, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:YFjsv_pBGBYC Cited by: 169
Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: a CASP‐CAPRI experiment MF Lensink, S Velankar, A Kryshtafovych, SY Huang, … Proteins: Structure, Function, and Bioinformatics 84, 323-348, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3fE2CSJIrl8C Cited by: 168
Generalized biomolecular modeling and design with RoseTTAFold All-Atom R Krishna, J Wang, W Ahern, P Sturmfels, P Venkatesh, I Kalvet, GR Lee, … Science 384 (6693), eadl2528, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:zA6iFVUQeVQC Cited by: 110
Protein contact prediction using metagenome sequence data and residual neural networks Q Wu, Z Peng, I Anishchenko, Q Cong, D Baker, J Yang Bioinformatics 36 (1), 41-48, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:mB3voiENLucC Cited by: 96
Dockground: a comprehensive data resource for modeling of protein complexes PJ Kundrotas, I Anishchenko, T Dauzhenka, I Kotthoff, D Mnevets, … Protein Science 27 (1), 172-181, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:9ZlFYXVOiuMC Cited by: 86
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA M Baek, R McHugh, I Anishchenko, H Jiang, D Baker, F DiMaio Nature methods 21 (1), 117-121, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:fPk4N6BV_jEC Cited by: 60
ProteinGCN: Protein model quality assessment using graph convolutional networks S Sanyal, I Anishchenko, A Dagar, D Baker, P Talukdar BioRxiv, 2020.04. 06.028266, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:RHpTSmoSYBkC Cited by: 51
Efficient and accurate prediction of protein structure using RoseTTAFold2 M Baek, I Anishchenko, IR Humphreys, Q Cong, D Baker, F DiMaio BioRxiv, 2023.05. 24.542179, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:rO6llkc54NcC Cited by: 50
Computational model of the HIV-1 subtype A V3 loop: study on the conformational mobility for structure-based anti-AIDS drug design AM Andrianov, IV Anishchenko Journal of Biomolecular Structure and Dynamics 27 (2), 179-193, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:u5HHmVD_uO8C Cited by: 50
David Baker googlescholarauthor_id:UKqIqRsAAAAJ
Sergey Ovchinnikov googlescholarauthor_id:8KJ9gf4AAAAJ
Petras Kundrotas googlescholarauthor_id:JvYJSc4AAAAJ
Justas Dauparas googlescholarauthor_id:jlgADF8AAAAJ
Alexander Tuzikov googlescholarauthor_id:EL7ilZEAAAAJ
Minkyung Baek googlescholarauthor_id:HNPzCLoAAAAJ
Hahnbeom Park googlescholarauthor_id:Y8Tqu4MAAAAJ
Christoffer Norn googlescholarauthorid:vdVO7UAAAAJ
Sam Pellock googlescholarauthorid:tyRl8IAAAAJ
Jianyi Yang googlescholarauthor_id:OXry1e0AAAAJ
Qian Cong googlescholarauthor_id:bD4hIqcAAAAJ
Gyu Rie Lee googlescholarauthor_id:HA-B4T4AAAAJ
Хадарович, Анна Юрьевна (Hadarovi… googlescholarauthor_id:DoRlAQoAAAAJ
Hetu K googlescholarauthor_id:FXs0ZQoAAAAJ
Ivan Anishchenko Vilya N/A Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, … Science 373 (6557), 871-876, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:O3NaXMp0MMsC Cited by: 3650
Improved protein structure prediction using predicted interresidue orientations J Yang, I Anishchenko, H Park, Z Peng, S Ovchinnikov, D Baker Proceedings of the National Academy of Sciences 117 (3), 1496-1503, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:e5wmG9Sq2KIC Cited by: 1312
Robust deep learning–based protein sequence design using ProteinMPNN J Dauparas, I Anishchenko, N Bennett, H Bai, RJ Ragotte, LF Milles, … Science 378 (6615), 49-56, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:g5m5HwL7SMYC Cited by: 646
De novo protein design by deep network hallucination I Anishchenko, SJ Pellock, TM Chidyausiku, TA Ramelot, S Ovchinnikov, … Nature 600 (7889), 547-552, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:TFP_iSt0sucC Cited by: 439
The trRosetta server for fast and accurate protein structure prediction Z Du, H Su, W Wang, L Ye, H Wei, Z Peng, I Anishchenko, D Baker, … Nature protocols 16 (12), 5634-5651, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:vV6vV6tmYwMC Cited by: 379
Computed structures of core eukaryotic protein complexes IR Humphreys, J Pei, M Baek, A Krishnakumar, I Anishchenko, … Science 374 (6573), eabm4805, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:lSLTfruPkqcC Cited by: 374
Scaffolding protein functional sites using deep learning J Wang, S Lisanza, D Juergens, D Tischer, JL Watson, KM Castro, … Science 377 (6604), 387-394, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:pqnbT2bcN3wC Cited by: 265
Protein interaction networks revealed by proteome coevolution Q Cong, I Anishchenko, S Ovchinnikov, D Baker Science 365 (6449), 185-189, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:HDshCWvjkbEC Cited by: 236
Improved protein structure refinement guided by deep learning based accuracy estimation N Hiranuma, H Park, M Baek, I Anishchenko, J Dauparas, D Baker Nature communications 12 (1), 1340, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:JV2RwH3_ST0C Cited by: 212
De novo design of luciferases using deep learning AHW Yeh, C Norn, Y Kipnis, D Tischer, SJ Pellock, D Evans, P Ma, … Nature 614 (7949), 774-780, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3s1wT3WcHBgC Cited by: 195
Origins of coevolution between residues distant in protein 3D structures I Anishchenko, S Ovchinnikov, H Kamisetty, D Baker Proceedings of the National Academy of Sciences 114 (34), 9122-9127, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:Wp0gIr-vW9MC Cited by: 182
Protein sequence design by conformational landscape optimization C Norn, BIM Wicky, D Juergens, S Liu, D Kim, D Tischer, B Koepnick, … Proceedings of the National Academy of Sciences 118 (11), e2017228118, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:YFjsv_pBGBYC Cited by: 169
Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: a CASP‐CAPRI experiment MF Lensink, S Velankar, A Kryshtafovych, SY Huang, … Proteins: Structure, Function, and Bioinformatics 84, 323-348, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3fE2CSJIrl8C Cited by: 168
Generalized biomolecular modeling and design with RoseTTAFold All-Atom R Krishna, J Wang, W Ahern, P Sturmfels, P Venkatesh, I Kalvet, GR Lee, … Science 384 (6693), eadl2528, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:zA6iFVUQeVQC Cited by: 110
Protein contact prediction using metagenome sequence data and residual neural networks Q Wu, Z Peng, I Anishchenko, Q Cong, D Baker, J Yang Bioinformatics 36 (1), 41-48, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:mB3voiENLucC Cited by: 96
Dockground: a comprehensive data resource for modeling of protein complexes PJ Kundrotas, I Anishchenko, T Dauzhenka, I Kotthoff, D Mnevets, … Protein Science 27 (1), 172-181, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:9ZlFYXVOiuMC Cited by: 86
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA M Baek, R McHugh, I Anishchenko, H Jiang, D Baker, F DiMaio Nature methods 21 (1), 117-121, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:fPk4N6BV_jEC Cited by: 60
ProteinGCN: Protein model quality assessment using graph convolutional networks S Sanyal, I Anishchenko, A Dagar, D Baker, P Talukdar BioRxiv, 2020.04. 06.028266, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:RHpTSmoSYBkC Cited by: 51
Efficient and accurate prediction of protein structure using RoseTTAFold2 M Baek, I Anishchenko, IR Humphreys, Q Cong, D Baker, F DiMaio BioRxiv, 2023.05. 24.542179, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:rO6llkc54NcC Cited by: 50
Computational model of the HIV-1 subtype A V3 loop: study on the conformational mobility for structure-based anti-AIDS drug design AM Andrianov, IV Anishchenko Journal of Biomolecular Structure and Dynamics 27 (2), 179-193, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:u5HHmVD_uO8C Cited by: 50
David Baker googlescholarauthorid davidbaker.md:UKqIqRsAAAAJ
Sergey Ovchinnikov googlescholarauthorid sergeyovchinnikov.md:8KJ9gf4AAAAJ
Petras Kundrotas googlescholarauthorid petraskundrotas.md:JvYJSc4AAAAJ
Justas Dauparas googlescholarauthorid justasdauparas.md:jlgADF8AAAAJ
Alexander Tuzikov googlescholarauthorid alexandertuzikov.md:EL7ilZEAAAAJ
Minkyung Baek googlescholarauthorid minkyungbaek.md:HNPzCLoAAAAJ
Hahnbeom Park googlescholarauthorid hahnbeompark.md:Y8Tqu4MAAAAJ
Christoffer Norn googlescholarauthorid christoffernorn.md:vd_VO7UAAAAJ
Sam Pellock googlescholarauthorid sampellock.md:tyR_l8IAAAAJ
Jianyi Yang googlescholarauthorid jianyiyang.md:OXry1e0AAAAJ
Qian Cong googlescholarauthorid qiancong.md:bD4hIqcAAAAJ
Gyu Rie Lee googlescholarauthorid gyurie_lee.md:HA-B4T4AAAAJ
Хадарович, Анна Юрьевна (Hadarovi… googlescholarauthorid хадарович,аннаюрьевна(hadarovi….md:DoRlAQoAAAAJ
Hetu K googlescholarauthorid hetuk.md:FXs0ZQoAAAAJ
Ivan Anishchenko
Vilya
N/A
Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, … Science 373 (6557), 871-876, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:O3NaXMp0MMsC
Improved protein structure prediction using predicted interresidue orientations J Yang, I Anishchenko, H Park, Z Peng, S Ovchinnikov, D Baker Proceedings of the National Academy of Sciences 117 (3), 1496-1503, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:e5wmG9Sq2KIC
Robust deep learning–based protein sequence design using ProteinMPNN J Dauparas, I Anishchenko, N Bennett, H Bai, RJ Ragotte, LF Milles, … Science 378 (6615), 49-56, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:g5m5HwL7SMYC
De novo protein design by deep network hallucination I Anishchenko, SJ Pellock, TM Chidyausiku, TA Ramelot, S Ovchinnikov, … Nature 600 (7889), 547-552, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:TFP_iSt0sucC
The trRosetta server for fast and accurate protein structure prediction Z Du, H Su, W Wang, L Ye, H Wei, Z Peng, I Anishchenko, D Baker, … Nature protocols 16 (12), 5634-5651, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:vV6vV6tmYwMC
Computed structures of core eukaryotic protein complexes IR Humphreys, J Pei, M Baek, A Krishnakumar, I Anishchenko, … Science 374 (6573), eabm4805, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:lSLTfruPkqcC
Scaffolding protein functional sites using deep learning J Wang, S Lisanza, D Juergens, D Tischer, JL Watson, KM Castro, … Science 377 (6604), 387-394, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:pqnbT2bcN3wC
Protein interaction networks revealed by proteome coevolution Q Cong, I Anishchenko, S Ovchinnikov, D Baker Science 365 (6449), 185-189, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:HDshCWvjkbEC
Improved protein structure refinement guided by deep learning based accuracy estimation N Hiranuma, H Park, M Baek, I Anishchenko, J Dauparas, D Baker Nature communications 12 (1), 1340, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:JV2RwH3_ST0C
De novo design of luciferases using deep learning AHW Yeh, C Norn, Y Kipnis, D Tischer, SJ Pellock, D Evans, P Ma, … Nature 614 (7949), 774-780, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3s1wT3WcHBgC
Origins of coevolution between residues distant in protein 3D structures I Anishchenko, S Ovchinnikov, H Kamisetty, D Baker Proceedings of the National Academy of Sciences 114 (34), 9122-9127, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:Wp0gIr-vW9MC
Protein sequence design by conformational landscape optimization C Norn, BIM Wicky, D Juergens, S Liu, D Kim, D Tischer, B Koepnick, … Proceedings of the National Academy of Sciences 118 (11), e2017228118, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:YFjsv_pBGBYC
Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: a CASP‐CAPRI experiment MF Lensink, S Velankar, A Kryshtafovych, SY Huang, … Proteins: Structure, Function, and Bioinformatics 84, 323-348, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:3fE2CSJIrl8C
Generalized biomolecular modeling and design with RoseTTAFold All-Atom R Krishna, J Wang, W Ahern, P Sturmfels, P Venkatesh, I Kalvet, GR Lee, … Science 384 (6693), eadl2528, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:zA6iFVUQeVQC
Protein contact prediction using metagenome sequence data and residual neural networks Q Wu, Z Peng, I Anishchenko, Q Cong, D Baker, J Yang Bioinformatics 36 (1), 41-48, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:mB3voiENLucC
Dockground: a comprehensive data resource for modeling of protein complexes PJ Kundrotas, I Anishchenko, T Dauzhenka, I Kotthoff, D Mnevets, … Protein Science 27 (1), 172-181, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:9ZlFYXVOiuMC
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA M Baek, R McHugh, I Anishchenko, H Jiang, D Baker, F DiMaio Nature methods 21 (1), 117-121, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:fPk4N6BV_jEC
ProteinGCN: Protein model quality assessment using graph convolutional networks S Sanyal, I Anishchenko, A Dagar, D Baker, P Talukdar BioRxiv, 2020.04. 06.028266, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:RHpTSmoSYBkC
Efficient and accurate prediction of protein structure using RoseTTAFold2 M Baek, I Anishchenko, IR Humphreys, Q Cong, D Baker, F DiMaio BioRxiv, 2023.05. 24.542179, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:rO6llkc54NcC
Computational model of the HIV-1 subtype A V3 loop: study on the conformational mobility for structure-based anti-AIDS drug design AM Andrianov, IV Anishchenko Journal of Biomolecular Structure and Dynamics 27 (2), 179-193, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=Hp8zwAgAAAAJ&citationforview=Hp8zwAgAAAAJ:u5HHmVD_uO8C
David Baker googlescholarauthorid davidbaker.md:UKqIqRsAAAAJ
Sergey Ovchinnikov googlescholarauthorid sergeyovchinnikov.md:8KJ9gf4AAAAJ
Petras Kundrotas googlescholarauthorid petraskundrotas.md:JvYJSc4AAAAJ
Justas Dauparas googlescholarauthorid justasdauparas.md:jlgADF8AAAAJ
Alexander Tuzikov googlescholarauthorid alexandertuzikov.md:EL7ilZEAAAAJ
Minkyung Baek googlescholarauthorid minkyungbaek.md:HNPzCLoAAAAJ
Hahnbeom Park googlescholarauthorid hahnbeompark.md:Y8Tqu4MAAAAJ
Christoffer Norn googlescholarauthorid christoffernorn.md:vd_VO7UAAAAJ
Sam Pellock googlescholarauthorid sampellock.md:tyR_l8IAAAAJ
Jianyi Yang googlescholarauthorid jianyiyang.md:OXry1e0AAAAJ
Qian Cong googlescholarauthorid qiancong.md:bD4hIqcAAAAJ
Gyu Rie Lee googlescholarauthorid gyurie_lee.md:HA-B4T4AAAAJ
Хадарович, Анна Юрьевна (Hadarovi… googlescholarauthorid хадарович,аннаюрьевна(hadarovi….md:DoRlAQoAAAAJ
Hetu K googlescholarauthorid hetuk.md:FXs0ZQoAAAAJ
Youtube Title: Journal Club: De novo protein design by deep network hallucination by Anishchenko Ivan et al.
Youtube Link: link
Youtube Channel Name: Surgical Oncology
Youtube Channel Link: https://www.youtube.com/@surgicaloncology616
Journal Club: De novo protein design by deep network hallucination by Anishchenko Ivan et al.
Youtube Title: Evolutionary Hologenomics Podcast ep5: Journal club: food additives, gut microbiome and trout health
Youtube Link: link
Youtube Channel Name: Center for Evolutionary Hologenomics
Youtube Channel Link: https://www.youtube.com/@hologenomics
Evolutionary Hologenomics Podcast ep5: Journal club: food additives, gut microbiome and trout health
Youtube Title: The coming of age of de novo protein design
Youtube Link: link
Youtube Channel Name: Oxford University Scientific Society
Youtube Channel Link: https://www.youtube.com/@oxforduniversityscientific3517
The coming of age of de novo protein design
Youtube Title: On-Demand Webinar: Intro to de novo Protein Sequencing
Youtube Link: link
Youtube Channel Name: Rapid Novor - Protein Sequencing Experts
Youtube Channel Link: https://www.youtube.com/@rapidnovor-proteinsequenci3828
On-Demand Webinar: Intro to de novo Protein Sequencing
Youtube Title: Molecular ML Reading Group (10/25/23): RoseTTAFold All-Atom (Krishna et al., 2023)
Youtube Link: link
Youtube Channel Name: MaomLab at the University of Michigan
Youtube Channel Link: https://www.youtube.com/@maomlab
Molecular ML Reading Group (10/25/23): RoseTTAFold All-Atom (Krishna et al., 2023)
Youtube Title: Linna Ann | De Novo Design of Small Molecule Binding Proteins
Youtube Link: link
Youtube Channel Name: Foresight Institute
Youtube Channel Link: https://www.youtube.com/@ForesightInstitute
Linna Ann | De Novo Design of Small Molecule Binding Proteins
Youtube Title: Ivan Alekseichuk, Dose-Response in Non-Invasive Brain Stimulation (Animal/Cellular Level)
Youtube Link: link
Youtube Channel Name: International Network of Neuroimaging Neuromod
Youtube Channel Link: https://www.youtube.com/@INNN_Network
Ivan Alekseichuk, Dose-Response in Non-Invasive Brain Stimulation (Animal/Cellular Level)
Youtube Title: IQB Crash Course Dec 2021 - "Enabling Protein Structure Prediction w/AI" Intro and Dr Minkyung Baek
Youtube Link: link
Youtube Channel Name: Rutgers - Institute of Quantitative Biomedicine
Youtube Channel Link: https://www.youtube.com/@rutgers-instituteofquantit7931
IQB Crash Course Dec 2021 - "Enabling Protein Structure Prediction w/AI" Intro and Dr Minkyung Baek
Youtube Title: Protein Identification - Sam Mackintosh - IDeA National Resource for Quantitative Proteomics
Youtube Link: link
Youtube Channel Name: IDeA National Resource for Quantitative Proteomics
Youtube Channel Link: https://www.youtube.com/@ideanationalresourceforqua4640
Protein Identification - Sam Mackintosh - IDeA National Resource for Quantitative Proteomics
Youtube Title: Neuromorphic Computing: Ivan Schuller
Youtube Link: link
Youtube Channel Name: American Physical Society
Youtube Channel Link: https://www.youtube.com/@apsphysics
Neuromorphic Computing: Ivan Schuller
Youtube Title: SYNB0.DL6_Methods in Computational Protein Design
Youtube Link: link
Youtube Channel Name: CSBERG
Youtube Channel Link: https://www.youtube.com/@csberg5856
SYNB0.DL6_Methods in Computational Protein Design
Youtube Title: SYNB0.DL5_Mathematics of Protein Design (ML)
Youtube Link: link
Youtube Channel Name: CSBERG
Youtube Channel Link: https://www.youtube.com/@csberg5856
SYNB0.DL5_Mathematics of Protein Design (ML)
Youtube Title: How the Posthuman Helps Us Respond to a Changing World
Youtube Link: link
Youtube Channel Name: Ivan Allen College of Liberal Arts
Youtube Channel Link: https://www.youtube.com/@IvanAllenCollege
How the Posthuman Helps Us Respond to a Changing World
Youtube Title: Success story of Rational Protein designing: Focusing on De Novo Process
Youtube Link: link
Youtube Channel Name: IIT Roorkee July 2018
Youtube Channel Link: https://www.youtube.com/@IITRoorkeeJuly
Success story of Rational Protein designing: Focusing on De Novo Process
Youtube Title: Anishchenko Ivan Fedorovich - veteran of war
Youtube Link: link
Youtube Channel Name: RybnoeNet – Рыбное Онлайн
Youtube Channel Link: https://www.youtube.com/@rybnoe
Anishchenko Ivan Fedorovich - veteran of war
Youtube Title: Training to professional fulfilment: the history of women’s education in Ukraine
Youtube Link: link
Youtube Channel Name: Not So Easy Science
Youtube Channel Link: https://www.youtube.com/@NotSoEasyScience
Training to professional fulfilment: the history of women’s education in Ukraine
Youtube Title: Sergey Ovchinnikov: Inverting protein structure prediction models to solve problems in biology
Youtube Link: link
Youtube Channel Name: Institut Pasteur de Montevideo
Youtube Channel Link: https://www.youtube.com/@institutpasteurdemontevideo
Sergey Ovchinnikov: Inverting protein structure prediction models to solve problems in biology
Youtube Title: Two New TOOLS in the Game – Lab Report 37
Youtube Link: link
Youtube Channel Name: Foldit
Youtube Channel Link: https://www.youtube.com/@UWfoldit
Two New TOOLS in the Game – Lab Report 37