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Proceedings of the National Academy of Sciences of the United States of America
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January 4, 2020
Improved protein structure prediction using predicted interresidue orientations
Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, et al.
Nature Methods
|
November 23, 2023
Accurate prediction of protein-nucleic acid complexes using RoseTTAFoldNA
Minkyung Baek, Ryan McHugh, Ivan Anishchenko, et al.
Nature Communications
|
February 27, 2021
Improved protein structure refinement guided by deep learning based accuracy estimation
Naozumi Hiranuma, Hahnbeom Park, Minkyung Baek, et al.
Proteins
|
December 7, 2018
Gene ontology improves template selection in comparative protein docking
Anna Hadarovich, Ivan Anishchenko, Alexander V Tuzikov, et al.
Proteins
|
July 21, 2019
High-accuracy refinement using Rosetta in CASP13
Hahnbeom Park, Gyu Rie Lee, David E Kim, et al.
Briefings in Bioinformatics
|
May 31, 2022
Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs
Neeladri Sen, Ivan Anishchenko, Nicola Bordin, et al.
Nature Methods
|
March 29, 2025
Atomic context-conditioned protein sequence design using LigandMPNN
Justas Dauparas, Gyu Rie Lee, Robert Pecoraro, et al.
Proteins
|
September 15, 2017
Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function
Petras J Kundrotas, Ivan Anishchenko, Varsha D Badal, et al.
Iucrj
|
September 17, 2020
Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM
Daniel P Farrell, Ivan Anishchenko, Shabih Shakeel, et al.
Protein Science : a Publication of the Protein Society
|
September 12, 2017
Dockground: A comprehensive data resource for modeling of protein complexes
Petras J Kundrotas, Ivan Anishchenko, Taras Dauzhenka, et al.
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of 5
Search research articles
Search
Showing results (11-20 of 41) with videos related to
Sort By:
Page
of 5
Proceedings of the National Academy of Sciences of the United States of America
|
January 4, 2020
Improved protein structure prediction using predicted interresidue orientations
Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, et al.
Nature Methods
|
November 23, 2023
Accurate prediction of protein-nucleic acid complexes using RoseTTAFoldNA
Minkyung Baek, Ryan McHugh, Ivan Anishchenko, et al.
Nature Communications
|
February 27, 2021
Improved protein structure refinement guided by deep learning based accuracy estimation
Naozumi Hiranuma, Hahnbeom Park, Minkyung Baek, et al.
Proteins
|
December 7, 2018
Gene ontology improves template selection in comparative protein docking
Anna Hadarovich, Ivan Anishchenko, Alexander V Tuzikov, et al.
Proteins
|
July 21, 2019
High-accuracy refinement using Rosetta in CASP13
Hahnbeom Park, Gyu Rie Lee, David E Kim, et al.
Briefings in Bioinformatics
|
May 31, 2022
Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs
Neeladri Sen, Ivan Anishchenko, Nicola Bordin, et al.
Nature Methods
|
March 29, 2025
Atomic context-conditioned protein sequence design using LigandMPNN
Justas Dauparas, Gyu Rie Lee, Robert Pecoraro, et al.
Proteins
|
September 15, 2017
Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function
Petras J Kundrotas, Ivan Anishchenko, Varsha D Badal, et al.
Iucrj
|
September 17, 2020
Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM
Daniel P Farrell, Ivan Anishchenko, Shabih Shakeel, et al.
Protein Science : a Publication of the Protein Society
|
September 12, 2017
Dockground: A comprehensive data resource for modeling of protein complexes
Petras J Kundrotas, Ivan Anishchenko, Taras Dauzhenka, et al.
Page
of 5