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 | 3888 | 2021 |
The Rosetta all-atom energy function for macromolecular modeling and design RF Alford, A Leaver-Fay, JR Jeliazkov, MJ O’Meara, FP DiMaio, H Park, ... Journal of chemical theory and computation 13 (6), 3031-3048, 2017 | 1361 | 2017 |
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 | 1350 | 2020 |
GalaxyRefine: Protein structure refinement driven by side-chain repacking L Heo, H Park, C Seok Nucleic acids research 41 (W1), W384-W388, 2013 | 989 | 2013 |
GalaxyWEB server for protein structure prediction and refinement J Ko, H Park, L Heo, C Seok Nucleic acids research 40 (W1), W294-W297, 2012 | 777 | 2012 |
Macromolecular modeling and design in Rosetta: recent methods and frameworks JK Leman, BD Weitzner, SM Lewis, J Adolf-Bryfogle, N Alam, RF Alford, ... Nature methods 17 (7), 665-680, 2020 | 636 | 2020 |
Protein structure determination using metagenome sequence data S Ovchinnikov, H Park, N Varghese, PS Huang, GA Pavlopoulos, DE Kim, ... Science 355 (6322), 294-298, 2017 | 536 | 2017 |
Simultaneous optimization of biomolecular energy functions on features from small molecules and macromolecules H Park, P Bradley, P Greisen Jr, Y Liu, VK Mulligan, DE Kim, D Baker, ... Journal of chemical theory and computation 12 (12), 6201-6212, 2016 | 451 | 2016 |
De novo design of a fluorescence-activating β-barrel J Dou, AA Vorobieva, W Sheffler, LA Doyle, H Park, MJ Bick, B Mao, ... Nature 561 (7724), 485-491, 2018 | 345 | 2018 |
Large-scale determination of previously unsolved protein structures using evolutionary information S Ovchinnikov, L Kinch, H Park, Y Liao, J Pei, DE Kim, H Kamisetty, ... elife 4, e09248, 2015 | 278 | 2015 |
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 | 223 | 2021 |
Conditioning by adaptive sampling for robust design D Brookes, H Park, J Listgarten International conference on machine learning, 773-782, 2019 | 218 | 2019 |
Community-wide assessment of protein-interface modeling suggests improvements to design methodology SJ Fleishman, TA Whitehead, EM Strauch, JE Corn, S Qin, HX Zhou, ... Journal of molecular biology 414 (2), 289-302, 2011 | 157 | 2011 |
Protein loop modeling by using fragment assembly and analytical loop closure J Lee, D Lee, H Park, EA Coutsias, C Seok Proteins: Structure, Function, and Bioinformatics 78 (16), 3428-3436, 2010 | 127 | 2010 |
Protein structure prediction using Rosetta in CASP12 S Ovchinnikov, H Park, DE Kim, F DiMaio, D Baker Proteins: Structure, Function, and Bioinformatics 86, 113-121, 2018 | 125 | 2018 |
GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions J Ko, H Park, C Seok BMC bioinformatics 13, 1-8, 2012 | 117 | 2012 |
Community‐wide evaluation of methods for predicting the effect of mutations on protein–protein interactions R Moretti, SJ Fleishman, R Agius, M Torchala, PA Bates, PL Kastritis, ... Proteins: Structure, Function, and Bioinformatics 81 (11), 1980-1987, 2013 | 115 | 2013 |
The FALC-Loop web server for protein loop modeling J Ko, D Lee, H Park, EA Coutsias, J Lee, C Seok Nucleic acids research 39 (suppl_2), W210-W214, 2011 | 101 | 2011 |
Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments H Park, GR Lee, L Heo, C Seok PloS one 9 (11), e113811, 2014 | 98 | 2014 |
Prediction of protein mutational free energy: benchmark and sampling improvements increase classification accuracy B Frenz, SM Lewis, I King, F DiMaio, H Park, Y Song Frontiers in bioengineering and biotechnology 8, 558247, 2020 | 81 | 2020 |