Minkyung Baek is a researcher in the field of computational biology and chemistry, with a focus on protein structure prediction and modelling. Baek has developed machine learning methods and artificial intelligence-based computational methods to predict and understand protein structures and interactions, contributing to the development of novel therapeutics.
Baek received her Bachelor of Science (B.S.) and Doctor of Philosophy (PhD) in Chemistry from Seoul National University in South Korea. Her PhD is specifically listed as being in Physical Chemistry in one source.
Baek is currently a Postdoctoral researcher at the University of Washington's Institute for Protein Design, working in the BakerLab. Previously, she was a Postdoctoral researcher at her alma mater, Seoul National University.
Baek has multiple publications in esteemed scientific journals, including:
Some notable publications include:
Minkyung Baek
School of Biological Sciences, Seoul National University
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=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:kcbZDykSQC
De novo design of protein structure and function with RFdiffusion JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, … Nature 620 (7976), 1089-1100, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:e5wmG9Sq2KIC
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=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:4DMP91E08xMC
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=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:ZeXyd9-uunAC
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=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:Zph67rFs4hoC
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=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:u-x6o8ySG0sC
Hallucinating symmetric protein assemblies BIM Wicky, LF Milles, A Courbet, RJ Ragotte, J Dauparas, E Kinfu, S Tipps, … Science 378 (6615), 56-61, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:hC7cP41nSMkC
GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure M Baek, T Park, L Heo, C Park, C Seok Nucleic Acids Research 45 (W1), W320-W324, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:UeHWp8X0CEIC
Blind prediction of homo‐and hetero‐protein complexes: The CASP13‐CAPRI experiment MF Lensink, G Brysbaert, N Nadzirin, S Velankar, RAG Chaleil, T Gerguri, … Proteins: Structure, Function, and Bioinformatics 87 (12), 1200-1221, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:hqOjcs7Dif8C
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=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:r0BpntZqJG4C
Improving de novo protein binder design with deep learning NR Bennett, B Coventry, I Goreshnik, B Huang, A Allen, D Vafeados, … Nature Communications 14 (1), 2625, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:QIV2ME_5wuYC
Deep learning and protein structure modeling M Baek, D Baker Nature Methods 19 (1), 13-14, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:Wp0gIr-vW9MC
The challenge of modeling protein assemblies: the CASP12‐CAPRI experiment MF Lensink, S Velankar, M Baek, L Heo, C Seok, SJ Wodak Proteins: Structure, Function, and Bioinformatics 86, 257-273, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:Tyk-4Ss8FVUC
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA M Baek, R McHugh, I Anishchenko, H Jiang, D Baker, F DiMaio Nature Methods, 1-5, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:RHpTSmoSYBkC
Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning J Won, M Baek, B Monastyrskyy, A Kryshtafovych, C Seok Proteins: Structure, Function, and Bioinformatics 87 (12), 1351-1360, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:Se3iqnhoufwC
Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein–Ligand Docking H Park, G Zhou, M Baek, D Baker, F DiMaio Journal of Chemical Theory and Computation 17 (3), 2000-2010, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:KlAtU1dfN6UC
Top-down design of protein architectures with reinforcement learning ID Lutz, S Wang, C Norn, A Courbet, AJ Borst, YT Zhao, A Dosey, L Cao, … Science 380 (6642), 266-273, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:HDshCWvjkbEC
GalaxyDock BP2 score: a hybrid scoring function for accurate protein–ligand docking M Baek, WH Shin, HW Chung, C Seok Journal of Computer-Aided Molecular Design 31 (7), 653-666, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:IjCSPb-OGe4C
Efficient and accurate prediction of protein structure using RoseTTAFold2 M Baek, I Anishchenko, I Humphreys, Q Cong, D Baker, F DiMaio bioRxiv, 2023.05. 24.542179, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:R3hNpaxXUhUC
Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14 I Anishchenko, M Baek, H Park, N Hiranuma, DE Kim, J Dauparas, … Proteins: Structure, Function, and Bioinformatics 89 (12), 1722-1733, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=HNPzCLoAAAAJ&citationforview=HNPzCLoAAAAJ:M3ejUd6NZC8C
David Baker googlescholarauthorid davidbaker.md:UKqIqRsAAAAJ
Chaok Seok googlescholarauthorid chaokseok.md:r6K6zAIAAAAJ
Frank DiMaio googlescholarauthorid frankdimaio.md:jQpFYpIAAAAJ
Ivan Anishchenko googlescholarauthorid ivananishchenko.md:Hp8zwAgAAAAJ
Justas Dauparas googlescholarauthorid justasdauparas.md:jlgADF8AAAAJ
Hahnbeom Park googlescholarauthorid hahnbeompark.md:Y8Tqu4MAAAAJ
Gyu Rie Lee googlescholarauthorid gyurie_lee.md:HA-B4T4AAAAJ
Lim Heo googlescholarauthorid limheo.md:73JdVH0AAAAJ
Naozumi Hiranuma googlescholarauthorid naozumihiranuma.md:OaRpTJcAAAAJ
Jonghun Won googlescholarauthorid jonghunwon.md:7m-R650AAAAJ
Woong-Hee Shin googlescholarauthorid woong-heeshin.md:Lm0RuHYAAAAJ
Bernard R. Brooks googlescholarauthorid bernardr._brooks.md:ZncU9vkAAAAJ