| A TALE nuclease architecture for efficient genome editing JC Miller, S Tan, G Qiao, KA Barlow, J Wang, DF Xia, X Meng, ... Nature biotechnology 29 (2), 143-148, 2011 | 2938 | 2011 |
| 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 | 564 | 2020 |
| Flex ddG: Rosetta ensemble-based estimation of changes in protein–protein binding affinity upon mutation KA Barlow, S Ó Conchúir, S Thompson, P Suresh, JE Lucas, M Heinonen, ... The Journal of Physical Chemistry B 122 (21), 5389-5399, 2018 | 187 | 2018 |
| Improved specificity of TALE-based genome editing using an expanded RVD repertoire JC Miller, L Zhang, DF Xia, JJ Campo, IV Ankoudinova, DY Guschin, ... Nature methods 12 (5), 465-471, 2015 | 115 | 2015 |
| Deamidation and isomerization liability analysis of 131 clinical-stage antibodies X Lu, RP Nobrega, H Lynaugh, T Jain, K Barlow, T Boland, ... MAbs 11 (1), 45-57, 2019 | 103 | 2019 |
| Computational design of a modular protein sense-response system AA Glasgow, YM Huang, DJ Mandell, M Thompson, R Ritterson, ... Science 366 (6468), 1024-1028, 2019 | 102 | 2019 |
| A web resource for standardized benchmark datasets, metrics, and Rosetta protocols for macromolecular modeling and design S ó Conchúir, KA Barlow, RA Pache, N Ollikainen, K Kundert, MJ O'Meara, ... PLOS one 10 (9), e0130433, 2015 | 84 | 2015 |
| Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting D Mavor, K Barlow, S Thompson, BA Barad, AR Bonny, CL Cario, ... Elife 5, e15802, 2016 | 78 | 2016 |
| Ó Conchúir S, Thompson S, Suresh P, Lucas JE, Heinonen M, Kortemme T. Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation KA Barlow J Phys Chem B 122 (21), 5389-5399, 2018 | 53 | 2018 |
| Extending chemical perturbations of the ubiquitin fitness landscape in a classroom setting reveals new constraints on sequence tolerance D Mavor, KA Barlow, D Asarnow, Y Birman, D Britain, W Chen, EM Green, ... Biology Open 7 (7), bio036103, 2018 | 20 | 2018 |
| Single-cell analysis of habituation in Stentor coeruleus D Rajan, T Makushok, A Kalish, L Acuna, A Bonville, KC Almanza, ... Current Biology 33 (2), 241-251. e4, 2023 | 18 | 2023 |
| Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks J Koehler Leman, S Lyskov, SM Lewis, J Adolf-Bryfogle, RF Alford, ... Nature communications 12 (1), 6947, 2021 | 15 | 2021 |
| Design of light-controlled protein conformations and functions RS Ritterson, D Hoersch, KA Barlow, T Kortemme Computational Design of Ligand Binding Proteins, 197-211, 2016 | 4 | 2016 |
| Conchú ir S Lyskov, FC Chou SO., Der, BS, Drew, K., Kuroda, D., Xu, J., Weitzner, BD, Renfrew, PD …, 2013 | 4 | 2013 |
| Rapid and efficient generation of panels of T cell engaging (TCE) multispecific antibodies via complementary technologies C Ahonen, N Sharkey, M Durkin, A Avery, M Battles, B Sharkey, K Barlow, ... Cancer Research 84 (6_Supplement), 5792-5792, 2024 | | 2024 |
| Increasing the complexity of computational protein modeling methodologies for functional applications in biology K Barlow University of California, San Francisco, 2017 | | 2017 |
| A Web resource for standardized benchmark datasets, metrics, and rosetta protocols for macromolecular modeling and design T Kortemme, S Conchúir, KA Barlow, RA Pache, N Ollikainen, K Kundert, ... | | 2015 |
| Computational Protein Stability Prediction K Barlow | | 2014 |
Tanja KortemmeUCSFVerified email at cgl.ucsf.edu
