The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Kemp elimination catalysts by computational enzyme design
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874
Cited by
The role of dynamic conformational ensembles in biomolecular recognition
Boehr, D. D., Nussinov, R., & Wright, P. E. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.232 · Cited by 2,008
SwissDock, a protein-small molecule docking web service based on EADock DSS
Grosdidier, A., Zoete, V., & Michielin, O. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr366 · Cited by 1,950
Leaver-Fay (2011) · doi:10.1016/b978-0-12-381270-4.00019-6 · Cited by 1,904
Enzyme promiscuity: A mechanistic and evolutionary perspective
Tawfik, O. K. A. D. S. (2010) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-030409-143718 · Cited by 1,355
Towards the online computer-aided design of catalytic pockets
Falivene, L., Cao, Z., Petta, A., Serra, L., Poater, A., Oliva, R., Scarano, V., & Cavallo, L. (2019) · Nature Chemistry · doi:10.1038/s41557-019-0319-5 · Cited by 1,290
Advances in protein structure prediction and design
Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806
Directed evolution drives the next generation of biocatalysts
Turner, N. J. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.203 · Cited by 785
Principles for designing ideal protein structures
Koga, N., Tatsumi-Koga, R., Liu, G., Xiao, R., Acton, T. B., Montelione, G. T., & Baker, D. (2012) · Nature · doi:10.1038/nature11600 · Cited by 564
Self-citationScaffolding protein functional sites using deep learning
Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J. L., Castro, K. M., Ragotte, R., Saragovi, A., Milles, L. F., Baek, M., et al. (2022) · Science · doi:10.1126/science.abn2100 · Cited by 487
Self-citationSerum albumin in health and disease: esterase, antioxidant, transporting and signaling properties
Belinskaia, D. A., Voronina, P. A., Shmurak, V. I., Jenkins, R. O., & Goncharov, N. V. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910318 · Cited by 416
Engineering an allosteric transcription factor to respond to new ligands
Taylor, N. D., Garruss, A. S., Moretti, R., Chan, S., Arbing, M. A., Cascio, D., Rogers, J. K., Isaacs, F. J., Kosuri, S., Baker, D., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3696 · Cited by 328
Self-citationComputational design of serine hydrolases
Lauko, A., Pellock, S. J., Sumida, K. H., Anishchenko, I., Juergens, D., Ahern, W., Jeung, J., Shida, A. F., Hunt, A., Kalvet, I., et al. (2025) · Science · doi:10.1126/science.adu2454 · Cited by 235
Self-citationTaking Ockham's razor to enzyme dynamics and catalysis
Glowacki, D. R., Harvey, J. N., & Mulholland, A. J. (2012) · Nature Chemistry · doi:10.1038/nchem.1244 · Cited by 232
RosettaAntibodyDesign (RAbD): a general framework for computational antibody design
Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., & Dunbrack, R. L. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006112 · Cited by 227
Protein sequence design with a learned potential
Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2022) · Nature Communications · doi:10.1038/s41467-022-28313-9 · Cited by 171
Principles for computational design of binding antibodies
Baran, D., Pszolla, M. G., Lapidoth, G. D., Norn, C., Dym, O., Unger, T., Albeck, S., Tyka, M. D., & Fleishman, S. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707171114 · Cited by 136
Self-citationIg-VAE: Generative Modeling of Protein Structure by Direct 3D Coordinate Generation
Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2020) · doi:10.1101/2020.08.07.242347 · Cited by 141
Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation
Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010271 · Cited by 141
Catalytic Contributions from Remote Regions of Enzyme Structure
Lee, J., & Goodey, N. M. (2011) · Chemical Reviews · doi:10.1021/cr100042n · Cited by 116
De novo protein design by inversion of the AlphaFold structure prediction network
Goverde, C. A., Wolf, B., Khakzad, H., Rosset, S., & Correia, B. E. (2023) · Protein Science · doi:10.1002/pro.4653 · Cited by 103
Choreographing an enzyme's dance
Villali, J., & Kern, D. (2010) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2010.08.007 · Cited by 98
An Evolutionary Algorithm for de Novo Optimization of Functional Transition Metal Compounds
Chu, Y., Heyndrickx, W., Occhipinti, G., Jensen, V. R., & Alsberg, B. K. (2012) · Journal of the American Chemical Society · doi:10.1021/ja300865u · Cited by 90
Computational enzyme design by catalytic motif scaffolding
Braun, M., Tripp, A., Chakatok, M., Kaltenbrunner, S., Fischer, C., Stoll, D., Bijelic, A., Elaily, W., Totaro, M. G., Moser, M., et al. (2026) · Nature · doi:10.1038/s41586-025-09747-9 · Cited by 73
Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering
Xie, W. J., & Warshel, A. (2023) · National Science Review · doi:10.1093/nsr/nwad331 · Cited by 69
Challenges in the computational design of proteins
Suárez, M., & Jaramillo, A. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0508.focus · Cited by 59
Protein engineering by highly parallel screening of computationally designed variants
Sun, M. G. F., Seo, M.-H., Nim, S., Corbi-Verge, C., & Kim, P. M. (2016) · Science Advances · doi:10.1126/sciadv.1600692 · Cited by 41
Efficient generation of protein pockets with PocketGen
Zhang, Z., Shen, W. X., Liu, Q., & Zitnik, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00920-9 · Cited by 32
Crean, R. M., Slusky, J. S. G., Kasson, P. M., & Kamerlin, S. C. L. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0140882 · Cited by 30
Mutexa: A Computational Ecosystem for Intelligent Protein Engineering
Yang, Z. J., Shao, Q., Jiang, Y., Jurich, C., Ran, X., Juarez, R. J., Yan, B., Stull, S. L., Gollu, A., & Ding, N. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00602 · Cited by 28
Recent advances in automated protein design and its future challenges
Setiawan, D., Brender, J., & Zhang, Y. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1465922 · Cited by 26
Assessing and enhancing foldability in designed proteins
Listov, D., Lipsh‐Sokolik, R., Rosset, S., Yang, C., Correia, B. E., & Fleishman, S. J. (2022) · Protein Science · doi:10.1002/pro.4400 · Cited by 24
EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation
Shao, Q., Jiang, Y., & Yang, Z. J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00618 · Cited by 23
Computational design of serine hydrolases
Lauko, A., Pellock, S. J., Anischanka, I., Sumida, K. H., Juergens, D., Ahern, W., Shida, A., Hunt, A., Kalvet, I., Norn, C., et al. (2024) · doi:10.1101/2024.08.29.610411 · Cited by 16
Self-citationDesign of stable circular permutants of the GroEL chaperone apical domain
Melnik, T. N., Majorina, M. A., Vorobeva, D. E., Nagibina, G. S., Veselova, V. R., Glukhova, K. A., Pak, M. A., Ivankov, D. N., Uversky, V. N., & Melnik, B. S. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-023-01426-4 · Cited by 1
Metadata
- Authors
- Röthlisberger, D., Khersonsky, O., Wollacott, A. M., Jiang, L., DeChancie, J., Betker, J., Gallaher, J. L., Althoff, E. A., Zanghellini, A., Dym, O., Albeck, S., Houk, K. N., Tawfik, D. S., & Baker, D.
- Venue
- Nature
- Published
- 2008-03-19
- Volume
- 453
- Issue
- 7192
- Pages
- 190-195
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W1987696643
- Cited by
- 1,307
- References
- 1
- FWCI
- 38.84
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
