The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Automated Structure- and Sequence-Based Design of Proteins for High Bacterial Expression and Stability
Open accessAbstract
Upon heterologous overexpression, many proteins misfold or aggregate, thus resulting in low functional yields. Human acetylcholinesterase (hAChE), an enzyme mediating synaptic transmission, is a typical case of a human protein that necessitates mammalian systems to obtain functional expression. We developed a computational strategy and designed an AChE variant bearing 51 mutations that improved core packing, surface polarity, and backbone rigidity. This variant expressed at ∼2,000-fold higher levels in E. coli compared to wild-type hAChE and exhibited 20°C higher thermostability with no change in enzymatic properties or in the active-site configuration as determined by crystallography. To demonstrate broad utility, we similarly designed four other human and bacterial proteins. Testing at most three designs per protein, we obtained enhanced stability and/or higher yields of soluble and active protein in E. coli. Our algorithm requires only a 3D structure and several dozen sequences of naturally occurring homologs, and is available at http://pross.weizmann.ac.il.
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
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
The sirtuin SIRT6 regulates lifespan in male mice
Kanfi, Y., Naiman, S., Amir, G., Peshti, V., Zinman, G., Nahum, L., Bar-Joseph, Z., & Cohen, H. Y. (2012) · Nature · doi:10.1038/nature10815 · Cited by 1,059
Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin
Fleishman, S. J., Whitehead, T. A., Ekiert, D. C., Dreyfus, C., Corn, J. E., Strauch, E.-M., Wilson, I. A., & Baker, D. (2011) · Science · doi:10.1126/science.1202617 · Cited by 585
Self-citation
Cited by
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
Mega-scale experimental analysis of protein folding stability in biology and design
Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J. J., Mangan, N. M., Ovchinnikov, S., & Rocklin, G. J. (2023) · Nature · doi:10.1038/s41586-023-06328-6 · Cited by 350
Jia, B., & Jeon, C. O. (2016) · Open Biology · doi:10.1098/rsob.160196 · Cited by 295
Transfer learning to leverage larger datasets for improved prediction of protein stability changes
Dieckhaus, H., Brocidiacono, M., Randolph, N. Z., & Kuhlman, B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314853121 · Cited by 205
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-citationAlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function
Laurents, D. V. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.906437 · Cited by 95
Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., Lonishin, L. R., Meister, A. V., Yakupova, A. G., Kondrashov, F. A., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty340 · Cited by 93
Combinatorial assembly and design of enzymes
Lipsh-Sokolik, R., Khersonsky, O., Schröder, S. P., de Boer, C., Hoch, S.-Y., Davies, G. J., Overkleeft, H. S., & Fleishman, S. J. (2023) · Science · doi:10.1126/science.ade9434 · Cited by 78
Self-citationRecent Advances in β-Glucosidase Sequence and Structure Engineering: A Brief Review
Ouyang, B., Wang, G., Zhang, N., Zuo, J., Huang, Y., & Zhao, X. (2023) · Molecules · doi:10.3390/molecules28134990 · Cited by 56
End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman
Petti, S., Bhattacharya, N., Rao, R., Dauparas, J., Thomas, N., Zhou, J., Rush, A. M., Koo, P., & Ovchinnikov, S. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac724 · Cited by 54
Design of a Cereblon construct for crystallographic and biophysical studies of protein degraders
Kroupova, A., Spiteri, V. A., Rutter, Z. J., Furihata, H., Darren, D., Ramachandran, S., Chakraborti, S., Haubrich, K., Pethe, J., Gonzales, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52871-9 · Cited by 47
SNAP-tag2 for faster and brighter protein labeling
Kühn, S., Nasufovic, V., Wilhelm, J., Kompa, J., de Lange, E. M. F., Lin, Y.-H., Egoldt, C., Fischer, J., Lennoi, A., Tarnawski, M., et al. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-01942-z · Cited by 44
Computational stabilization of T cell receptors allows pairing with antibodies to form bispecifics
Froning, K., Maguire, J., Sereno, A., Huang, F., Chang, S., Weichert, K., Frommelt, A. J., Dong, J., Wu, X., Austin, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16231-7 · Cited by 37
Simultaneous enhancement of multiple functional properties using evolution-informed protein design
Fram, B., Su, Y., Truebridge, I., Riesselman, A. J., Ingraham, J. B., Passera, A., Napier, E., Thadani, N. N., Lim, S., Roberts, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49119-x · Cited by 34
Computational Stabilization of a Non‐Heme Iron Enzyme Enables Efficient Evolution of New Function
King, B. R., Sumida, K. H., Caruso, J. L., Baker, D., & Zalatan, J. G. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202414705 · 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
Self-citationESM ‐scan—A tool to guide amino acid substitutions
Totaro, M. G., Vide, U., Zausinger, R., Winkler, A., & Oberdorfer, G. (2024) · Protein Science · doi:10.1002/pro.5221 · Cited by 19
Ertelt, M., Moretti, R., Meiler, J., & Schoeder, C. T. (2025) · Science Advances · doi:10.1126/sciadv.adr7338 · Cited by 15
Platero-Rochart, D., Krivobokova, T., Gastegger, M., Reibnegger, G., & Sánchez-Murcia, P. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00772 · Cited by 15
Artificial intelligence and first‐principle methods in protein redesign: A marriage of convenience?
Cianferoni, D., Vizarraga, D., Fernández‐Escamilla, A. M., Fita, I., Hamdani, R., Reche, R., Delgado, J., & Serrano, L. (2025) · Protein Science · doi:10.1002/pro.70210 · Cited by 4
Optimizing Stability in Dynamic Small-Molecule Binding Proteins
Scherer, M., Kriegel, M., Höcker, B., & Fleishman, S. J. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.5c19571 · Cited by 2
Self-citationCombinatorial assembly and design of enzymes
Lipsh-Sokolik, R., Khersonsky, O., Schröder, S. P., de Boer, C., Hoch, S.-Y., Davies, G. J., Overkleeft, H. S., & Fleishman, S. J. (2022) · doi:10.1101/2022.09.17.508230 · Cited by 2
Self-citation
Metadata
- Authors
- Goldenzweig, A., Goldsmith, M., Hill, S. E., Gertman, O., Laurino, P., Ashani, Y., Dym, O., Unger, T., Albeck, S., Prilusky, J., Lieberman, R. L., Aharoni, A., Silman, I., Sussman, J. L., Tawfik, D. S., & Fleishman, S. J.
- Venue
- Molecular Cell
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 63
- Issue
- 2
- Pages
- 337-346
- ISSN
- 1097-2765 (ISSN-L)
- OpenAlex
- W2473135273
- Cited by
- 572
- References
- 4
- FWCI
- 13.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
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.