Massively parallel de novo protein design for targeted therapeutics
Open accessAbstract
De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37-43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Designing proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge.
“The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1–5.”
From Design of protein-binding proteins from the target structure alone (2022) · cites this paper as [1]
Most current computational methods used to design proteins that bind to a target surface utilize information derived from structures of the native complex on specific side-chain interactions or protein backbone placements optimal for binding.
“Most current computational methods used to design proteins that bind to a target surface utilize information derived from structures of the native complex on specific side-chain interactions or protein backbone placements optimal for binding1–3.”
From Design of protein-binding proteins from the target structure alone (2022) · cites this paper as [1]
Protein, peptide and small-molecule inhibitors have been designed to bind to the stem region of group 1 HA and neutralize influenza A viruses, but none recognize the group 2 HA.
“Protein1,5, peptide36 and small-molecule inhibitors37 have been designed to bind to the stem region of group 1 HA and neutralize influenza A viruses, but none recognize the group 2 HA.”
From Design of protein-binding proteins from the target structure alone (2022) · cites this paper as [1]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J., Impey, R., & Klein, M. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 38,335 · see citing works
[20] Processing of X-ray diffraction data collected in oscillation mode
Otwinowski, Z., & Minor, W. (1997) · Methods in Enzymology · doi:10.1016/s0076-6879(97)76066-x · Cited by 33,475 · see citing works
<i>Coot</i> : model-building tools for molecular graphics
Emsley, P., & Cowtan, K. (2004) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444904019158 · Cited by 30,571 · see citing works
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M., Darden, T., Lee, H.-C., & Pedersen, L. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 21,329 · see citing works
<i>Phaser</i> crystallographic software
McCoy, A. J., Grosse-Kunstleve, R. W., Adams, P. D., Winn, M. D., Storoni, L. C., & Read, R. J. (2007) · Journal of Applied Crystallography · doi:10.1107/s0021889807021206 · Cited by 20,612 · see citing works
GROMACS: Fast, flexible, and free
van der Spoel, D., Lindahl, E. R., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20291 · Cited by 19,292 · see citing works
LINCS: A linear constraint solver for molecular simulations
Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199709)18:12 · Cited by 18,369 · see citing works
Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,024 · see citing works
Kabsch, W. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909047337 · Cited by 16,463 · see citing works
<i>MolProbity</i> : all-atom structure validation for macromolecular crystallography
Chen, V. B., Arendall, W. B., Headd, J. J., Keedy, D. A., Immormino, R. M., Kapral, G. J., Murray, L. W., Richardson, J. S., & Richardson, D. C. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909042073 · Cited by 14,115 · see citing works
Improved side-chain torsion potentials for the Amber ff99SB protein force field
Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J., Dror, R., & Shaw, D. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22711 · Cited by 6,037 · see citing works
PEAR: a fast and accurate Illumina Paired-End reAd mergeR
Zhang, J., Kobert, K., Flouri, T., & Stamatakis, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt593 · Cited by 4,497 · see citing works
Free R value: a novel statistical quantity for assessing the accuracy of crystal structures
Brünger, A. T. (1992) · Nature · doi:10.1038/355472a0 · Cited by 3,877 · see citing works
Constant pressure molecular dynamics for molecular systems
Nosé, S., & Klein, M. L. (1983) · Molecular Physics · doi:10.1080/00268978300102851 · Cited by 3,402 · see citing works
Corti, D., Voss, J., Gamblin, S. J., Codoni, G., Macagno, A., Jarrossay, D., Vachieri, S. G., Pinna, D., Minola, A., Vanzetta, F., et al. (2011) · Science · doi:10.1126/science.1205669 · Cited by 1,203 · see citing works
Waskom, M., Botvinnik, O. B., O'Kane, D., Hobson, P., Ostblom, J., Lukauskas, S., Gemperline, D. C., Augspurger, T., Halchenko, Y., Cole, J. B., et al. (2014) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.592845 · Cited by 1,119 · see citing works
Isolating and engineering human antibodies using yeast surface display
Chao, G., Lau, W. L., Hackel, B. J., Sazinsky, S. L., Lippow, S. M., & Wittrup, K. D. (2006) · Nature Protocols · doi:10.1038/nprot.2006.94 · Cited by 913 · see citing works
The Phenix Software for Automated Determination of Macromolecular Structures
Adams, P., Afonine, P., Bunkoczi, G., Chen, V. B., Echols, N., Headd, J., Hung, L., Jain, S., Kapral, G. J., Grosse-Kunstleve, R., et al. (2011) · Methods · doi:10.1016/j.ymeth.2011.07.005 · Cited by 912 · see citing works
The Structure and Receptor Binding Properties of the 1918 Influenza Hemagglutinin
Gamblin, S. J., Haire, L. F., Russell, R. J., Stevens, D. J., Xiao, B., Ha, Y., Vasisht, N., Steinhauer, D. A., Daniels, R. S., Elliot, A. J., et al. (2004) · Science · doi:10.1126/science.1093155 · Cited by 712 · see citing works
RosettaScripts: A Scripting Language Interface to the Rosetta Macromolecular Modeling Suite
Fleishman, S. J., Leaver‐Fay, A., Corn, J. E., Strauch, E., Khare, S. D., Koga, N., Ashworth, J. R., Murphy, P. M., Richter, F. M., Lemmon, G., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020161 · Cited by 659 · see citing works
Self-citationWatson, G. (1988) · Cited by 614 · see citing works
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 · see citing works
Self-citationPrinciples 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 · see citing works
Self-citationDNAWorks: an automated method for designing oligonucleotides for PCR-based gene synthesis
Hoover, D. M. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.10.e43 · Cited by 521 · see citing works
Global analysis of protein folding using massively parallel design, synthesis, and testing
Rocklin, G. J., Chidyausiku, T. M., Goreshnik, I., Ford, A., Houliston, S., Lemak, A., Carter, L., Ravichandran, R., Mulligan, V. K., Chevalier, A., et al. (2017) · Science · doi:10.1126/science.aan0693 · Cited by 501 · see citing works
Self-citationAccurate de novo design of hyperstable constrained peptides
Bhardwaj, G., Mulligan, V. K., Bahl, C. D., Gilmore, J. M., Harvey, P. J., Cheneval, O., Buchko, G. W., Pulavarti, S. V. S. R. K., Kaas, Q., Eletsky, A., et al. (2016) · Nature · doi:10.1038/nature19791 · Cited by 442 · see citing works
Self-citationAn improved yeast transformation method for the generation of very large human antibody libraries
Benatuil, L., Perez, J. M., Belk, J., & Hsieh, C.-M. (2010) · Protein Engineering Design and Selection · doi:10.1093/protein/gzq002 · Cited by 435 · see citing works
RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design
Huang, P., Ban, Y. A., Richter, F. M., André, I., Vernon, R. M., Schief, W. R., & Baker, D. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024109 · Cited by 373 · see citing works
Self-citationWhitehead, T. A., Chevalier, A. A., Song, Y., Dreyfus, C., Fleishman, S. J., de Mattos, C., Myers, C., Kamisetty, H., Blair, P. J., Wilson, I. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2214 · Cited by 364 · see citing works
Self-citationEngineered protein scaffolds as next-generation antibody therapeutics
Gebauer, M., & Skerra, A. (2009) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2009.04.627 · Cited by 339 · see citing works
Zahnd, C., Kawe, M., Stumpp, M. T., de Pasquale, C., Tamaskovic, R., Nagy‐Davidescu, G., Dreier, B., Schibli, R., Binz, H. K., Waibel, R., et al. (2010) · Cancer Research · doi:10.1158/0008-5472.can-09-2724 · Cited by 245 · see citing works
Botulinum neurotoxin B recognizes its protein receptor with high affinity and specificity
Jin, R., Rummel, A., Binz, T., & Brünger, A. T. (2006) · Nature · doi:10.1038/nature05387 · Cited by 235 · see citing works
Self-citationHurt, A. C., Chotpitayasunondh, T., Cox, N. J., Daniels, R. S., Fry, A. M., Gubareva, L. V., Hayden, F. G., Hui, D. S. C., Hungnes, O., Lackenby, A., et al. (2011) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(11)70318-8 · Cited by 229 · see citing works
Cass, L., Efthymiopoulos, C., & Bye, A. (1999) · Clinical Pharmacokinetics · doi:10.2165/00003088-199936001-00001 · Cited by 227 · see citing works
Challenges and opportunities for non-antibody scaffold drugs
Vazquez-Lombardi, R., Phan, T. G., Zimmermann, C., Lowe, D., Jermutus, L., & Christ, D. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2015.09.004 · Cited by 226 · see citing works
Spasmodic dysphonia and botulinum toxin: experience from the largest treatment series
Blitzer, A. (2010) · European Journal of Neurology · doi:10.1111/j.1468-1331.2010.03047.x · Cited by 138 · see citing works
Control over overall shape and size in de novo designed proteins
Lin, Y., Koga, N., Koga, R., Liu, G., Clouser, A. F., Montelione, G. T., & Baker, D. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509508112 · Cited by 134 · see citing works
Self-citationProcko, E., Berguig, G. Y., Shen, B. W., Song, Y., Frayo, S., Convertine, A. J., Margineantu, D., Booth, G., Correia, B. E., Cheng, Y., et al. (2014) · Cell · doi:10.1016/j.cell.2014.04.034 · Cited by 127 · see citing works
Self-citationRemoving T-cell epitopes with computational protein design
King, C., Garza, E. N., Mazor, R., Linehan, J. L., Pastan, I. H., Pepper, M., & Baker, D. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1321126111 · Cited by 121 · see citing works
Self-citationEngineered knottin peptides as diagnostics, therapeutics, and drug delivery vehicles
Kintzing, J. R., & Cochran, J. R. (2016) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2016.08.022 · Cited by 118 · see citing works
Production of complex nucleic acid libraries using highly parallel in situ oligonucleotide synthesis
Cleary, M. A., Kilian, K. A., Wang, Y., Bradshaw, J., Cavet, G., Ge, W., Kulkarni, A., Paddison, P. J., Chang, K., Sheth, N. U., et al. (2004) · Nature Methods · doi:10.1038/nmeth724 · Cited by 108 · see citing works
Berger, S., Procko, E., Margineantu, D. H., Lee, E. F., Shen, B. W., Zelter, A., Silva, D., Chawla, K., Herold, M. J., Garnier, J.-M., et al. (2016) · eLife · doi:10.7554/elife.20352 · Cited by 93 · see citing works
Self-citationPRALINE: A Versatile Multiple Sequence Alignment Toolkit
Bawono, P., & Heringa, J. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-646-7_16 · Cited by 78 · see citing works
SwiftLib: rapid degenerate-codon-library optimization through dynamic programming
Jacobs, T. M., Yumerefendi, H., Kuhlman, B. A., & Leaver‐Fay, A. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1323 · Cited by 65 · see citing works
Motif-Driven Design of Protein–Protein Interfaces
Silva, D., Correia, B. E., & Procko, E. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3569-7_17 · Cited by 60 · see citing works
Self-citationKoday, M. T., Nelson, J., Chevalier, A. A., Koday, M., Kalinoski, H. M., Stewart, L. J., Carter, L. P., Nieusma, T., Lee, P. S., Ward, A. B., et al. (2016) · PLoS Pathogens · doi:10.1371/journal.ppat.1005409 · Cited by 55 · see citing works
Self-citationWaskom, M., Botvinnik, O. B., drewokane, et al. (2016) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.54844 · Cited by 55 · see citing works
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 · see citing works
Structure of Botulinum neurotoxin B binding domain in complex with both synaptotagmin II and GD1a
Berntsson, R. P. ‐., Peng, L., Dong, M., & Stenmark, P. (2013) · Worldwide Protein Data Bank · doi:10.2210/pdb4kbb/pdb · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Molecular Dynamics Simulation for All
Hollingsworth, S. A., & Dror, R. O. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.011 · Cited by 3,313 · see citing works
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2022) · doi:10.1101/2022.12.09.519842 · Cited by 2,369 · see citing works
Self-citationDe novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Self-citationAdvances 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 · see citing works
De novo design of picomolar SARS-CoV-2 miniprotein inhibitors
Cao, L., Goreshnik, I., Coventry, B., Case, J. B., Miller, L., Kozodoy, L., Chen, R. E., Carter, L., Walls, A. C., Park, Y.-J., et al. (2020) · Science · doi:10.1126/science.abd9909 · Cited by 742 · see citing works
Self-citationDesign of protein-binding proteins from the target structure alone
Cao, L., Coventry, B., Goreshnik, I., Huang, B., Sheffler, W., Park, J. S., Jude, K. M., Marković, I., Kadam, R. U., Verschueren, K. H. G., et al. (2022) · Nature · doi:10.1038/s41586-022-04654-9 · Cited by 577 · see citing works
Self-citationDe novo design of potent and selective mimics of IL-2 and IL-15
Silva, D.-A., Yu, S., Ulge, U. Y., Spangler, J. B., Jude, K. M., Labão-Almeida, C., Ali, L. R., Quijano-Rubio, A., Ruterbusch, M., Leung, I., et al. (2019) · Nature · doi:10.1038/s41586-018-0830-7 · Cited by 531 · see citing works
Self-citationChoi, K. R., Jang, W. D., Yang, D., Cho, J. S., Park, D., & Lee, S. Y. (2019) · Trends in biotechnology · doi:10.1016/j.tibtech.2019.01.003 · Cited by 565 · see citing works
Scaffolding 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 · see citing works
“…acid identities which produce the desired activity--for enzymes this can be done using quantum chemistry calculations (1–3) and for protein binders by fragment docking calculations (4, 5); alternatively, functional sites can be extracted from a native protein having the desired activity (6, 7).”
BackgroundSelf-citationEngineering bacteria for diagnostic and therapeutic applications
Riglar, D. T., & Silver, P. A. (2018) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2017.172 · Cited by 460 · see citing works
Improving de novo protein binder design with deep learning
Bennett, N. R., Coventry, B., Goreshnik, I., Huang, B., Allen, A., Vafeados, D., Peng, Y. P., Dauparas, J., Baek, M., Stewart, L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38328-5 · Cited by 402 · see citing works
Self-citationEngineering protein-based therapeutics through structural and chemical design
Ebrahimi, S. B., & Samanta, D. (2023) · Nature Communications · doi:10.1038/s41467-023-38039-x · Cited by 389 · see citing works
“Efforts in this area have led to the identi fi cation of potent mimics of cytokines interleukin-2 (IL-2) and IL-15 78 , programmed cell death protein-1 (PD-1) agonists 79 , and SARS-CoV-2 inhibitors 80 , among others 81,82 .”
Protein Design: From the Aspect of Water Solubility and Stability
Qing, R., Hao, S.-L., Smorodina, E., Jin, D., Zalevsky, A. O., & Zhang, S.-G. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00757 · Cited by 287 · see citing works
Barlow, K. A., Conchúir, S. Ó., Thompson, S., Suresh, P., Lucas, J. E., Heinonen, M., & Kortemme, T. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b11367 · Cited by 298 · see citing works
Building machines with DNA molecules
Ramezani, H., & Dietz, H. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0175-6 · Cited by 284 · see citing works
De novo design of modular and tunable protein biosensors
Quijano-Rubio, A., Yeh, H.-W., Park, J., Lee, H., Langan, R. A., Boyken, S. E., Lajoie, M. J., Cao, L., Chow, C. M., Miranda, M. C., et al. (2021) · Nature · doi:10.1038/s41586-021-03258-z · Cited by 260 · see citing works
Self-citationInfluenza Hemagglutinin Structures and Antibody RecognitionPreprint
Wu, N. C., & Wilson, I. A. (2019) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a038778 · Cited by 253 · see citing works
“Identification of these common binding features facilitated structure-based design of stem-binding proteins (Fleishman et al. 2011; Whitehead et al. 2012; Chevalier et al. 2017), peptides (Kadam et al. 2017), and small molecules (van Dongen et al. 2019) with neutralizing activity akin to the bnAbs.”
BackgroundSelf-citationDe novo protein design, a retrospective
Korendovych, I. V., & DeGrado, W. F. (2020) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583519000131 · Cited by 249 · see citing works
“Combining computational design with experimental library screening to achieve function In another pioneering contribution, Baker and coworkers (Chevalier et al., 2017) integrated large-scale computational design, parallel oligonucleotide synthesis, yeast display screening, and next-generation…”
MethodDe novo design of high-affinity binders of bioactive helical peptides
Torres, S. V., Leung, P. J. Y., Venkatesh, P., Lutz, I. D., Hink, F., Huynh, H., Becker, J. O., Yeh, A. H.-W., Juergens, D., Bennett, N. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06953-1 · Cited by 265 · see citing works
Self-citationRecent advances in de novo protein design: Principles, methods, and applications
Pan, X., & Kortemme, T. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.100558 · Cited by 221 · see citing works
“Exciting recently designed functions include inhibitors of viral infections (3, 4), immune system modulators (5, 6), self-assembling biomaterials (7 – 9), sense-and-respond signaling systems (10 – 13), and protein logic gates (14, 15).”
BackgroundFast and Flexible Protein Design Using Deep Graph Neural Networks
Strokach, A. V., Becerra, D., Corbi‐Verge, C., Perez‐Riba, A., & Kim, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.016 · Cited by 218 · see citing works
Nanocarriers for the Delivery of Medical, Veterinary, and Agricultural Active Ingredients
Chariou, P. L., Ortega-Rivera, O. A., & Steinmetz, N. F. (2020) · ACS Nano · doi:10.1021/acsnano.0c00173 · Cited by 212 · see citing works
De novo protein design enables the precise induction of RSV-neutralizing antibodies
Sesterhenn, F., Yang, C., Bonet, J., Cramer, J. T., Wen, X., Wang, Y., Chiang, C.-I., Abriata, L. A., Kucharska, I., Castoro, G., et al. (2020) · Science · doi:10.1126/science.aay5051 · Cited by 191 · see citing works
“3B), an effective method to digest partially unfolded proteins (7, 38).”
BackgroundMethodDe novo design of potent and resilient hACE2 decoys to neutralize SARS-CoV-2
Linsky, T. W., Vergara, R., Codina, N., Nelson, J. W., Walker, M. J., Su, W., Barnes, C. O., Hsiang, T.-Y., Esser-Nobis, K., Yu, K., et al. (2020) · Science · doi:10.1126/science.abe0075 · Cited by 216 · see citing works
Self-citationOpportunities and challenges in design and optimization of protein function
Listov, D., Goverde, C. A., Correia, B. E., & Fleishman, S. J. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00718-y · Cited by 192 · see citing works
“De novo-designed proteins have been further designed to generate new binders of proteins or small molecules 24,26,27 , thereby advancing towards the completely rational generation of new-to-nature protein activities.”
Beigel, J. H., Nam, H. H., Adams, P. L., Krafft, A. E., Ince, W. L., El‐Kamary, S. S., & Sims, A. M. (2019) · Antiviral Research · doi:10.1016/j.antiviral.2019.04.006 · Cited by 168 · see citing works
“, 2011) and manufacturability (Chevalier et al., 2017) have significantly improved in recent years.”
BackgroundA brief history of de novo protein design: minimal, rational, and computational.
Woolfson, D. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167160 · Cited by 139 · see citing works
Wang, L., Pereira, L. D. S., Flores-García, Y., O’Connor, J. H., Flynn, B. J., Schön, A., Hurlburt, N. K., Dillon, M., Yang, A., Fabra-García, A., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.08.014 · Cited by 154 · see citing works
“Generation of junctional probe—S02, a 43-residue structurally stabilized peptide mimicking the junctional epitope (MPESSSNPDCNANPNVDPNEDLIKKCEKINVPTEEIKKEIEEKK), was designed using Rosetta (Chevalier et al., 2017) by modifying the junctional peptide 21 with flanking sequences to stabilize the peptide in a conformation…”
MethodInfluentialSelf-citationDesigned endocytosis-inducing proteins degrade targets and amplify signals
Huang, B., Abedi, M. H., Ahn, G., Coventry, B., Sappington, I., Tang, C., Wang, R., Schlichthaerle, T., Zhang, J. Z., Wang, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07948-2 · Cited by 167 · see citing works
“De novo protein design has the advantage of being able to target binders to specific sites of interest on a target 15–17 , and is thus well suited to designing protein binders that target receptor sites that do not overlap with those of native ligands.”
Self-citationComputational Drug Design Methods—Current and Future Perspectives
Prieto-Martínez, F. D., López‐López, E., Juárez‐Mercado, K. E., & Medina‐Franco, J. L. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00002-x · Cited by 152 · see citing works
B cells engineered to express pathogen-specific antibodies protect against infection
Moffett, H. F., Harms, C. K., Fitzpatrick, K. S., Tooley, M. R., Boonyaratanakornkit, J. B., & Taylor, J. J. (2019) · Science Immunology · doi:10.1126/sciimmunol.aax0644 · Cited by 147 · see citing works
Diffusion models in bioinformatics and computational biology
Guo, Z., Liu, J., Wang, Y., Chen, M., Wang, D., Xu, D., & Cheng, J. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00114-9 · Cited by 163 · see citing works
“…discussed in this section, some proteins designed by RFdiffusion have not only been validated in silico, but also by biochemical and biophysical experiments 170,171 , making it one of the first generative artificial intelligence methods of protein design that have been experimentally validated.”
The JAK-STAT pathway: from structural biology to cytokine engineering
Lv, Y., Qi, J., Babon, J. J., Cao, L., Fan, G.-H., Lang, J., Zhang, J., Mi, P., Kobe, B., & Wang, F. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01934-w · Cited by 149 · see citing works
“419,420,423 – 427 In recent times, AI approaches for protein structure prediction and design may hold promise for cytokine design.”
BackgroundThird generation antibody discovery methods: in silico rational design
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00523k · Cited by 136 · see citing works
Programmable protein circuit design
Chen, Z., & Elowitz, M. B. (2021) · Cell · doi:10.1016/j.cell.2021.03.007 · Cited by 137 · see citing works
“, 2008), and designed protein binders (Chevalier et al., 2017) may partly address these issues.”
BackgroundDesign of metalloproteins and novel protein folds using variational autoencoders
Greener, J. G., Moffat, L., & Jones, D. T. (2018) · Scientific Reports · doi:10.1038/s41598-018-34533-1 · Cited by 135 · see citing works
Game changers in science and technology - now and beyond
Betz, U. A. K., Arora, L., Assal, R. A., de Azevedo, H. F. Z., Baldwin, J., Becker, M. S., Bostock, S., Cheng, V. W. T., Egle, T., Ferrari, N., et al. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122588 · Cited by 146 · see citing works
Deep learning techniques have significantly impacted protein structure prediction and protein design
Pearce, R., & Zhang, Y. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.01.007 · Cited by 127 · see citing works
Protein Nanoparticles: Uniting the Power of Proteins with Engineering Design Approaches
Habibi, N., Mauser, A., Ko, Y., & Lahann, J. (2022) · Advanced Science · doi:10.1002/advs.202104012 · Cited by 126 · see citing works
Coiled coil protein origami: from modular design principles towards biotechnological applications.
Lapenta, F., Aupič, J., Strmšek, Ž., & Jerala, R. (2018) · Chemical Society Reviews · doi:10.1039/c7cs00822h · Cited by 74 · see citing works
Hunt, A. C., Case, J. B., Park, Y., Cao, L., Wu, K., Walls, A. C., Liu, Z., Bowen, J. E., Yeh, A. H.-W., Saini, S., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abn1252 · Cited by 116 · see citing works
Self-citationDeep generative modeling for protein design
Strokach, A. V., & Kim, P. M. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.008 · Cited by 114 · see citing works
BindCraft: one-shot design of functional protein bindersPreprint
Pačesa, M., Nickel, L., Schellhaas, C., Schmidt, J. H., Pyatova, E., Kissling, L., Barendse, P., Choudhury, J., Kapoor, S., Alcaraz‐Serna, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.30.615802 · Cited by 111 · see citing works
Machine learning to navigate fitness landscapes for protein engineering
Freschlin, C. R., Fahlberg, S. A., & Romero, P. A. (2022) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102713 · Cited by 105 · see citing works
“Protein engineering aims to realize this potential by altering protein functions for applications in biotechnology, industry, and medicine [1–3].”
BackgroundSun, H., Li, Y., Yu, S., & Liu, J. (2020) · Nano Today · doi:10.1016/j.nantod.2020.100985 · Cited by 105 · see citing works
Heterotypic immunity against vaccinia virus in an HLA-B*07:02 transgenic mousepox infection model
Kumar, A., Suryadevara, N., Wolf, K. J., Wilson, J. T., DiPaolo, R. J., Brien, J. D., & Joyce, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69897-w · Cited by 100 · see citing works
Schlichthaerle, T., Strauss, M. T., Schueder, F., Auer, A., Nijmeijer, B., Kueblbeck, M., Sabinina, V. J., Thevathasan, J. V., Ries, J., Ellenberg, J., et al. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201905685 · Cited by 97 · see citing works
Structural insights into the design of novel anti-influenza therapies
Wu, N. C., & Wilson, I. A. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0025-9 · Cited by 96 · see citing works
Self-citationBottom-up de novo design of functional proteins with complex structural features
Yang, C., Sesterhenn, F., Bonet, J., van Aalen, E. A., Scheller, L., Abriata, L. A., Cramer, J. T., Wen, X., Rosset, S., Georgeon, S., et al. (2021) · Nature Chemical Biology · doi:10.1038/s41589-020-00699-x · Cited by 84 · see citing works
Preclinical proof of principle for orally delivered Th17 antagonist miniproteins
Berger, S., Seeger, F., Yu, T.-Y., Aydin, M., Hui-lin, Y., Rosenblum, D., Guenin‐Macé, L., Glassman, C. R., Arguinchona, L., Sniezek, C. M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.05.052 · Cited by 93 · see citing works
“Several designed proteins have generated very low or undetectable levels of anti-mini-binder IgG after repeated systemic dosing in mice 14–16 and repeated oral dosing in humans.”
BackgroundSelf-citationSolubility, Stability, and Avidity of Recombinant Antibody Fragments Expressed in Microorganisms
Kang, T. H., & Seong, B. L. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01927 · Cited by 89 · see citing works
“, 2017), have enabled successful de novo antibody engineering (Chevalier et al., 2017), such as anti-influenza antibodies (Strauch et al.”
BackgroundMiniproteins as a Powerful Modality in Drug Development
Crook, Z. R., Nairn, N. W., & Olson, J. M. (2020) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2019.12.008 · Cited by 87 · see citing works
Pearce, R., Huang, X., Setiawan, D., & Zhang, Y. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.028 · Cited by 83 · see citing works
“This approach has been successfully applied to the redesign of various protein systems [4–8], and holds tremendous potential for the development of novel therapeutics, enzymes, and other useful proteins.”
MethodSelf-citationRichards, D. A. (2018) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2018.10.005 · Cited by 83 · see citing works
An enumerative algorithm for de novo design of proteins with diverse pocket structures
Basanta, B., Bick, M. J., Bera, A. K., Norn, C., Chow, C. M., Carter, L. P., Goreshnik, I., DiMaio, F., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005412117 · Cited by 81 · see citing works
“This has been achieved for protein–protein binding due to the simplicity of small globular proteins (10).”
Self-citationEvolving a Peptide: Library Platforms and Diversification Strategies
Bozovičar, K., & Bratkovič, T. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms21010215 · Cited by 81 · see citing works
“Today, new technologies enable accurately modeling peptide structure as long as 40 residues [276], and in silico design and screening using bioinformatics tools like Rosetta [277].”
MethodGetting Momentum: From Biocatalysis to Advanced Synthetic Biology
Badenhorst, C. P. S., & Bornscheuer, U. T. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.01.003 · Cited by 79 · see citing works
Multi-input chemical control of protein dimerization for programming graded cellular responses
Wink, G., Wang, Z., Wei, C. T., Greisen, P. J., Warner, K., Cunningham‐Bryant, D., Park, K., Brunette, T., Sheffler, W., Baker, D. A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0242-8 · Cited by 79 · see citing works
Self-citationQi, Y., & Zhang, J. Z. H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00043 · Cited by 79 · see citing works
Case, J. B., Chen, R. E., Cao, L., Ying, B., Winkler, E. S., Johnson, M., Goreshnik, I., Pham, M. N., Shrihari, S., Kafai, N. M., et al. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2021.06.008 · Cited by 73 · see citing works
“Indeed, miniprotein inhibitors against influenza virus have shown efficacy by nasal delivery (Chevalier et al., 2017).”
Self-citationCo‐Evolutionary Fitness Landscapes for Sequence Design
Tian, P., Louis, J. M., Baber, J. L., Aniana, A., & Best, R. B. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201713220 · Cited by 76 · see citing works
De novo designed protein inhibitors of amyloid aggregation and seeding
Murray, K. A., Hu, C. J., Griner, S. L., Pan, H., Bowler, J. T., Abskharon, R. N. N., Rosenberg, G. M., Cheng, X., Seidler, P. M., & Eisenberg, D. S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2206240119 · Cited by 76 · see citing works
“incorporated into the designs, as has been done in previous studies, because of the reducing conditions of the cytoplasm (44).”
MethodHigh-throughput directed evolution: a golden era for protein science
McLure, R. J., Radford, S. E., & Brockwell, D. J. (2022) · Trends in Chemistry · doi:10.1016/j.trechm.2022.02.004 · Cited by 75 · see citing works
The role and future prospects of artificial intelligence algorithms in peptide drug development.
Chen, Z.-H., Wang, R.-X., Guo, J., & Wang, X. (2024) · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · doi:10.1016/j.biopha.2024.116709 · Cited by 72 · see citing works
AlphaDesign: A <i>de novo</i> protein design framework based on AlphaFold
Jendrusch, M., Korbel, J. O., & Sadiq, S. K. (2021) · doi:10.1101/2021.10.11.463937 · Cited by 65 · see citing works
“Recent examples include protein logic gates [38], self-assembling systems [39] and targeted therapeutics [40].”
BackgroundDe Novo-Designed α-Helical Barrels as Receptors for Small Molecules.
Thomas, F., Dawson, W., Lang, E. J. M., Burton, A. J., Bartlett, G. J., Rhys, G. G., Mulholland, A., & Woolfson, D. (2018) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00225 · Cited by 63 · see citing works
Reconfigurable asymmetric protein assemblies through implicit negative design
Sahtoe, D. D., Praetorius, F., Courbet, A., Hsia, Y., Wicky, B. I. M., Edman, N. I., Miller, L. M., Timmermans, B. J. R., Decarreau, J. A., Morris, H. M., et al. (2022) · Science · doi:10.1126/science.abj7662 · Cited by 72 · see citing works
“Themini- mizeddockedstrandswerethengeometri-callymatchedtothescaffoldlibraryusingthe MotifGraftMovertocreateadockedprotein-proteincomplex( 36 ).”
Self-citationBiomolecular Modeling and Simulation: A Prospering Multidisciplinary Field
Tamar Schlick, A., Portillo‐Ledesma, S., Myers, C. G., Beljak, L., Chen, J. B., Dakhel, S., Darling, D., Ghosh, S., Hall, J. W., Jan, M., et al. (2021) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-091720-102019 · Cited by 68 · see citing works
“Various high-throughput miniprotein design techniques,such as the massively parallel de novo protein design platform of the Baker group (29), can design proteins with customized shapes to bind therapeutic targets.”
BackgroundDe novo design of drug-binding proteins with predictable binding energy and specificity
Lü, L., Gou, X., Tan, S. K., Mann, S. I., Yang, H., Zhong, X., Gazgalis, D., Valdiviezo, J., Jo, H., Wu, Y., et al. (2024) · Science · doi:10.1126/science.adl5364 · Cited by 73 · see citing works
Rotamer-free protein sequence design based on deep learning and self-consistency
Liu, Y., Zhang, L., Wang, W., Zhu, M., Wang, C., Li, F., Zhang, J., Li, H., Chen, Q., & Liu, H. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00273-6 · Cited by 72 · see citing works
“12–15) suggest that all of these proteins are monomeric and fold into well-defined three-dimensional structures.”
BackgroundSelf-citationProtein design via deep learning
Ding, W., Nakai, K., & Gong, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac102 · Cited by 66 · see citing works
“Advances in immune signaling [2, 3], targeted therapeutics [4, 5], sense-response systems [6], protein switches [7, 8], self-assembly materials [9, 10] and other fields not mentioned here have shown the exciting potential of utilizing proteins as functional and reproducible materials.”
Zhu, X., Turner, H. L., Lang, S., McBride, R., Bangaru, S., Gilchuk, I. M., Yu, W., Paulson, J. C., Crowe, J. E., Ward, A. B., et al. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.10.002 · Cited by 68 · see citing works
“Our results reveal sites of vulnerability on NA that may inform design of new drugs, such as therapeutic antibodies, small proteins, peptides or small molecules [as for the HA (Chevalier et al., 2017; Fleishman et al., 2011; Kadam et al., 2017; Laursen et al., 2018; Strauch et al., 2017; van Dongen et al., 2019)], to…”
BackgroundSelf-citationSYNBIP: synthetic binding proteins for research, diagnosis and therapy
Wang, X., Li, F., Qiu, W., Xu, B., Li, Y., Lian, X., Yu, H., Zhang, Z., Wang, J., Li, Z., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab926 · Cited by 68 · see citing works
Different genetic barriers for resistance to HA stem antibodies in influenza H3 and H1 viruses
Wu, N. C., Thompson, A. J., Lee, J. M., Su, W., Arlian, B. M., Xie, J., Lerner, R. A., Yen, H., Bloom, J. D., & Wilson, I. A. (2020) · Science · doi:10.1126/science.aaz5143 · Cited by 68 · see citing works
Self-citationPeng, J., Wang, W., Yu, Y., Gu, H.-L., & Huang, X.-H. (2018) · Chinese Journal of Chemical Physics · doi:10.1063/1674-0068/31/cjcp1806147 · Cited by 52 · see citing works
Self-citationComputational Design of Potent D-Peptide Inhibitors of SARS-CoV-2
Valiente, P. A., Wen, H., Nim, S., Lee, J., Kim, H. J., Kim, J., Perez‐Riba, A., Paudel, Y. P., Hwang, I., Kim, K., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00655 · Cited by 66 · see citing works
DNA-based construction at the nanoscale: emerging trends and applications
Xavier, P. L., & Chandrasekaran, A. R. (2017) · Nanotechnology · doi:10.1088/1361-6528/aaa120 · Cited by 65 · see citing works
“nificant roles in the development of bionanomaterials and biophysical applications [276, 277].”
BackgroundMarchand, A., Hall‐Beauvais, A. V., & Correia, B. E. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102370 · Cited by 65 · see citing works
Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists
Glögl, M., Krishnakumar, A., Ragotte, R. J., Goreshnik, I., Coventry, B., Bera, A. K., Kang, A., Joyce, E., Ahn, G., Huang, B., et al. (2024) · Science · doi:10.1126/science.adp1779 · Cited by 67 · see citing works
Self-citationStructural Biology of Influenza Hemagglutinin: An Amaranthine Adventure
Wu, N. C., & Wilson, I. A. (2020) · Viruses · doi:10.3390/v12091053 · Cited by 63 · see citing works
Self-citationIntegrative proteomics identifies thousands of distinct, multi-epitope, and high-affinity nanobodies
Xiang, Y., Sang, Z., Bitton, L., Xu, J., Liu, Y., Schneidman‐Duhovny, D., & Shi, Y. (2021) · Cell Systems · doi:10.1016/j.cels.2021.01.003 · Cited by 61 · see citing works
Engineering synthetic regulatory circuits in plants
Kassaw, T. K., Donayre-Torres, A. J., Antunes, M. S., Morey, K. J., & Medford, J. I. (2018) · Plant Science · doi:10.1016/j.plantsci.2018.04.005 · Cited by 60 · see citing works
Computational design of a synthetic PD-1 agonist
Bryan, C. M., Rocklin, G. J., Bick, M. J., Ford, A., Majri-Morrison, S. S., Kroll, A. V., Miller, C. J., Carter, L. P., Goreshnik, I., Kang, A., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102164118 · Cited by 61 · see citing works
“Oligo pools were amplified and transformed into yeast as previously described (25).”
Self-citationBeyond genomics in Patescibacteria: A trove of unexplored biology packed into ultrasmall bacteria
Srinivas, P., Peterson, S. B., Gallagher, L. A., Wang, Y.-X., & Mougous, J. D. (2024) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2419369121 · Cited by 56 · see citing works
“The development of nanobodies or synthetic peptide binders designed to target proteins of interest could also be a useful tool for linking the numerous essential genes with no characterized homologs to phenotypes, as a first step toward uncovering their functions (65).”
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · see citing works
Comparing proteins and nucleic acids for next-generation biomolecular engineering
Pugh, G., Burns, J. R., & Howorka, S. (2018) · Nature Reviews Chemistry · doi:10.1038/s41570-018-0015-9 · Cited by 58 · see citing works
Programming Structured DNA Assemblies to Probe Biophysical Processes
Wamhoff, E., Banal, J. L., Bricker, W. P., Shepherd, T. R., Parsons, M. F., Veneziano, R., Stone, M. B., Jun, H., Wang, X., & Bathe, M. (2019) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-052118-115259 · Cited by 57 · see citing works
Next generation of multispecific antibody engineering
Kéri, D., Walker, M., Singh, I., Nishikawa, K. K., & Garcés, F. (2023) · Antibody Therapeutics · doi:10.1093/abt/tbad027 · Cited by 57 · see citing works
Yao, Y., Kadam, R. U., Lee, C.-C. D., Woehl, J. L., Wu, N. C., Zhu, X., Kitamura, S., Wilson, I. A., & Wolan, D. W. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006893117 · Cited by 57 · see citing works
“4 (25) from the group 1 HA stem epitope.”
BackgroundSelf-citationChang, L., Mondal, A., Singh, B., Martínez‐Noa, Y., & Perez, A. (2024) · WIREs Computational Molecular Science · doi:10.1002/wcms.1693 · Cited by 56 · see citing works
“53,54 Rosetta-based designs typically draw information from developer expertise and complementary experiments such as directed evolution.”
BackgroundMethodDesign and structural validation of peptide–drug conjugate ligands of the kappa-opioid receptor
Muratspahić, E., Deibler, K., Han, J., Tomašević, N., Jadhav, K. B., Olivé-Marti, A.-L., Hochrainer, N., Hellinger, R., Koehbach, J., Fay, J. F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43718-w · Cited by 54 · see citing works
“2) 20 .”
Self-citationCode to complex: AI-driven de novo binder design
Fox, D., Taveneau, C., Clement, J., Grinter, R., & Knott, G. J. (2025) · Structure · doi:10.1016/j.str.2025.08.007 · Cited by 59 · see citing works
Essentials of de novo protein design: Methods and applications
Marcos, E., & Silva, D. (2018) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1374 · Cited by 55 · see citing works
“37 Finally, Chevalier et al. introduced disulfide bonds into the massive design of miniprotein binders, 46 and the subsequent analysis of the massive dataset allowed to determine that designed disulfide bridges can effectively stabilize de novo binders as long as its introduction results in a…”
BackgroundInfluentialSelf-citationXenoprotein engineering via synthetic libraries
Gates, Z. P., Vinogradov, A. A., Quartararo, A. J., Bandyopadhyay, A., Choo, Z.-N., Evans, E. D., Halloran, K. H., Mijalis, A. J., Mong, S. K., Simon, M. D., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1722633115 · Cited by 54 · see citing works
“In the future, synthetic libraries may be used to empirically evaluate the success of de novo xenoprotein designs, as has recently been achieved with yeast surface display and de novo designs based on proteogenic amino acids (91, 95).”
BackgroundDisulfide-Directed Multicyclic Peptide Libraries for the Discovery of Peptide Ligands and Drugs
Lu, S., Fan, S., Xiao, S., Li, J., Zhang, S., Wu, Y., Kong, C., Zhuang, J., Liu, H., Zhao, Y., et al. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c12462 · Cited by 53 · see citing works
Fragment-based computational design of antibodies targeting structured epitopes
Aguilar Rangel, M., Bedwell, A., Costanzi, E., Taylor, R. J., Russo, R., Bernardes, G. J. L., Ricagno, S., Frydman, J., Vendruscolo, M., & Sormanni, P. (2022) · Science Advances · doi:10.1126/sciadv.abp9540 · Cited by 55 · see citing works
Addressing epistasis in the design of protein function
Lipsh‐Sokolik, R., & Fleishman, S. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314999121 · Cited by 54 · see citing works
“…accurate enough to propose stable combinatorial mutants in an active site, and successful engi - neering of functional variants demanded cycles of computa - tional and experimental screening often aided by protein crystallography and high - throughput functional screening methods (72, 127, 128).”
BackgroundMethodMolecular evolution of peptides by yeast surface display technology
Linciano, S., Pluda, S., Bacchin, A., & Angelini, A. (2019) · MedChemComm · doi:10.1039/c9md00252a · Cited by 49 · see citing works
Identifying the origin of local flexibility in a carbohydrate polymer
Anggara, K., Zhu, Y., Fittolani, G., Yu, Y., Tyrikos‐Ergas, T., Delbianco, M., Rauschenbach, S., Abb, S., Seeberger, P. H., & Kern, K. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102168118 · Cited by 49 · see citing works
“The correlation between the sequence, the higher hierarchy structures, and the resulting properties is relatively well established for polynucleotides (13, 14) and polypeptides (15, 16), while comparatively little is known for polysaccharides (17).”
Background
- Authors
- Chevalier, A., Silva, D.-A., Rocklin, G. J., Hicks, D. R., Vergara, R., Murapa, P., Bernard, S. M., Zhang, L., Lam, K.-H., Yao, G., Bahl, C. D., Miyashita, S.-I., Goreshnik, I., Fuller, J. T., Koday, M. T., Jenkins, C. M., Colvin, T., Carter, L., Bohn, A., Bryan, C. M., Fernández-Velasco, D. A., Stewart, L., Dong, M., Huang, X., Jin, R., Wilson, I. A., Fuller, D. H., & Baker, D.
- Affiliations
- University of Washington (US)
- Scripps Research Institute (US)
- Chinese Academy of Sciences (CN)
- University of California, Irvine (US)
- Boston Children's Hospital (US)
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Baker, David
- ORCID
- Chevalier, Aaron
- Silva, Daniel-Adriano
- Hicks, Derrick R.
- Vergara, Renan
- Murapa, Patience
- Bernard, Steffen M.
- Zhang, Lu
- Lam, Kwok-Ho
- Yao, Guorui
- Bahl, Christopher D.
- Miyashita, Shin-Ichiro
- Goreshnik, Inna
- Fuller, James T.
- Jenkins, Cody M.
- Carter, Lauren
- Bryan, Cassie M.
- Stewart, Lance
- Dong, Min
- Huang, Xuhui
- Jin, Rongsheng
- Wilson, Ian A.
- Fuller, Deborah H.
- Baker, David
- Venue
- Nature
- Published
- 2017-09-27
- Volume
- 550
- Issue
- 7674
- Pages
- 74-79
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5802399
- OpenAlex
- W2759860792
- Cited by
- 453
- References
- 48
- Citations by year
- 2026: 28 · 2025: 57 · 2024: 48 · 2023: 42 · 2022: 59 · 2021: 55 · 2020: 57 · 2019: 53 · 2018: 48 · 2017: 6
- FWCI
- 18.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- Funders
- National Institutes of Health (1s10od021527-01, 5t32ai007354-30)
- National Institute of General Medical Sciences (P41GM103403)
- National Institute of Allergy and Infectious Diseases (R41AI122431, R01AI091823, R56AI127371, R56AI117675, R21AI119258, R33AI119258, R21AI123920, T32-AI007354., R01AI125704)
- National Institute of Neurological Disorders and Stroke (R01NS080833)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- de novo protein design, miniproteins, protein binders, influenza haemagglutinin, botulinum neurotoxin B, oligonucleotide synthesis, protein stability, antiviral agents, prophylactic protection, antibody alternatives, immune response
- MeSH terms
- Botulinum Toxins, Computer Simulation, Hot Temperature, Humans, Influenza, Human, Protein Binding, Proteins, Temperature, Drug Design, Protein Engineering, Hemagglutinin Glycoproteins, Influenza Virus, Protein Stability, Molecular Dynamics Simulation, Molecular Targeted Therapy
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
