Protein engineering by highly parallel screening of computationally designed variants
Open accessAbstract
Current combinatorial selection strategies for protein engineering have been successful at generating binders against a range of targets; however, the combinatorial nature of the libraries and their vast undersampling of sequence space inherently limit these methods due to the difficulty in finely controlling protein properties of the engineered region. Meanwhile, great advances in computational protein design that can address these issues have largely been underutilized. We describe an integrated approach that computationally designs thousands of individual protein binders for high-throughput synthesis and selection to engineer high-affinity binders. We show that a computationally designed library enriches for tight-binding variants by many orders of magnitude as compared to conventional randomization strategies. We thus demonstrate the feasibility of our approach in a proof-of-concept study and successfully obtain low-nanomolar binders using in vitro and in vivo selection systems.
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The need for large candidate libraries reflects relatively low positive predictive value of scoring functions for predicting candidate binding.
“Most of these methods require the generation of libraries of many candidates (~millions)], adopting highly parallel screening strategies that have been developed previously [93], reflecting relatively low positive predictive value of the scoring functions to predict candidate binding.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [93]
Most classical antibody design methods require the generation of libraries of many candidates, around millions.
“Most of these methods require the generation of libraries of many candidates (~millions)], adopting highly parallel screening strategies that have been developed previously [93], reflecting relatively low positive predictive value of the scoring functions to predict candidate binding.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [93]
Most classical antibody design methods adopt highly parallel screening strategies.
“Most of these methods require the generation of libraries of many candidates (~millions)], adopting highly parallel screening strategies that have been developed previously [93], reflecting relatively low positive predictive value of the scoring functions to predict candidate binding.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [93]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · 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
WebLogo: A Sequence Logo Generator: Figure 1Preprint
Crooks, G. E., Hon, G. C., Chandonia, J., & Brenner, S. E. (2004) · Genome Research · doi:10.1101/gr.849004 · Cited by 12,433 · see citing works
GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., et al. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt055 · Cited by 7,493 · 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
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666 · see citing works
Kemp elimination catalysts by computational enzyme design
Röthlisberger, D., Khersonsky, O., Wollacott, A. M., Jiang, L., DeChancie, J., Betker, J. L., Gallaher, J. L., Althoff, E. A., Zanghellini, A., Dym, O., et al. (2008) · Nature · doi:10.1038/nature06879 · Cited by 1,299 · see citing works
De Novo Protein Design: Fully Automated Sequence Selection
Dahiyat, B. I., & Mayo, S. L. (1997) · Science · doi:10.1126/science.278.5335.82 · Cited by 1,142 · see citing works
Methods for the directed evolution of proteins
Packer, M. S., & Liu, D. R. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3927 · Cited by 1,002 · see citing works
Shapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000 · see citing works
Selecting and screening recombinant antibody libraries
Hoogenboom, H. R. (2005) · Nature Biotechnology · doi:10.1038/nbt1126 · Cited by 975 · see citing works
Large-scale de novo DNA synthesis: technologies and applications
Kosuri, S., & Church, G. M. (2014) · Nature Methods · doi:10.1038/nmeth.2918 · Cited by 869 · see citing works
Computational design of receptor and sensor proteins with novel functions
Looger, L. L., Dwyer, M. A., Smith, J. J., & Hellinga, H. W. (2003) · Nature · doi:10.1038/nature01556 · Cited by 668 · 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
SciNet: Lessons Learned from Building a Power-efficient Top-20 System and Data Centre
Loken, C., Gruner, D., Groer, L., Peltier, R., Bunn, N., Craig, M., Henriques, T., Dempsey, J., Yu, C.-H., Chen, J., et al. (2010) · Journal of Physics Conference Series · doi:10.1088/1742-6596/256/1/012026 · Cited by 493 · see citing works
Phage display: Concept, innovations, applications and future
Pande, J., Szewczyk, M. M., & Grover, A. K. (2010) · Biotechnology Advances · doi:10.1016/j.biotechadv.2010.07.004 · Cited by 492 · see citing works
Badis, G., Chan, E. T., van Bakel, H., Peña‐Castillo, L., Tillo, D., Tsui, K., Carlson, C. D., Gossett, A. J., Hasinoff, M. J., Warren, C. L., et al. (2008) · Molecular Cell · doi:10.1016/j.molcel.2008.11.020 · Cited by 472 · see citing works
Protein Design by Directed Evolution
Jäckel, C., Kast, P., & Hilvert, D. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.032807.125832 · Cited by 410 · see citing works
[21] Phage display for selection of novel binding peptides
Sidhu, S. S., Lowman, H. B., Cunningham, B. C., & Wells, J. A. (2000) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(00)28406-1 · Cited by 403 · see citing works
Large-scale high-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method
Gietz, R. D., & Schiestl, R. H. (2007) · Nature Protocols · doi:10.1038/nprot.2007.15 · Cited by 393 · see citing works
Computational design of antibody-affinity improvement beyond in vivo maturation
Lippow, S. M., Wittrup, K. D., & Tidor, B. (2007) · Nature Biotechnology · doi:10.1038/nbt1336 · Cited by 342 · see citing works
Smith, C. A., & Kortemme, T. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.05.023 · Cited by 314 · see citing works
A Strategy for Modulation of Enzymes in the Ubiquitin System
Ernst, A., Avvakumov, G. V., Tong, J., Fan, Y., Zhao, Y., Alberts, P., Persaud, A. K., Walker, J. R., Neculai, A.-M., Neculai, D., et al. (2013) · Science · doi:10.1126/science.1230161 · Cited by 285 · see citing works
Prediction of protein conformational freedom from distance constraints
de Groot, B. L., van Aalten, D. M. F., Scheek, R. M., Amadei, A., Vriend, G., & Berendsen, H. J. C. (1997) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(199710)29:2 · Cited by 269 · see citing works
Stranges, P. B., & Kuhlman, B. A. (2012) · Protein Science · doi:10.1002/pro.2187 · Cited by 233 · see citing works
Tonikian, R., Zhang, Y., Boone, C. M., & Sidhu, S. S. (2007) · Nature Protocols · doi:10.1038/nprot.2007.151 · Cited by 202 · see citing works
Ivarsson, Y., Arnold, R., McLaughlin, M., Nim, S., Joshi, R., Ray, D., Liu, B., Teyra, J., Pawson, T., Moffat, J., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312296111 · Cited by 135 · see citing works
Self-citationComputational design of affinity and specificity at protein–protein interfaces
Karanicolas, J., & Kuhlman, B. A. (2009) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2009.07.005 · Cited by 126 · see citing works
The FALC-Loop web server for protein loop modeling
Ko, J., Lee, D., Park, H., Coutsias, E. A., Lee, J., & Seok, C. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr352 · Cited by 103 · see citing works
Conformational stabilization of ubiquitin yields potent and selective inhibitors of USP7
Zhang, Y., Zhou, L., Rougé, L., Phillips, A. H., Lam, C., Liu, P., Sandoval, W. N., Helgason, E., Murray, J. M., Wertz, I. E., et al. (2012) · Nature Chemical Biology · doi:10.1038/nchembio.1134 · Cited by 102 · see citing works
Treynor, T. P., Vizcarra, C. L., Nedelcu, D., & Mayo, S. L. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609647103 · Cited by 93 · see citing works
Smith, C. A., & Kortemme, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020451 · Cited by 92 · see citing works
Computational design of a pH-sensitive IgG binding protein
Strauch, E., Fleishman, S. J., & Baker, D. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1313605111 · Cited by 91 · see citing works
Hallen, M. A., Keedy, D. A., & Donald, B. R. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24150 · Cited by 85 · see citing works
Haidar, J. N. S., Pierce, B. G., Yu, Y., Tong, W., Li, M. Y., & Weng, Z. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22203 · Cited by 72 · see citing works
Engineering a protein–protein interface using a computationally designed library
Guntas, G., Purbeck, C., & Kuhlman, B. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006528107 · Cited by 67 · see citing works
McLaughlin, M. E., & Sidhu, S. S. (2013) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-394292-0.00015-1 · Cited by 31 · see citing works
Dwyer, J. J., Dwyer, M. A., & Kossiakoff, A. A. (2001) · Biochemistry · doi:10.1021/bi011703b · Cited by 26 · see citing works
Optimizing energy functions for protein–protein interface design
Sharabi, O., Yanover, C., Dekel, A., & Shifman, J. M. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21594 · Cited by 18 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [5]S. S. Sidhu, H. B. Lowman, B. C. Cunningham, J. A. Wells, in Applications of Chimeric Genes and Hybrid Proteins—Part C: Protein-Protein Interactions and Genomics, S. D. Emr, J. N. Abelson, J. Thorner, Eds. (Academic Press, Cambridge, MA, 2000), p. 333.
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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
Massively parallel de novo protein design for targeted therapeutics
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., et al. (2017) · Nature · doi:10.1038/nature23912 · Cited by 453 · see citing works
Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
Self-citation
- Authors
- Sun, M. G. F., Seo, M.-H., Nim, S., Corbi-Verge, C., & Kim, P. M.
- Affiliations
- University of Toronto (CA)
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
- Corresponding author
- Sun, Mark G. F.
- Seo, Moon-Hyeong
- Kim, Philip M.
- Venue
- Science Advances
- Published
- 2016-07
- Volume
- 2
- Issue
- 7
- Pages
- e1600692-e1600692
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC4956399
- OpenAlex
- W2476649705
- Cited by
- 41
- References
- 39
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 5 · 2023: 8 · 2022: 2 · 2021: 6 · 2020: 2 · 2019: 2 · 2018: 4 · 2017: 6 · 2016: 1
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Science Advances (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research (ID0EH1AG8772, MOP-123526)
- 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
- protein engineering, computational protein design, combinatorial protein libraries, high-throughput selection, protein binders, affinity maturation, parallel screening, in vitro selection, in vivo selection, high-affinity binders
- MeSH terms
- Amino Acid Sequence, Calorimetry, DNA, Humans, Models, Molecular, Protein Binding, Recombinant Proteins, Protein Engineering, Sequence Analysis, DNA, Protein Structure, Tertiary, Peptide Library, Principal Component Analysis, Ubiquitin, Ubiquitin Thiolesterase
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