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A high-throughput platform for efficient exploration of functional polypeptide chemical space

Nature Synthesis volume 2, pages 515–526 (2023)Cite this article

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Abstract

Rapid and in-depth exploration of the chemical space of high-molecular-weight synthetic polypeptides via ring-opening polymerization of N-carboxyanhydride allows the discovery of protein mimics and functional biomaterials. The traditional synthetic workflow, however, is labour intensive and has limited throughput. Here we develop an approach for the high-throughput diversification of polypeptides based on a click-like reaction between selenolate and various electrophiles in aqueous solutions. Importantly, the platform is amenable to automation, which allows rapid generation of up to 1,200 homopolypeptides or random heteropolypeptides (RHPs) within one day. With the assistance of machine learning, iterative exploration of the RHP library identifies candidates with improved glutathione peroxidase-like activity from the complex chemical space of which we have little previous knowledge. This automated and high-throughput platform provides potential solutions to unmet challenges, such as the de novo design of artificial enzymes, biomacromolecule delivery and understanding of intrinsically disordered proteins.

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Fig. 1: PPM of polypeptides through highly efficient selenium chemistry.
Fig. 2: Synthesis of the precursor polypeptide PSeO2Na.
Fig. 3: Representative characterizations of homopolypeptides prepared by PPM of PSeO2Na.
Fig. 4: Control of the molecular composition of RHPs in binary organohalide systems.
Fig. 5: Closed-loop optimization of GPx activity of the RHPs via HTS and machine learning.

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Data availability

All of the data generated or analysed in this study are included in this published article and its Supplementary Information files. Source data are provided with this paper.

Code availability

The code for the generation of random RHPs and Bayesian optimization in this study is included in Supplementary Code 1.

References

  1. Cole, J. P., Hanlon, A. M., Rodriguez, K. J. & Berda, E. B. Protein-like structure and activity in synthetic polymers. J. Polym. Sci. A Polym. Chem. 55, 191–206 (2017).

    CAS  Google Scholar 

  2. Rothfuss, H., Knofel, N. D., Roesky, P. W. & Barner-Kowollik, C. Single-chain nanoparticles as catalytic nanoreactors. J. Am. Chem. Soc. 140, 5875–5881 (2018).

    CAS  PubMed  Google Scholar 

  3. Bonduelle, C. Secondary structures of synthetic polypeptide polymers. Polym. Chem. 9, 1517–1529 (2018).

    CAS  Google Scholar 

  4. Varanko, A. K., Su, J. C. & Chilkoti, A. Elastin-like polypeptides for biomedical applications. Annu. Rev. Biomed. Eng. 22, 343–369 (2020).

    CAS  PubMed  Google Scholar 

  5. Callmann, C. E., Thompson, M. P. & Gianneschi, N. C. Poly(peptide): synthesis, structure, and function of peptide–polymer amphiphiles and protein-like polymers. Acc. Chem. Res. 53, 400–413 (2020).

    CAS  PubMed  Google Scholar 

  6. Jiang, T. et al. Single-chain heteropolymers transport protons selectively and rapidly. Nature 577, 216–220 (2020).

    CAS  PubMed  Google Scholar 

  7. Panganiban, B. et al. Random heteropolymers preserve protein function in foreign environments. Science 359, 1239–1243 (2018).

    CAS  PubMed  Google Scholar 

  8. Hilburg, S. L., Ruan, Z. Y., Xu, T. & Alexander-Katz, A. Behavior of protein-inspired synthetic random heteropolymers. Macromolecules 53, 9187–9199 (2020).

    CAS  Google Scholar 

  9. Han, Z., Hilburg, S. L. & Alexander-Katz, A. Forced unfolding of protein-inspired single-chain random heteropolymers. Macromolecules 55, 1295–1309 (2022).

    CAS  Google Scholar 

  10. Song, Z. Y., Tan, Z. Z. & Cheng, J. J. Recent advances and future perspectives of synthetic polypeptides from N-carboxyanhydrides. Macromolecules 52, 8521–8539 (2019).

    CAS  Google Scholar 

  11. Song, Z. Y. et al. Synthetic polypeptides: from polymer design to supramolecular assembly and biomedical application. Chem. Soc. Rev. 46, 6570–6599 (2017).

    CAS  PubMed  Google Scholar 

  12. Zhou, X. F. & Li, Z. B. Advances and biomedical applications of polypeptide hydrogels derived from α-amino acid N-carboxyanhydride (NCA) polymerizations. Adv. Healthcare Mater. 7, e1800020 (2018).

    Google Scholar 

  13. Deng, C. et al. Functional polypeptide and hybrid materials: precision synthesis via α-amino acid N-carboxyanhydride polymerization and emerging biomedical applications. Prog. Polym. Sci. 39, 330–364 (2014).

    CAS  Google Scholar 

  14. Hou, Y. Q. & Lu, H. Protein PEPylation: a new paradigm of protein–polymer conjugation. Bioconjugate Chem. 30, 1604–1616 (2019).

    CAS  Google Scholar 

  15. Deming, T. J. Synthetic polypeptides for biomedical applications. Prog. Polym. Sci. 32, 858–875 (2007).

    CAS  Google Scholar 

  16. Liu, Y., Li, D., Ding, J. X. & Chen, X. S. Controlled synthesis of polypeptides. Chin. Chem. Lett. 31, 3001–3014 (2020).

    CAS  Google Scholar 

  17. Ruggieri, M., Avolio, C., Livrea, P. & Trojano, M. Glatiramer acetate in multiple sclerosis: a review. CNS Drug Rev. 13, 178–191 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Taylor, S. V., Walter, K. U., Kast, P. & Hilvert, D. Searching sequence space for protein catalysts. Proc. Natl Acad. Sci. USA 98, 10596–10601 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Reis, M. et al. Machine-learning-guided discovery of F-19 MRI agents enabled by automated copolymer synthesis. J. Am. Chem. Soc. 143, 17677–17689 (2021).

    CAS  PubMed  Google Scholar 

  20. Macarron, R. et al. Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Discov. 10, 188–195 (2011).

    CAS  PubMed  Google Scholar 

  21. Coley, C. W., Eyke, N. S. & Jensen, K. F. Autonomous discovery in the chemical sciences part I: progress. Angew. Chem. Int. Ed. 59, 22858–22893 (2020).

    CAS  Google Scholar 

  22. Yang, L. L. et al. High-throughput methods in the discovery and study of biomaterials and materiobiology. Chem. Rev. 121, 4561–4677 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Soheilmoghaddam, F., Rumble, M. & Cooper-White, J. High-throughput routes to biomaterials discovery. Chem. Rev. 121, 10792–10864 (2021).

    CAS  PubMed  Google Scholar 

  24. DeBenedictis, E. A. et al. Systematic molecular evolution enables robust biomolecule discovery. Nat. Methods 19, 55–64 (2022).

    CAS  PubMed  Google Scholar 

  25. Gromski, P. S., Granda, J. M. & Cronin, L. Universal chemical synthesis and discovery with ‘the Chemputer’. Trends Chem. 2, 4–12 (2020).

    CAS  Google Scholar 

  26. Pollice, R. et al. Data-driven strategies for accelerated materials design. Acc. Chem. Res. 54, 849–860 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Correa-Baena, J. P. et al. Accelerating materials development via automation, machine learning, and high-performance computing. Joule 2, 1410–1420 (2018).

    CAS  Google Scholar 

  28. Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).

    CAS  PubMed  Google Scholar 

  29. Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18, 463–477 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Kumar, R. et al. Efficient polymer-mediated delivery of gene-editing ribonucleoprotein payloads through combinatorial design, parallelized experimentation, and machine learning. ACS Nano. 14, 17626–17639 (2020).

    CAS  PubMed  Google Scholar 

  31. Kumar, R., Le, N., Oviedo, F., Brown, M. E. & Reineke, T. M. Combinatorial polycation synthesis and causal machine learning reveal divergent polymer design rules for effective pDNA and ribonucleoprotein delivery. JACS Au 2, 428–442 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Goldberg, M., Mahon, K. & Anderson, D. Combinatorial and rational approaches to polymer synthesis for medicine. Adv. Drug. Deliv. Rev. 60, 971–978 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Baudis, S. & Behl, M. High-throughput and combinatorial approaches for the development of multifunctional polymers. Macromol. Rapid Commun. 43, 2100400 (2022).

    CAS  Google Scholar 

  34. Holmes, P. F., Bohrer, M. & Kohn, J. Exploration of polymethacrylate structure–property correlations: advances towards combinatorial and high-throughput methods for biomaterials discovery. Prog. Polym. Sci. 33, 787–796 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Gormley, A. J. & Webb, M. A. Machine learning in combinatorial polymer chemistry. Nat. Rev. Mater. 6, 642–644 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Upadhya, R. et al. Automation and data-driven design of polymer therapeutics. Adv. Drug. Deliv. Rev. 171, 1–28 (2021).

    CAS  PubMed  Google Scholar 

  37. Patel, R. A., Borca, C. H. & Webb, M. A. Featurization strategies for polymer sequence or composition design by machine learning. Mol. Syst. Des. Eng. 7, 661–676 (2022).

    CAS  Google Scholar 

  38. Oliver, S., Zhao, L., Gormley, A. J., Chapman, R. & Boyer, C. Living in the fast lane high throughput controlled/living radical polymerization. Macromolecules 52, 3–23 (2019).

    CAS  Google Scholar 

  39. Lynn, D. M., Anderson, D. G., Putnam, D. & Langer, R. Accelerated discovery of synthetic transfection vectors: parallel synthesis and screening of a degradable polymer library. J. Am. Chem. Soc. 123, 8155–8156 (2001).

    CAS  PubMed  Google Scholar 

  40. Green, J. J., Langer, R. & Anderson, D. G. A combinatorial polymer library approach yields insight into nonviral gene delivery. Acc. Chem. Res. 41, 749–759 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Gormley, A. J. et al. An oxygen-tolerant PET–RAFT polymerization for screening structure–activity relationships. Angew. Chem. Int. Ed. 57, 1557–1562 (2018).

    CAS  Google Scholar 

  42. Judzewitsch, P. R. et al. High-throughput process for the discovery of antimicrobial polymers and their upscaled production via flow polymerization. Macromolecules 53, 631–639 (2020).

    CAS  Google Scholar 

  43. Kosuri, S. et al. Machine-assisted discovery of chondroitinase ABC complexes toward sustained neural regeneration. Adv. Healthcare Mater. 11, 2102101 (2022).

    CAS  Google Scholar 

  44. Tamasi, M. J. et al. Machine learning on a robotic platform for the design of polymer–protein hybrids. Adv. Mater. 34, 2201809 (2022).

    CAS  Google Scholar 

  45. Gauthier, M. A., Gibson, M. I. & Klok, H. A. Synthesis of functional polymers by post-polymerization modification. Angew. Chem. Int. Ed. 48, 48–58 (2009).

    CAS  Google Scholar 

  46. Gunay, K. A., Theato, P. & Klok, H. A. Standing on the shoulders of Hermann Staudinger: post-polymerization modification from past to present. J. Polym. Sci. A Polym. Chem. 51, 1–28 (2013).

    Google Scholar 

  47. Zhong, Y. B., Zeberl, B. J., Wang, X. & Luo, J. T. Combinatorial approaches in post-polymerization modification for rational development of therapeutic delivery systems. Acta Biomater. 73, 21–37 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Ladmiral, V. et al. Synthesis of neoglycopolymers by a combination of “click chemistry” and living radical polymerization. J. Am. Chem. Soc. 128, 4823–4830 (2006).

    CAS  PubMed  Google Scholar 

  49. Wong, S. Y., Sood, N. & Putnam, D. Combinatorial evaluation of cations, pH-sensitive and hydrophobic moieties for polymeric vector design. Mol. Ther. 17, 480–490 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Pedone, E., Li, X. W., Koseva, N., Alpar, O. & Brocchini, S. An information rich biomedical polymer library. J. Mater. Chem. 13, 2825–2837 (2003).

    CAS  Google Scholar 

  51. Yan, Y. F. et al. Functional polyesters enable selective siRNA delivery to lung cancer over matched normal cells. Proc. Natl. Acad. Sci. USA 113, E5702–E5710 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Wyrsta, M. D., Cogen, A. L. & Deming, T. J. A parallel synthetic approach for the analysis of membrane interactive copolypeptides. J. Am. Chem. Soc. 123, 12919–12920 (2001).

    CAS  PubMed  Google Scholar 

  53. Deming, T. J. Synthesis of side-chain modified polypeptides. Chem. Rev. 116, 786–808 (2016).

    CAS  PubMed  Google Scholar 

  54. Deming, T. J. Functional modification of thioether groups in peptides, polypeptides, and proteins. Bioconjugate Chem. 28, 691–700 (2017).

    CAS  Google Scholar 

  55. Lu, H. et al. Ring-opening polymerization of γ-(4-vinylbenzyl)-l-glutamate N-carboxyanhydride for the synthesis of functional polypeptides. Macromolecules 44, 6237–6240 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhou, J. R. et al. A simple and versatile synthetic strategy to functional polypeptides via vinyl sulfone-substituted l-cysteine N-carboxyanhydride. Macromolecules 46, 6723–6730 (2013).

    CAS  Google Scholar 

  57. Engler, A. C., Lee, H. I. & Hammond, P. T. Highly efficient “grafting onto” a polypeptide backbone using click chemistry. Angew. Chem. Int. Ed. 48, 9334–9338 (2009).

    CAS  Google Scholar 

  58. Krannig, K. S. & Schlaad, H. pH-responsive bioactive glycopolypeptides with enhanced helicity and solubility in aqueous solution. J. Am. Chem. Soc. 134, 18542–18545 (2012).

    CAS  PubMed  Google Scholar 

  59. Cao, J. B. et al. Non-ionic water-soluble “clickable” α-helical polypeptides: synthesis, characterization and side chain modification. Polym. Chem. 6, 1226–1229 (2015).

    CAS  Google Scholar 

  60. Xie, Y., Lopez-Silva, T. L. & Schneider, J. P. Hydrophilic azide-containing amino acid to enhance the solubility of peptides for SPAAC reactions. Org. Lett. 24, 7378–7382 (2022).

    CAS  PubMed  Google Scholar 

  61. Pickens, C. J., Johnson, S. N., Pressnall, M. M., Leon, M. A. & Berkland, C. J. Practical considerations, challenges, and limitations of bioconjugation via azide–alkyne cycloaddition. Bioconjugate Chem. 29, 686–701 (2018).

    CAS  Google Scholar 

  62. Liu, J., Chen, Q. Q. & Rozovsky, S. Utilizing selenocysteine for expressed protein ligation and bioconjugations. J. Am. Chem. Soc. 139, 3430–3437 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhao, Z. G., Shimon, D. & Metanis, N. Chemoselective copper-mediated modification of selenocysteines in peptides and proteins. J. Am. Chem. Soc. 143, 12817–12824 (2021).

    CAS  PubMed  Google Scholar 

  64. Quaderer, R., Sewing, A. & Hilvert, D. Selenocysteine-mediated native chemical ligation. Helv. Chim. Acta 84, 1197–1206 (2001).

    CAS  Google Scholar 

  65. Li, X. et al. Stable and potent selenomab–drug conjugates. Cell Chem. Biol. 24, 433–442 e436 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Li, X. L. et al. Site-specific dual antibody conjugation via engineered cysteine and selenocysteine residues. Bioconjugate Chem. 26, 2243–2248 (2015).

    CAS  Google Scholar 

  67. Sayers, J. et al. Construction of challenging proline–proline junctions via diselenide–selenoester ligation chemistry. J. Am. Chem. Soc. 140, 13327–13334 (2018).

    CAS  PubMed  Google Scholar 

  68. Flemer, S. Jr. Selenol protecting groups in organic chemistry: special emphasis on selenocysteine Se-protection in solid phase peptide synthesis. Molecules. 16, 3232–3251 (2011).

  69. Sharpless, K. B., Lauer, R. F. & Teranishi, A. Y. Electrophilic and nucleophilic organoselenium reagents. New routes to α,β-unsaturated carbonyl compounds. J. Am. Chem. Soc. 95, 6137–6139 (1973).

    CAS  Google Scholar 

  70. Wu, J. A. et al. The functionalization of poly(ε-caprolactone) as a versatile platform using ε-(α-phenylseleno) caprolactone as a monomer. Polym. Chem. 10, 3851–3858 (2019).

    CAS  Google Scholar 

  71. Yu, L., Zhang, M., Du, F. S. & Li, Z. C. ROS-responsive poly(ε-caprolactone) with pendent thioether and selenide motifs. Polym. Chem. 9, 3762–3773 (2018).

    CAS  Google Scholar 

  72. Wang, L. et al. ROS-triggered degradation of selenide-containing polymers based on selenoxide elimination. Polym. Chem. 10, 2039–2046 (2019).

    CAS  Google Scholar 

  73. Reich, H. J., Wollowitz, S., Trend, J. E., Chow, F. & Wendelborn, D. F. Syn elimination of alkyl selenoxides. Side reactions involving selenenic acids. Structural and solvent effects on rates. J. Org. Chem. 43, 1697–1705 (1978).

    CAS  Google Scholar 

  74. Yang, Y. et al. Genetically encoded releasable photo-cross-linking strategies for studying protein–protein interactions in living cells. Nat. Protoc. 12, 2147–2168 (2017).

    CAS  PubMed  Google Scholar 

  75. Yang, Y. et al. Genetically encoded protein photocrosslinker with a transferable mass spectrometry-identifiable label. Nat. Commun. 7, 12299 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Lin, S. et al. Genetically encoded cleavable protein photo-cross-linker. J. Am. Chem. Soc. 136, 11860–11863 (2014).

    CAS  PubMed  Google Scholar 

  77. Tian, Z. Y., Zhang, Z. C., Wang, S. & Lu, H. A moisture-tolerant route to unprotected α/β-amino acid N-carboxyanhydrides and facile synthesis of hyperbranched polypeptides. Nat. Commun. 12, 5810 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Wu, G. et al. Synthesis of water soluble and multi-responsive selenopolypeptides via ring-opening polymerization of N-carboxyanhydrides. Chem. Commun. 55, 7860–7863 (2019).

    CAS  Google Scholar 

  79. Lin, Y. Y. A. et al. Rapid cross-metathesis for reversible protein modifications via chemical access to Se-allyl-selenocysteine in proteins. J. Am. Chem. Soc. 135, 12156–12159 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Reddy, K. M. & Mugesh, G. Application of dehydroalanine as a building block for the synthesis of selenocysteine-containing peptides. RSC Adv. 9, 34–43 (2019).

    CAS  Google Scholar 

  81. Banik, S. M. et al. Lysosome-targeting chimaeras for degradation of extracellular proteins. Nature 584, 291–297 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Ahn, G. et al. LYTACs that engage the asialoglycoprotein receptor for targeted protein degradation. Nat. Chem. Biol. 17, 937–946 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Delaveris, C. S., Chiu, S. H., Riley, N. M. & Bertozzi, C. R. Modulation of immune cell reactivity with cis-binding Siglec agonists. Proc. Natl Acad. Sci. USA 118, e2012408118 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Du, J. J. et al. Glycopeptide ligation via direct aminolysis of selenoester. Chin. Chem. Lett. 29, 1127–1130 (2018).

    CAS  Google Scholar 

  85. Temperini, A., Piazzolla, F., Minuti, L., Curini, M. & Siciliano, C. General, mild, and metal-free synthesis of phenyl selenoesters from anhydrides and their use in peptide synthesis. J. Org. Chem. 82, 4588–4603 (2017).

    CAS  PubMed  Google Scholar 

  86. Chen, L. H. et al. Polymer informatics: current status and critical next steps. Mater. Sci. Eng. R Rep. 144, 100595 (2021).

    Google Scholar 

  87. Upadhya, R., Kanagala, M. J. & Gormley, A. J. Purifying low-volume combinatorial polymer libraries with gel filtration columns. Macromol. Rapid Commun. 40, 1900528 (2019).

    CAS  Google Scholar 

  88. Barbosa, N. V. et al. Organoselenium compounds as mimics of selenoproteins and thiol modifier agents. Metallomics 9, 1703–1734 (2017).

    CAS  PubMed  Google Scholar 

  89. Huang, X., Liu, X. M., Luo, Q. A., Liu, J. Q. & Shen, J. C. Artificial selenoenzymes: designed and redesigned. Chem. Soc. Rev. 40, 1171–1184 (2011).

    CAS  PubMed  Google Scholar 

  90. Seibt, T. M., Proneth, B. & Conrad, M. Role of GPX4 in ferroptosis and its pharmacological implication. Free Radic. Biol. Med. 133, 144–152 (2019).

    CAS  PubMed  Google Scholar 

  91. Yant, L. J. et al. The selenoprotein GPX4 is essential for mouse development and protects from radiation and oxidative damage insults. Free Radic. Biol. Med. 34, 496–502 (2003).

    CAS  PubMed  Google Scholar 

  92. Xu, C. X. et al. The glutathione peroxidase Gpx4 prevents lipid peroxidation and ferroptosis to sustain Treg cell activation and suppression of antitumor immunity.Cell Rep. 35, 109235 (2021).

    CAS  PubMed  Google Scholar 

  93. Parnham, M. & Sies, H. Ebselen: prospective therapy for cerebral ischaemia. Expert Opin. Investig. Drugs 9, 607–619 (2000).

    CAS  PubMed  Google Scholar 

  94. Landgraf, A. D. et al. Neuroprotective and anti-neuroinflammatory properties of ebselen derivatives and their potential to inhibit neurodegeneration. ACS Chem. Neurosci. 11, 3008–3016 (2020).

    CAS  PubMed  Google Scholar 

  95. Yamagata, K., Ichinose, S., Miyashita, A. & Tagami, M. Protective effects of ebselen, a seleno-organic antioxidant on neurodegeneration induced by hypoxia and reperfusion in stroke-prone spontaneously hypertensive rat. Neuroscience. 153, 428–435 (2008).

    CAS  PubMed  Google Scholar 

  96. Paglia, D. E. & Valentine, W. N. Studies on the quantitative and qualitative characterization of erythrocyte glutathione peroxidase. J. Lab. Clin. Med. 70, 158–169 (1967).

    CAS  PubMed  Google Scholar 

  97. Shahriari, B., Swersky, K., Wang, Z. Y., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016).

    Google Scholar 

  98. Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).

    CAS  PubMed  Google Scholar 

  99. Nambiar, A. M. K. et al. Bayesian optimization of computer-proposed multistep synthetic routes on an automated robotic flow platform. ACS Cent. Sci. 8, 825–836 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Hase, F., Roch, L. M., Kreisbeck, C. & Aspuru-Guzik, A. Phoenics: a Bayesian optimizer for chemistry. ACS Cent. Sci. 4, 1134–1145 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Balandat, M. et al. BoTorch: a framework for efficient Monte-Carlo Bayesian optimization. Adv. Neural Inf. Process. Syst. 33, 21524–21538 (2020).

    Google Scholar 

  102. Shao, L. X., Li, Y. M., Lu, J. M. & Jiang, X. F. Recent progress in selenium-catalyzed organic reactions. Org. Chem. Front. 6, 2999–3041 (2019).

    Google Scholar 

  103. Reich, H. J. & Hondal, R. J. Why nature chose selenium. ACS Chem. Biol. 11, 821–841 (2016).

    CAS  PubMed  Google Scholar 

  104. Xia, J. H., Li, T. Y., Lu, C. J. & Xu, H. P. Selenium-containing polymers: perspectives toward diverse applications in both adaptive and biomedical materials. Macromolecules 51, 7435–7455 (2018).

    CAS  Google Scholar 

  105. Li, Q. L. et al. Organoselenium chemistry-based polymer synthesis. Org. Chem. Front. 7, 2815–2841 (2020).

    CAS  Google Scholar 

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2019YFA0904203 to H.L.), National Natural Science Foundation of China (22125101 and 21975004 to H.L. and 22105008 to G.W.) and Beijing Natural Science Foundation (2220023 to H.L.), a fellowship of the China Postdoctoral Science Foundation (2020M680192 to G.W.) and the Li Ge-Zhao Ning Life Science Youth Research Foundation (LGZNQN202206 to H.L.). G.W. thanks the Boya Postdoctoral Fellowship of Peking University for financial support. G.W. thanks S. Goldman (Department of Chemical Engineering, Massachusetts Institute of Technology) and Z. Wang (Department of Industrial Engineering, Tsinghua University) for the discussion and R. Mercado (Department of Chemical Engineering, Massachusetts Institute of Technology) for examining the code. Figure 5a and graphical abstract created with BioRender.com.

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Authors and Affiliations

  1. Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, People’s Republic of China

    Guangqi Wu, Haisen Zhou, Zi-You Tian, Xingyi Liu, Shuo Wang & Hua Lu

  2. Key Laboratory of Polymer Chemistry and Physics, Ministry of Education, Peking University, Beijing, People’s Republic of China

    Guangqi Wu, Haisen Zhou, Zi-You Tian, Xingyi Liu, Shuo Wang & Hua Lu

  3. Center for Soft Matter Science and Engineering, Peking University, Beijing, People’s Republic of China

    Guangqi Wu, Haisen Zhou, Zi-You Tian, Xingyi Liu, Shuo Wang & Hua Lu

  4. Changping Laboratory, Beijing, People’s Republic of China

    Jun Zhang

  5. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Connor W. Coley

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  1. Guangqi Wu
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Contributions

G.W. and H.L. designed and directed the research. G.W., Z.-Y.T., X.L. and S.W. performed the synthesis of monomers and the precursor polypeptide. G.W. and H.Z. built and validated the HTS and characterization platform and performed the screening of GPx mimics. G.W. and J.Z. wrote the code of Bayesian optimization. G.W., C.W.C. and H.L. wrote the original draft. All authors reviewed and accepted the manuscript.

Corresponding author

Correspondence to Hua Lu.

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The authors declare no competing interests.

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Peer review information

Nature Synthesis thanks Adam Gormley, Michael Webb and Huaping Xu for their contribution to the peer review of this work. Primary Handling Editor: Peter Seavill, in collaboration with the Nature Synthesis team.

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Supplementary information

Supplementary Information

Experimental details and Supplementary Figs. 1–68 and Tables 1–4.

Supplementary Code 1

Source code (BO_GPx_Ax022.ipynb) for Bayesian optimization with the example input and output files, as well as Python code for the generation of random candidates in each round.

Supplementary Data 1

Experimental results of HTS with labels.

Source data

Source Data Fig. 2

Unprocessed picture of gram-scale synthesis.

Source Data Fig. 2

Source data for the SEC trace of Fig. 2c and the 77Se NMR spectrum in Fig. 2e.

Source Data Fig. 3

Source data for the 1H NMR spectrum of Fig. 3a, the gel permeation chromatography trace of Fig. 3b and the circular dichroism spectra of Fig. 3c.

Source Data Fig. 4

Source data for Fig. 4a–c.

Source Data Fig. 5

Source data for Fig. 5c–f.

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Wu, G., Zhou, H., Zhang, J. et al. A high-throughput platform for efficient exploration of functional polypeptide chemical space. Nat. Synth 2, 515–526 (2023). https://doi.org/10.1038/s44160-023-00294-7

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