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Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation

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Abstract

Insulin has been commonly adopted as a peptide drug to treat diabetes as it facilitates the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin’s proteolytic stability and reduce its dimerization propensity, but understanding such phenomena at the molecular level is still difficult. To address this challenge, we proposed and tested several structural determinants that could potentially influence insulin’s proteolytic stability and dimerization propensity. We used these metrics to assess the properties of interest from \(10\ \mu \hbox {s}\) aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, respectively, as was in part the solvent-accessible surface area of proteolytic sites. However, other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation influences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms.

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Data availability

As discussed in Section Analysis techniques, most values reported in our results are averaged over different wild-type crystal structure models. For individual analysis result of each insulin glycoform, please refer to our GitHub repository of the project. The repository also contains input configurations/MD parameters and Python codes for data analysis. The outputs of the MD trajectories are too large to release as they are several terabytes in size and statistically representative outputs can be generated from the input files.

References

  1. Carino GP, Mathiowitz E (1999) Oral insulin delivery. Adv Drug Deliv Rev 35(2–3):249–257

    Article  CAS  PubMed  Google Scholar 

  2. Fonte P, Araújo F, Reis S, Sarmento B (2013) Oral insulin delivery: how far are we? J Diabetes Sci Technol 7(2):520–531

    Article  PubMed  PubMed Central  Google Scholar 

  3. Gedawy A, Martinez J, Al-Salami H, Dass CR (2018) Oral insulin delivery: existing barriers and current counter-strategies. J Pharm Pharmacol 70(2):197–213

    Article  CAS  PubMed  Google Scholar 

  4. Hoffman A, Ziv E (1997) Pharmacokinetic considerations of new insulin formulations and routes of administration. Clin Pharmacokinet 33(4):285–301

    Article  CAS  PubMed  Google Scholar 

  5. Owens DR (2002) New horizons–alternative routes for insulin therapy. Nat Rev Drug Discovery 1(7):529–540

    Article  CAS  PubMed  Google Scholar 

  6. Bruno BJ, Miller GD, Lim CS (2013) Basics and recent advances in peptide and protein drug delivery. Ther Deliv 4(11):1443–1467

    Article  CAS  PubMed  Google Scholar 

  7. Hinds KD, Kim SW (2002) Effects of PEG conjugation on insulin properties. Adv Drug Deliv Rev 54(4):505–530

    Article  CAS  PubMed  Google Scholar 

  8. Clement S, Still JG, Kosutic G, McAllister R (2002) Oral insulin product hexyl-insulin monoconjugate 2 (HIM2) in type 1 diabetes mellitus: the glucose stabilization effects of HIM2. Diabetes Technol Ther 4(4):459–466

    Article  PubMed  CAS  Google Scholar 

  9. Deng W, Xie Q, Wang H, Ma Z, Wu B, Zhang X (2017) Selenium nanoparticles as versatile carriers for oral delivery of insulin: insight into the synergic antidiabetic effect and mechanism. Nanomed Nanotechnol Biol Med 13(6):1965–1974

    Article  CAS  Google Scholar 

  10. Bhattacharyya A, Mukherjee D, Mishra R, Kundu P (2017) Preparation of polyurethane-alginate/chitosan core shell nanoparticles for the purpose of oral insulin delivery. Eur Polym J 92:294–313

    Article  CAS  Google Scholar 

  11. Zhou Y, Liu L, Cao Y, Yu S, He C, Chen X (2020) A Nanocomposite vehicle based on metal-organic framework nanoparticle incorporated biodegradable microspheres for enhanced oral insulin delivery. ACS Appl Mater Interfaces 12(20):22581–22592

    Article  CAS  PubMed  Google Scholar 

  12. Agarwal V, Reddy IK, Khan MA (2000) Oral delivery of proteins: Effect of chicken and duck ovomucoid on the stability of insulin in the presence of \(\alpha \)-chymotrypsin and trypsin. Pharm Pharmacol Commun 6(5):223–227

    Article  CAS  Google Scholar 

  13. Kayser V, Chennamsetty N, Voynov V, Forrer K, Helk B, Trout BL (2011) Glycosylation influences on the aggregation propensity of therapeutic monoclonal antibodies. Biotechnol J 6(1):38–44

    Article  CAS  PubMed  Google Scholar 

  14. Raju TS, Scallon BJ (2006) Glycosylation in the Fc domain of IgG increases resistance to proteolytic cleavage by papain. Biochem Biophys Res Commun 341(3):797–803

    Article  CAS  PubMed  Google Scholar 

  15. Russell D, Oldham NJ, Davis BG (2009) Site-selective chemical protein glycosylation protects from autolysis and proteolytic degradation. Carbohyd Res 344(12):1508–1514

    Article  CAS  Google Scholar 

  16. Losev Y, Paul A, Frenkel-Pinter M, Abu-Hussein M, Khalaila I, Gazit E et al (2019) Novel model of secreted human tau protein reveals the impact of the abnormal N-glycosylation of tau on its aggregation propensity. Sci Rep 9(1):1–10

    Article  CAS  Google Scholar 

  17. van Veen HA, Geerts ME, van Berkel PH, Nuijens JH (2004) The role of N-linked glycosylation in the protection of human and bovine lactoferrin against tryptic proteolysis. Eur J Biochem 271(4):678–684

    Article  PubMed  CAS  Google Scholar 

  18. Sareneva T, Pirhonen J, Cantell K, Julkunen I (1995) N-glycosylation of human interferon-\(\gamma \): glycans at Asn-25 are critical for protease resistance. Biochem J 308(1):9–14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Guan X, Chaffey PK, Wei X, Gulbranson DR, Ruan Y, Wang X et al (2018) Chemically precise glycoengineering improves human insulin. ACS Chem Biol 13(1):73–81

    Article  CAS  PubMed  Google Scholar 

  20. Mark AE, Berendsen HJ, Van Gunsteren WF (1991) Conformational flexibility of aqueous monomeric and dimeric insulin: a molecular dynamics study. Biochemistry 30(45):10866–10872

    Article  CAS  PubMed  Google Scholar 

  21. Zoete V, Meuwly M, Karplus M (2004) A comparison of the dynamic behavior of monomeric and dimeric insulin shows structural rearrangements in the active monomer. J Mol Biol 342(3):913–929

    Article  CAS  PubMed  Google Scholar 

  22. Yang C, Lu D, Liu Z (2011) How PEGylation enhances the stability and potency of insulin: a molecular dynamics simulation. Biochemistry 50(13):2585–2593

    Article  CAS  PubMed  Google Scholar 

  23. Pincus M (1970) Letter to the editor—a Monte Carlo method for the approximate solution of certain types of constrained optimization problems. Oper Res 18(6):1225–1228

    Article  Google Scholar 

  24. Isralewitz B, Gao M, Schulten K (2001) Steered molecular dynamics and mechanical functions of proteins. Curr Opin Struct Biol 11(2):224–230

    Article  CAS  PubMed  Google Scholar 

  25. Sugita Y, Kitao A, Okamoto Y (2000) Multidimensional replica-exchange method for free-energy calculations. J Chem Phys 113(15):6042–6051

    Article  CAS  Google Scholar 

  26. Antoszewski A, Feng CJ, Vani BP, Thiede EH, Hong L, Weare J et al (2020) Insulin dissociates by diverse mechanisms of coupled unfolding and unbinding. J Phys Chem B 124(27):5571–5587

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Busto-Moner L, Feng CJ, Antoszewski A, Tokmakoff A, Dinner AR (2021) Structural Ensemble of the Insulin Monomer. Biochemistry 60(42):3125–3136

    Article  CAS  PubMed  Google Scholar 

  28. Hansmann UH (1997) Parallel tempering algorithm for conformational studies of biological molecules. Chem Phys Lett 281(1–3):140–150

    Article  CAS  Google Scholar 

  29. Earl DJ, Deem MW (2005) Parallel tempering: Theory, applications, and new perspectives. Phys Chem Chem Phys 7(23):3910–3916

    Article  CAS  PubMed  Google Scholar 

  30. Nuske F, Keller BG, Pérez-Hernández G, Mey AS, Noé F (2014) Variational approach to molecular kinetics. J Chem Theory Comput 10(4):1739–1752

    Article  PubMed  CAS  Google Scholar 

  31. Lorpaiboon C, Thiede EH, Webber RJ, Weare J, Dinner AR (2020) Integrated variational approach to conformational dynamics: A robust strategy for identifying eigenfunctions of dynamical operators. J Phys Chem B 124(42):9354–9364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Prinz JH, Wu H, Sarich M, Keller B, Senne M, Held M et al (2011) Markov models of molecular kinetics: Generation and validation. J Chem Phys 134(17):174105

    Article  PubMed  CAS  Google Scholar 

  33. Bowman GR, Pande VS, Noé F (2013) An introduction to Markov state models and their application to long timescale molecular simulation, vol 797. Springer, New York

    Google Scholar 

  34. Schütte C, Fischer A, Huisinga W, Deuflhard P (1999) A direct approach to conformational dynamics based on hybrid Monte Carlo. J Comput Phys 151(1):146–168

    Article  Google Scholar 

  35. Schilling RJ, Mitra AK (1991) Degradation of insulin by trypsin and alpha-chymotrypsin. Pharm Res 8(6):721–727

    Article  CAS  PubMed  Google Scholar 

  36. Chaffey PK, Guan X, Chen C, Ruan Y, Wang X, Tran AH et al (2017) Structural insight into the stabilizing effect of O-glycosylation. Biochemistry 56(23):2897–2906

    Article  CAS  PubMed  Google Scholar 

  37. Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J Comput Phys 23(2):187–199

    Article  Google Scholar 

  38. Sugita Y, Okamoto Y (2000) Replica-exchange multicanonical algorithm and multicanonical replica-exchange method for simulating systems with rough energy landscape. Chem Phys Lett 329(3–4):261–270

    Article  CAS  Google Scholar 

  39. Lyubartsev A, Martsinovski A, Shevkunov S, Vorontsov-Velyaminov P (1992) New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles. J Chem Phys 96(3):1776–1783

    Article  CAS  Google Scholar 

  40. Fávero-Retto MP, Palmieri LC, Souza TA, Almeida FC, Lima LMT (2013) Structural meta-analysis of regular human insulin in pharmaceutical formulations. Eur J Pharm Biopharm 85(3):1112–1121

    Article  PubMed  CAS  Google Scholar 

  41. Timofeev V, Chuprov-Netochin R, Samigina V, Bezuglov V, Miroshnikov K, Kuranova I (2010) X-ray investigation of gene-engineered human insulin crystallized from a solution containing polysialic acid. Acta Crystallogr Sect F 66(3):259–263

    Article  CAS  Google Scholar 

  42. Křížková K, Veverka V, Maletínská L, Hexnerová R, Brzozowski AM, Jiráček J et al (2014) Structural and functional study of the GlnB22-insulin mutant responsible for maturity-onset diabetes of the young. PLoS ONE 9(11):e112883

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. DeLano WL et al (2002) Pymol: an open-source molecular graphics tool. CCP4 Newsl Protein Crystallogr 40(1):82–92

    Google Scholar 

  44. Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40(W1):W537–W541

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Myers J, Grothaus G, Narayanan S, Onufriev A (2006) A simple clustering algorithm can be accurate enough for use in calculations of pKs in macromolecules. Proteins 63(4):928–938

    Article  CAS  PubMed  Google Scholar 

  46. Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pK as and adding missing hydrogens to macromolecules. Nucleic Acids Res 33(suppl-2):W368–W371

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. McQueen C (2017) Comprehensive toxicology. Elsevier, Amsterdam

    Google Scholar 

  48. Fallingborg J (1999) Intraluminal pH of the human gastrointestinal tract. Dan Med Bull 46(3):183–196

    CAS  PubMed  Google Scholar 

  49. Kirschner KN, Yongye AB, Tschampel SM, González-Outeiriño J, Daniels CR, Foley BL et al (2008) GLYCAM06: a generalizable biomolecular force field. Carbohydrates 29(4):622–655

    CAS  Google Scholar 

  50. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B et al (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25

    Article  Google Scholar 

  51. Páll S, Abraham MJ, Kutzner C, Hess B, Lindahl E (2014) Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: International Conference on Exascale Applications and Software. Springer, pp 3–27

  52. Da Silva AWS, Vranken WF (2012) ACPYPE-Antechamber python parser interface. BMC Res Notes 5(1):1–8

    CAS  Google Scholar 

  53. Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126(1):014101

    Article  PubMed  CAS  Google Scholar 

  54. Berendsen HJ, Postma JV, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690

    Article  CAS  Google Scholar 

  55. Parrinello M, Rahman A (1980) Crystal structure and pair potentials: a molecular-dynamics study. Phys Rev Lett 45(14):1196

    Article  CAS  Google Scholar 

  56. Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52(12):7182–7190

    Article  CAS  Google Scholar 

  57. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593

    Article  CAS  Google Scholar 

  58. Hess B, Bekker H, Berendsen HJ, Fraaije JG (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18(12):1463–1472

    Article  CAS  Google Scholar 

  59. Wilcox P (1970) Chymotrypsinogens—chymotrypsins. In: Methods in enzymology, vol 19. Elsevier, pp 64–108

  60. Eisenhaber F, Lijnzaad P, Argos P, Sander C, Scharf M (1995) The double cubic lattice method: efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies. J Comput Chem 16(3):273–284

    Article  CAS  Google Scholar 

  61. Chodera JD (2016) A simple method for automated equilibration detection in molecular simulations. J Chem Theory Comput 12(4):1799–1805

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kendall MG, et al (1948) The advanced theory of statistics. Vols. 1. The advanced theory of statistics, vol 1. (Ed. 4)

  63. Schechter I, Berger A (1968) On the active site of proteases. III. Mapping the active site of papain; specific peptide inhibitors of papain. Biochem Biophys Res Commun 32(5):898–902

    Article  CAS  PubMed  Google Scholar 

  64. Appel W (1986) Chymotrypsin: molecular and catalytic properties. Clin Biochem 19(6):317–322

    Article  CAS  PubMed  Google Scholar 

  65. Hedstrom L (2002) Serine protease mechanism and specificity. Chem Rev 102(12):4501–4524

    Article  CAS  PubMed  Google Scholar 

  66. Bode W, Huber R (1993) Natural protein proteinase inhibitors and their interaction with proteinases. EJB Rev, pp 43–61

  67. Coombs GS, Rao MS, Olson AJ, Dawson PE, Madison EL (1999) Revisiting catalysis by chymotrypsin family serine proteases using peptide substrates and inhibitors with unnatural main chains. J Biol Chem 274(34):24074–24079

    Article  CAS  PubMed  Google Scholar 

  68. Gowers RJ, Linke M, Barnoud J, Reddy TJE, Melo MN, Seyler SL, et al (2019) MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations. Los Alamos National Lab.(LANL), Los Alamos, NM

  69. Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O (2011) MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem 32(10):2319–2327

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ramachandran GNVS, Ramakrishnan C (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99

    Article  CAS  PubMed  Google Scholar 

  71. Lovell SC, Davis IW, Arendall WB III, De Bakker PI, Word JM, Prisant MG et al (2003) Structure validation by C\(\alpha \) geometry: \(\phi \), \(\psi \) and C\(\beta \) deviation. Proteins 50(3):437–450

    Article  CAS  PubMed  Google Scholar 

  72. McGibbon RT, Beauchamp KA, Harrigan MP, Klein C, Swails JM, Hernández CX et al (2015) MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys J 109(8):1528–1532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Baker EN, Hubbard RE (1984) Hydrogen bonding in globular proteins. Prog Biophys Mol Biol 44(2):97–179

    Article  CAS  PubMed  Google Scholar 

  74. Harding MM, Hodgkin DC, Kennedy AF, O’Connor A, Weitzmann PDJ (1966) The crystal structure of insulin: II. An investigation of rhombohedral zinc insulin crystals and a report of other crystalline forms. J Mol Biol 16(1):212-IN30

    Article  CAS  PubMed  Google Scholar 

  75. Antolíková E, Žáková L, Turkenburg JP, Watson CJ, Hančlová I, Šanda M et al (2011) Non-equivalent role of inter- and intramolecular hydrogen bonds in the insulin dimer interface*. J Biol Chem 286(42):36968–36977

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Keller D, Clausen R, Josefsen K, Led JJ (2001) Flexibility and bioactivity of insulin: an NMR investigation of the solution structure and folding of an unusually flexible human insulin mutant with increased biological activity. Biochemistry 40(35):10732–10740

    Article  CAS  PubMed  Google Scholar 

  77. Jørgensen AMM, Olsen HB, Balschmidt P, Led JJ (1996) Solution structure of the superactive monomeric des-[Phe (B25)] human insulin mutant: elucidation of the structural basis for the monomerization of des-[Phe (B25)] insulin and the dimerization of native insulin. J Mol Biol 257(3):684–699

    Article  PubMed  Google Scholar 

  78. Bocian W, Sitkowski J, Bednarek E, Tarnowska A, Kawecki R, Kozerski L (2008) Structure of human insulin monomer in water/acetonitrile solution. J Biomol NMR 40(1):55–64

    Article  CAS  PubMed  Google Scholar 

  79. Bakan A, Meireles LM, Bahar I (2011) ProDy: Protein dynamics inferred from theory and experiments. Bioinformatics 27(11):1575–1577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D et al (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat Methods 17:261–272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D et al (2020) Array programming with NumPy. Nature 585:357–362

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Wilson EB (1927) Probable inference, the law of succession, and statistical inference. J Am Stat Assoc 22(158):209–212

    Article  Google Scholar 

  83. Newcombe RG (1998) Two-sided confidence intervals for the single proportion: Comparison of seven methods. Stat Med 17(8):857–872

    Article  CAS  PubMed  Google Scholar 

  84. Wallis S (2013) Binomial confidence intervals and contingency tests: mathematical fundamentals and the evaluation of alternative methods. J. Quant. Linguistics 20(3):178–208

    Article  Google Scholar 

  85. Ward MD, Zimmerman MI, Meller A, Chung M, Swamidass S, Bowman GR (2021) Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets. Nat Commun 12(1):1–12

    Article  CAS  Google Scholar 

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Acknowledgements

Research reported in this publication was primarily supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award number R01EB025892. This work was also supported in part by the NIH/CU Molecular Biophysics Graduate Traineeship T32 GM065103 and the CAMS Innovation Fund for Medical Sciences (CIFMS 2021-1-I2M-026). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Department of Chemical & Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA

    Wei-Tse Hsu & Michael R. Shirts

  2. Department of Biochemistry, University of Colorado Boulder, Boulder, CO, 80309, USA

    Dominique A. Ramirez

  3. Department of Chemistry, University of Colorado Boulder, Boulder, CO, 80309, USA

    Tarek Sammakia

  4. Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100050, China

    Zhongping Tan

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  1. Wei-Tse Hsu
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Contributions

Contributions based on CRediT taxonomy: W.-T.H.: Conceptualization, Writing – Original Draft, Writing – Review & Editing, Methodology, Investigation D.A.R.: Writing – Original Draft, Writing -Review & Editing, Methodology, Investigation T.S.: Conceptualization, Writing – Review & Editing, Funding Acquisition Z.T.: Conceptualization, Writing – Review & Editing, Funding Acquisition M.R.S.: Conceptualization, Writing – Review & Editing, Supervision, Funding Acquisition

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Correspondence to Zhongping Tan or Michael R. Shirts.

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M.R.S. is an Open Science Fellow at and consults for Roivant Sciences.

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Hsu, WT., Ramirez, D.A., Sammakia, T. et al. Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation. J Comput Aided Mol Des 36, 313–328 (2022). https://doi.org/10.1007/s10822-022-00453-6

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