Startseite Bridging experiment and theory: a computational exploration of UMG-SP3 dynamics
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Bridging experiment and theory: a computational exploration of UMG-SP3 dynamics

  • Luís M. C. Teixeira , Pedro Paiva , Pedro Ferreira , Laura Rotilio , Jens P. Morth , Daniel E. Otzen , Pedro A. Fernandes und Maria J. Ramos ORCID logo EMAIL logo
Veröffentlicht/Copyright: 27. August 2025
Pure and Applied Chemistry
Aus der Zeitschrift Pure and Applied Chemistry Band 97 Heft 10

Abstract

Understanding how dynamical behaviour and structural features influence protein function and stability is crucial. While extensive experimental data exist, studying real-time protein dynamics and enzyme catalysis remains challenging. Computational advances have been instrumental in overcoming experimental limitations, enabling molecular-level insights into biological macromolecules. The integration of experimental and computational approaches has proven to be very valuable in protein studies. Here, we demonstrate this synergy by investigating the conformational stability of the urethanase UMG-SP3, which exhibited a lower optimum temperature than expected and rapid loss of activity. Molecular dynamics simulations of the UMG-SP3-substrate complex at various temperatures revealed structural rearrangements outside the optimum temperature range (25–35 °C), leading to loss of the native protein fold and impaired substrate binding. Even at the optimum temperature for activity, the enzyme struggled to maintain a catalytically favourable orientation, aligning with experimental findings. Unfolding profiles were determined through differential scanning fluorimetry. Notably, the computational results provided a rationaly for the structural instability observed experimentally, emphasizing the strength of computational methods in elucidating protein behaviour at the atomic level. This study highlights the importance of combining experimental and computational approaches to deepen our understanding of protein stability and function.

Keywords: Enzyme stability; molecular dynamics; protein dynamics; quantum science and technology; UMG-SP3; urethanase
Corresponding author: Maria J. Ramos, LAQV/REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências Universidade do Porto, Rua do Campo Alegre, s/n, 4169-007, Porto, Portugal; and EnZync Center for Enzymatic Deconstruction of Thermoset Plastics, Aarhus , Denmark, e-mail:
Article note: A collection of invited papers to celebrate the UN’s proclamation of 2025 as the International Year of Quantum Science and Technology.

Funding source: Novo Nordisk Fonden

Award Identifier / Grant number: NNF22OC0072891

Funding source: Fundação para a Ciência e a Tecnologia

Award Identifier / Grant number: UID/50006

Acknowledgments

LMCT, PP, PF, PAF and MJR would like to thank the European High-Performance Computing Joint Undertaking (EuroHPC JU) that granted the access to MeluXina, the petascale Euro-HPC supercomputer located in Bissen, Luxembourg, under proposal EHPC-REG2023R03-163. PF thanks FCT for funding (Ref. CEECINST/00136/2021/CP2820/CT0002 DOI 10.54499/CEECINST/00136/2021/CP2820/CT0002. LMCT would also like to thank FCT/MECI for funding his PhD project through the grant 2024.05090.BD.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Conceptualization: LMCT, PP, PF, LR, JPM, DEO, PAF, and MJR; Methodology: LMCT, PP, PF, LR, PAF, and MJR; Formal Analysis: LMCT and PP; Investigation: LMCT, PP, and LR; Writing – Original Draft: LMCT; Writing – Review & Editing: LMCT, PP, PF, LR, JPM, DEO, PAF, and MJR; Project Administration: JPM, DEO, PAF, and MJR; Funding Acquisition: PAF and MJR.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The authors would like to thank the EnZync consortium, and the financial support received by the Novo Nordisk Fonden, under project NNF22OC0072891 (Challenge Programme 2022 - Recycling or a Sustainable Society). This work received financial support from the PT national funds (FCT/MECI, Fundação para a Ciência e Tecnologia and Ministério da Educação, Ciência e Inovação) through the project UID/50006 -Laboratório Associado para a Química Verde - Tecnologias e Processos Limpos.

  7. Data availability: The cartesian coordinates of the modelled complex and equilibrated structures, along with the force field parameters, are provided in the Supporting Information.

  8. Software availability: The initial PDB structure (9FW1) is available for download at https://www.rcsb.org/structure/9FW1. System preparation, minimization, and molecular dynamics simulations were performed using the AMBER 18 package, which can be purchased at https://ambermd.org/. The pKa prediction was carried out using the ProteinPrepare web server (https://open.playmolecule.org/tools/proteinprepare) and PROPKA 3.0, which is available for download at https://propka.readthedocs.io/en/latest. Visualization of the system was done using VMD, which can be downloaded from https://www.ks.uiuc.edu/Research/vmd/. Parameterization of DUE-MDA was performed using Gaussian 09 D01, which can be purchased at https://gaussian.com/. The enzyme-substrate complex was modelled using PyMOL, available at https://www.pymol.org/.

References

1. Bruce Alberts, A. J.; Lewis, Julian; Raff, Martin; Roberts, Keith; Walter, Peter Molecular Biology of the Cell; Garland Science: New York, 2002.Suche in Google Scholar

2. Gregory, A.; Petsko, D. R. Protein Structure and Function; New Science Press: Oxford, 2004.Suche in Google Scholar

3. Lesk, A. Introduction to Protein Science: Architecture, Function, and Genomics; Oxford University Press: Oxford, 2016.10.1093/hesc/9780198716846.001.0001Suche in Google Scholar

4. Nam, K.; Wolf-Watz, M. Protein Dynamics: The Future Is Bright and Complicated. Struct. Dyn. 2023, 10, 014301; https://doi.org/10.1063/4.0000179.Suche in Google Scholar PubMed PubMed Central

5. Schirò, G.; Weik, M. Role of Hydration Water in the Onset of Protein Structural Dynamics. J. Phys. Condens. Matter. 2019, 31, 463002; https://doi.org/10.1088/1361-648X/ab388a.Suche in Google Scholar PubMed

6. Maveyraud, L.; Mourey, L. Protein X-Ray Crystallography and Drug Discovery. Molecules 2020, 25; https://doi.org/10.3390/molecules25051030.Suche in Google Scholar PubMed PubMed Central

7. Hu, Y.; Cheng, K.; He, L.; Zhang, X.; Jiang, B.; Jiang, L.; Li, C.; Wang, G.; Yang, Y.; Liu, M. NMR-Based Methods for Protein Analysis. Anal. Chem. 2021, 93, 1866–1879; https://doi.org/10.1021/acs.analchem.0c03830.Suche in Google Scholar PubMed

8. de Oliveira, T. M.; van Beek, L.; Shilliday, F.; Debreczeni, J. É.; Phillips, C. Cryo-E. M. The Resolution Revolution and Drug Discovery. SLAS Discovery 2021, 26, 17–31. https://doi.org/10.1177/2472555220960401.Suche in Google Scholar PubMed

9. Hameduh, T.; Haddad, Y.; Adam, V.; Heger, Z. Homology Modeling in the Time of Collective and Artificial Intelligence. Comput. Struct. Biotechnol. J. 2020, 18, 3494–3506. https://doi.org/10.1016/j.csbj.2020.11.007.Suche in Google Scholar PubMed PubMed Central

10. Hollingsworth, S. A.; Dror, R. O. Molecular Dynamics Simulation for All. Neuron 2018, 99, 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011.Suche in Google Scholar PubMed PubMed Central

11. Levine, I. N. Quantum Chemistry; Pearson: New Jersey, 2014.Suche in Google Scholar

12. Ciccotti, G.; Dellago, C.; Ferrario, M.; Hernández, E. R.; Tuckerman, M. E. Molecular Simulations: Past, Present, and Future (A Topical Issue in EPJB). Eur. Phys. J. B 2022, 95, 3; https://doi.org/10.1140/epjb/s10051-021-00249-x.Suche in Google Scholar

13. Tuckerman, M. E. Statistical Mechanics: Theory and Molecular Simulation; Oxford University Press: Oxford, 2009.Suche in Google Scholar

14. Verlet, L. Computer “Experiments” on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules. Phys. Rev. 1967, 159, 98–103; https://doi.org/10.1103/PhysRev.159.98.Suche in Google Scholar

15. Michael Griebel, G. Z.; Knapek, S. Numerical Simulation in Molecular Dynamics: Numerics, Algorithms, Parallelization, Applications; Springer Science & Business Media: Berlin, 2007.Suche in Google Scholar

16. McCammon, J. A.; Gelin, B. R.; Karplus, M. Dynamics of Folded Proteins. Nature 1977, 267, 585–590; https://doi.org/10.1038/267585a0.Suche in Google Scholar PubMed

17. Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of N-Alkanes. J. Comput. Phys. 1977, 23, 327–341. https://doi.org/10.1016/0021-9991(77)90098-5.Suche in Google Scholar

18. Andersen, H. C. Molecular Dynamics Simulations at Constant Pressure and/or Temperature. J. Chem. Phys. 1980, 72, 2384–2393; https://doi.org/10.1063/1.439486.Suche in Google Scholar

19. Nosé, S. A Unified Formulation of the Constant Temperature Molecular Dynamics Methods. J. Chem. Phys. 1984, 81, 511–519; https://doi.org/10.1063/1.447334.Suche in Google Scholar

20. Barth, E.; Kuczera, K.; Leimkuhler, B.; Skeel, R. D. Algorithms for Constrained Molecular Dynamics. J. Comput. Chem. 1995, 16, 1192–1209. https://doi.org/10.1002/jcc.540161003.Suche in Google Scholar

21. Umegawa, Y.; Yamamoto, T.; Dixit, M.; Funahashi, K.; Seo, S.; Nakagawa, Y.; Suzuki, T.; Matsuoka, S.; Tsuchikawa, H.; Hanashima, S.; Oishi, T.; Matsumori, N.; Shinoda, W.; Murata, M. Amphotericin B Assembles into Seven-Molecule Ion Channels: An NMR and Molecular Dynamics Study. Sci. Adv. 2022, 8, eabo2658; https://doi.org/10.1126/sciadv.abo2658.Suche in Google Scholar PubMed PubMed Central

22. Rotilio, L.; Bayer, T.; Meinert, H.; Teixeira, L. M. C.; Johansen, M. B.; Sommerfeldt, A.; Petersen, A. R.; Sandahl, A.; Keller, M. B.; Holck, J.; Paiva, P.; Otzen, D. E.; Bornscheuer, U. T.; Wei, R.; Fernandes, P. A.; Ramos, M. J.; Westh, P.; Morth, J. P. Structural and Functional Characterization of an Amidase Targeting a Polyurethane for Sustainable Recycling. Angew Chem. Int. Ed. Engl. 2025, 64, e202419535; https://doi.org/10.1002/anie.202419535.Suche in Google Scholar PubMed

23. Aranda-García, D.; Stepniewski, T. M.; Torrens-Fontanals, M.; García-Recio, A.; Lopez-Balastegui, M.; Medel-Lacruz, B.; Morales-Pastor, A.; Peralta-García, A.; Dieguez-Eceolaza, M.; Sotillo-Nuñez, D.; Ding, T.; Drabek, M.; Jacquemard, C.; Jakowiecki, J.; Jespers, W.; Jiménez-Rosés, M.; Jun-Yu-Lim, V.; Nicoli, A.; Orzel, U.; Shahraki, A.; Tiemann, J. K. S.; Ledesma-Martin, V.; Nerín-Fonz, F.; Suárez-Dou, S.; Canal, O.; Pándy-Szekeres, G.; Mao, J.; Gloriam, D. E.; Kellenberger, E.; Latek, D.; Guixà-González, R.; Gutiérrez-de-Terán, H.; Tikhonova, I. G.; Hildebrand, P. W.; Filizola, M.; Babu, M. M.; Di Pizio, A.; Filipek, S.; Kolb, P.; Cordomi, A.; Giorgino, T.; Marti-Solano, M.; Selent, J. Large Scale Investigation of GPCR Molecular Dynamics Data Uncovers Allosteric Sites and Lateral Gateways. Nat. Commun. 2025, 16, 2020; https://doi.org/10.1038/s41467-025-57034-y.Suche in Google Scholar PubMed PubMed Central

24. Warshel, A.; Karplus, M. Calculation of Ground and Excited State Potential Surfaces of Conjugated Molecules. I. Formulation and Parametrization. J. Am. Chem. Soc. 1972, 94, 5612–5625; https://doi.org/10.1021/ja00771a014.Suche in Google Scholar

25. Warshel, A.; Levitt, M. Theoretical Studies of Enzymic Reactions: Dielectric, Electrostatic and Steric Stabilization of the Carbonium Ion in the Reaction of Lysozyme. J. Mol. Biol. 1976, 103, 227–249. https://doi.org/10.1016/0022-2836(76)90311-9.Suche in Google Scholar PubMed

26. Liu, M.; Wang, Y.; Chen, Y.; Field, M. J.; Gao, J. QM/MM Through the 1990s: The First Twenty Years of Method Development and Applications. Isr. J. Chem. 2014, 54, 1250–1263. https://doi.org/10.1002/ijch.201400036.Suche in Google Scholar PubMed PubMed Central

27. van der Kamp, M. W.; Mulholland, A. J. Combined Quantum Mechanics/Molecular Mechanics (QM/MM) Methods in Computational Enzymology. Biochemistry 2013, 52, 2708–2728; https://doi.org/10.1021/bi400215w.Suche in Google Scholar PubMed

28. Teixeira, L. M. C.; Coimbra, J. T. S.; Ramos, M. J.; Fernandes, P. A. Transmembrane Protease Serine 2 Proteolytic Cleavage of the SARS-CoV-2 Spike Protein: A Mechanistic Quantum Mechanics/Molecular Mechanics Study to Inspire the Design of New Drugs to Fight the COVID-19 Pandemic. J. Chem. Inf. Model. 2022, 62, 2510–2521; https://doi.org/10.1021/acs.jcim.1c01561.Suche in Google Scholar PubMed PubMed Central

29. Makurat, S.; Neves, R. P. P.; Ramos, M. J.; Rak, J. Q. M/M. M. MD Study on the Reaction Mechanism of Thymidine Phosphorylation Catalyzed by the Enzyme Thermotoga Maritima Thymidine Kinase 1. ACS Catal. 2024, 14, 9840–9849; https://doi.org/10.1021/acscatal.4c01867.Suche in Google Scholar

30. Kohen, A. Role of Dynamics in Enzyme Catalysis: Substantial versus Semantic Controversies. Acc. Chem. Res. 2015, 48, 466–473; https://doi.org/10.1021/ar500322s.Suche in Google Scholar PubMed PubMed Central

31. Duran, C.; Casadevall, G.; Osuna, S. Harnessing Conformational Dynamics in Enzyme Catalysis to Achieve Nature-like Catalytic Efficiencies: the Shortest Path Map Tool for Computational Enzyme Redesign. Faraday Discuss. 2024, 252, 306–322; https://doi.org/10.1039/D3FD00156C.Suche in Google Scholar

32. Krishna, N. B.; Roopa, L.; Pravin Kumar, R.; S, G. T. Computational Studies on the Catalytic Potential of the Double Active Site for Enzyme Engineering. Sci. Rep. 2024, 14, 17892; https://doi.org/10.1038/s41598-024-60824-x.Suche in Google Scholar PubMed PubMed Central

33. Paiva, P.; Teixeira, L. M. C.; Wei, R.; Liu, W.; Weber, G.; Morth, J. P.; Westh, P.; Petersen, A. R.; Johansen, M. B.; Sommerfeldt, A.; Sandahl, A.; Otzen, D. E.; Fernandes, P. A.; Ramos, M. J. Unveiling the Enzymatic Pathway of UMG-SP2 Urethanase: Insights into Polyurethane Degradation at the Atomic Level. Chem. Sci. 2025, 16, 2437–2452; https://doi.org/10.1039/D4SC06688J.Suche in Google Scholar PubMed PubMed Central

34. Branson, Y.; Söltl, S.; Buchmann, C.; Wei, R.; Schaffert, L.; Badenhorst, C. P. S.; Reisky, L.; Jäger, G.; Bornscheuer, U. T. Urethanases for the Enzymatic Hydrolysis of Low Molecular Weight Carbamates and the Recycling of Polyurethanes. Angew Chem. Int. Ed. Engl. 2023, 62, e202216220; https://doi.org/10.1002/anie.202216220.Suche in Google Scholar PubMed

35. Martínez-Rosell, G.; Giorgino, T.; De Fabritiis, G. PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations. J. Chem. Inf. Model. 2017, 57, 1511–1516; https://doi.org/10.1021/acs.jcim.7b00190.Suche in Google Scholar PubMed

36. Olsson, M. H. M.; Søndergaard, C. R.; Rostkowski, M.; Jensen, J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions. J. Chem. Theory Comput. 2011, 7, 525–537; https://doi.org/10.1021/ct100578z.Suche in Google Scholar PubMed

37. Gaussian 09, Revision D; Gaussian, Inc.: Wallingford CT, 2016.Suche in Google Scholar

38. The PyMOL Molecular Graphics System; Schrödinger, LLC: New York, 2015.Suche in Google Scholar

39. Li, Z.; Han, X.; Cong, L.; Singh, P.; Paiva, P.; Branson, Y.; Li, W.; Chen, Y.; Jaradat, D. M. M.; Lennartz, F.; Bayer, T.; Schmidt, L.; Garscha, U.; You, S.; Fernandes, P. A.; Ramos, M. J.; Bornscheuer, U. T.; Weber, G.; Wei, R.; Liu, W. Structure-Guided Engineering of a Versatile Urethanase Improves its Polyurethane Depolymerization Activity. Adv. Sci., 2416019. https://doi.org/10.1002/advs.202416019.Suche in Google Scholar PubMed PubMed Central

40. AMBER 2018; University of California: San Francisco, 2018.Suche in Google Scholar

41. Cornell, W. D.; Cieplak, P.; Bayly, C. I.; Gould, I. R.; Merz, K. M.; Ferguson, D. M.; Spellmeyer, D. C.; Fox, T.; Caldwell, J. W.; Kollman, P. A. A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules. J. Am. Chem. Soc. 1995, 117, 5179–5197; https://doi.org/10.1021/ja00124a002.Suche in Google Scholar

42. Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A Smooth Particle Mesh Ewald Method. J. Chem. Phys. 1995, 103, 8577–8593; https://doi.org/10.1063/1.470117.Suche in Google Scholar

43. Abraham, M. J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J. C.; Hess, B.; Lindahl, E. GROMACS: High Performance Molecular Simulations through Multi-Level Parallelism from Laptops to Supercomputers. SoftwareX 2015, 1-2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001.Suche in Google Scholar

44. Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984, 81, 3684–3690; https://doi.org/10.1063/1.448118.Suche in Google Scholar

45. Parrinello, M.; Rahman, A. Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method. J. Appl. Phys. 1981, 52, 7182–7190; https://doi.org/10.1063/1.328693.Suche in Google Scholar

46. Sanfelice, D.; Temussi, P. A. Cold Denaturation as a Tool to Measure Protein Stability. Biophys. Chem. 2016, 208, 4–8; https://doi.org/10.1016/j.bpc.2015.05.007.Suche in Google Scholar PubMed PubMed Central

47. Privalov, P. L. Cold Denaturation of Proteins. Crit. Rev. Biochem. Mol. Biol. 1990, 25, 281–305; https://doi.org/10.3109/10409239009090612.Suche in Google Scholar PubMed

48. Connolly, M. L. Solvent-Accessible Surfaces of Proteins and Nucleic Acids. Science 1983, 221, 709–713; https://doi.org/10.1126/science.6879170.Suche in Google Scholar PubMed

49. Ali, S. A.; Hassan, M. I.; Islam, A.; Ahmad, F. A Review of Methods Available to Estimate Solvent-Accessible Surface Areas of Soluble Proteins in the Folded and Unfolded States. Curr. Protein Pept. Sci. 2014, 15, 456–476; https://doi.org/10.2174/1389203715666140327114232.Suche in Google Scholar PubMed

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/pac-2025-0489).

Received: 2025-04-22
Accepted: 2025-08-08
Published Online: 2025-08-27
Published in Print: 2025-10-27

© 2025 IUPAC & De Gruyter

Artikel in diesem Heft

  1. Frontmatter
  2. Review Articles
  3. Minimum energy path methods and reactivity for enzyme reaction mechanisms: a perspective
  4. The quantum revolution in enzymatic chemistry: combining quantum and classical mechanics to understand biochemical processes
  5. A quantum chemical perspective of photoactivated biological functions
  6. Does chemistry need more physics?
  7. Rotational dynamics of ATP synthase: mechanical constraints and energy dissipative channels
  8. Transforming dreams into reality: a fairy-tale wedding of chemistry with quantum mechanics
  9. The quantum chemistry revolution and the instrumental revolution as evidenced by the Nobel Prizes in chemistry
  10. Influence of symmetry on the second-order NLO properties: insights from the few state approximations
  11. The dichotomy between chemical concepts and numbers after almost 100 years of quantum chemistry: conceptual density functional theory as a case study
  12. How ‘de facto variational’ are fully iterative, approximate iterative, and quasiperturbative coupled cluster methods near equilibrium geometries?
  13. Electronic structure of methyl radical photodissociation
  14. Bridging experiment and theory: a computational exploration of UMG-SP3 dynamics
  15. Research Articles
  16. O–Li⋯O and C–Li⋯C lithium bonds in small closed shell and open shell systems as analogues of hydrogen bonds
  17. Metal–ligand bonding and noncovalent interactions of mutated myoglobin proteins: a quantum mechanical study
https://doi.org/10.1515/pac-2025-0489
Schlagwörter für diesen Artikel
Enzyme stability; molecular dynamics; protein dynamics; quantum science and technology; UMG-SP3; urethanase

Artikel in diesem Heft

  1. Frontmatter
  2. Review Articles
  3. Minimum energy path methods and reactivity for enzyme reaction mechanisms: a perspective
  4. The quantum revolution in enzymatic chemistry: combining quantum and classical mechanics to understand biochemical processes
  5. A quantum chemical perspective of photoactivated biological functions
  6. Does chemistry need more physics?
  7. Rotational dynamics of ATP synthase: mechanical constraints and energy dissipative channels
  8. Transforming dreams into reality: a fairy-tale wedding of chemistry with quantum mechanics
  9. The quantum chemistry revolution and the instrumental revolution as evidenced by the Nobel Prizes in chemistry
  10. Influence of symmetry on the second-order NLO properties: insights from the few state approximations
  11. The dichotomy between chemical concepts and numbers after almost 100 years of quantum chemistry: conceptual density functional theory as a case study
  12. How ‘de facto variational’ are fully iterative, approximate iterative, and quasiperturbative coupled cluster methods near equilibrium geometries?
  13. Electronic structure of methyl radical photodissociation
  14. Bridging experiment and theory: a computational exploration of UMG-SP3 dynamics
  15. Research Articles
  16. O–Li⋯O and C–Li⋯C lithium bonds in small closed shell and open shell systems as analogues of hydrogen bonds
  17. Metal–ligand bonding and noncovalent interactions of mutated myoglobin proteins: a quantum mechanical study
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 22.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/pac-2025-0489/html
Button zum nach oben scrollen