Abstract
Metal–ligand bonding and noncovalent interactions (NCIs), such as hydrogen bonding or π–π interactions, play a crucial role in determining the structure, function, and selectivity of both biological and artificial metalloproteins. In this study, we employed a hybrid quantum mechanics/molecular mechanics (QM/MM) approach to investigate the ligation of water or cyanide in a mutated myoglobin system, in which the native heme scaffold was replaced with M-salophen or M-salen Schiff base complexes (M = Cr, Mn, Fe). Using our local vibrational mode analysis, particularly local vibrational mode force constants as intrinsic bond strength parameters, complemented with electron density and natural orbital analyses we explored the role of metal–ligand bonding and NCIs in different environments within the myoglobin pocket. Our analysis revealed that metal–ligand bonding, for both water and cyanide ligands, is strongest in the delta form of distal histidine and favors salophen prosthetic groups, as indicated by an overall increase in metal–ligand bond strength. Hydrogen bonding between the distal histidine and ligand also exhibited greater strength in the delta form; however, this effect was more pronounced with salen prosthetic groups. Additionally, the NCIs within the active pocket of the protein were found to be variable, highlighting the adaptability of local force constants. In summary, our data underscore the potential of computational methodologies in guiding the rational design of artificial metalloproteins for tailored applications, with local vibrational mode analysis serving as a powerful tool for bond strength assessment.
Funding source: National Science Foundation, NSF
Award Identifier / Grant number: CHE2102461
Award Identifier / Grant number: DGE-2034834
Acknowledgments
Computational resources provided by SMU’s O’Donnell Institute of Data Science and High Performance Computing.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: All other authors state no conflict of interest.
-
Research funding: This work was supported by the National Science Foundation, Grant CHE2102461, and the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-2034834.
-
Data availability: All data are available via the manuscript and/or the supporting Information.
References
1. Vornholt, T.; Leiss-Maier, F.; Jeong, W. J.; Zeymer, C.; Song, W. J.; Roelfes, G.; Ward, T. R. Artificial Metalloenzymes. Nat. Rev. Methods Primers 2024, 4, 78. https://doi.org/10.1038/s43586-024-00356-w.Suche in Google Scholar
2. Brouwer, B.; Della-Felice, F.; Illies, J. H.; Iglesias-Moncayo, E.; Roelfes, G.; Drienovská, I. Noncanonical Amino Acids: Bringing New-to-Nature Functionalities to Biocatalysis. Chem. Rev. 2024, 124, 10877–10923. https://doi.org/10.1021/acs.chemrev.4c00136.Suche in Google Scholar PubMed PubMed Central
3. Vornholt, T.; Christoffel, F.; Pellizzoni, M. M.; Panke, S.; Ward, T. R.; Jeschek, M. Systematic Engineering of Artificial Metalloenzymes for New-to-Nature Reactions. Sci. Adv. 2021, 7, eabe4208. https://doi.org/10.1126/sciadv.abe4208.Suche in Google Scholar PubMed PubMed Central
4. Bloomer, B. J.; Clark, D. S.; Hartwig, J. F. Progress, Challenges, and Opportunities with Artificial Metalloenzymes in Biosynthesis. Biochem 2023, 62, 221–228. https://doi.org/10.1021/acs.biochem.1c00829.Suche in Google Scholar PubMed
5. Gao, L.; Zhang, Y.; Zhao, L.; Niu, W.; Tang, Y.; Gao, F.; Cai, P.; Yuan, Q.; Wang, X.; Jiang, H.; Gao, X. An Artificial Metalloenzyme for Catalytic Cancer-Specific DNA Cleavage and Operando Imaging. Sci. Adv. 2020, 6, eabb1421. https://doi.org/10.1126/sciadv.abb1421.Suche in Google Scholar PubMed PubMed Central
6. Davis, H. J.; Ward, T. R. Artificial Metalloenzymes: Challenges and Opportunities. ACS Cent. Sci. 2019, 5, 1120–1136. https://doi.org/10.1021/acscentsci.9b00397.Suche in Google Scholar PubMed PubMed Central
7. Alonso-Cotchico, L.; Rodriguez-Guerra, J.; Lledos, A.; Marechal, J. D. Molecular Modeling for Artificial Metalloenzyme Design and Optimization. Acc. Chem. Res. 2020, 53, 986–905. https://doi.org/10.1021/acs.accounts.0c00031.Suche in Google Scholar PubMed
8. Jeong, W. J.; Yu, J.; Song, W. J. Proteins as Diverse, Efficient, and Evolvable Scaffolds for Artificial Metalloenzymes. Chem. Commun. 2020, 56, 9586. https://doi.org/10.1039/D0CC03137B.Suche in Google Scholar
9. Maity, B.; Taher, M.; Mazumdar, S.; Ueno, T. Artificial Metalloenzymes Based on Protein Assembly. Coord. Chem. Rev. 2022, 469, 214593. https://doi.org/10.1016/j.ccr.2022.214593.Suche in Google Scholar
10. Matsuo, T.; Hirota, S. Artificial Enzymes with Protein Scaffolds: Structural Design and Modification. Bioorg. Med. Chem. 2014, 22, 5638–5656. https://doi.org/10.1016/j.bmc.2014.06.021.Suche in Google Scholar PubMed
11. Shafaat, H. S.; Manesis, A. C.; Yerbulekova, A. How to Build a Metalloenzyme: Lessons from a Protein-Based Model of Acetyl Coenzyme A Synthase. Acc. Chem. Res. 2023, 56, 984–993. https://doi.org/10.1021/acs.accounts.2c00824.Suche in Google Scholar PubMed
12. Bhardwaj, M.; Kamble, P.; Mundhe, P.; Jindal, M.; Thakur, P.; Bajaj, P. Multifaceted Personality and Roles of Heme Enzymes in Industrial Biotechnology. 3 Biotech 2023, 13, 389. https://doi.org/10.1007/s13205-023-03804-8.Suche in Google Scholar PubMed PubMed Central
13. Lemon, C. M. Diversifying the Functions of Heme Proteins with Non-Porphyrin Cofactors. J. Inorg. Biochem. 2023, 246, 112282. https://doi.org/10.1016/j.jinorgbio.2023.112282.Suche in Google Scholar PubMed
14. Oohora, K.; Hayashi, T. Myoglobins Engineered with Artificial Cofactors Serve as Artificial Metalloenzymes and Models of Natural Enzymes. Dalton Trans. 2021, 50, 1940–1949. https://doi.org/10.1039/D0DT03597A.Suche in Google Scholar
15. Kato, S.; Abe, M.; Gröger, H.; Hayashi, T. Reconstitution of Myoglobin with Iron Porphycene Generates an Artificial Aldoxime Dehydratase with Expanded Catalytic Activities. ACS Catal. 2024, 14, 13081–13087. https://doi.org/10.1021/acscatal.4c03220.Suche in Google Scholar
16. Singh, P.; Yadav, P.; Sodhi, K. K.; Tomer, A.; Mehta, S. B. Advancement in the Synthesis of Metal Complexes with Special Emphasis on Schiff Base Ligands and Their Important Biological Aspects. Results Chem. 2024, 101222. https://doi.org/10.1016/j.rechem.2023.101222.Suche in Google Scholar
17. Shahraki, S. Schiff Base Compounds as Artificial Metalloenzymes. Colloids Surf., B 2022, 218, 112727. https://doi.org/10.1016/j.colsurfb.2022.112727.Suche in Google Scholar PubMed
18. Ueno, T.; Ohashi, M.; Kono, M.; Kondo, K.; Suzuki, A.; Yamane, T.; Watanabe, Y. Crystal Structures of Artificial Metalloproteins: Tight Binding of FeIII(Schiff-Base) by Mutation of Ala71 to Gly in Apo-Myoglobin. Inorg. Chem. 2004, 43, 2852–2858. https://doi.org/10.1021/ic0498539.Suche in Google Scholar PubMed
19. Ueno, T.; Koshiyama, T.; Ohashi, M.; Kondo, K.; Kono, M.; Suzuki, A.; Yamane, T.; Watanabe, Y. Coordinated Design of Cofactor and Active Site Structures in Development of New Protein Catalysts. J. Am. Chem. Soc. 2005, 127, 6556–6562. https://doi.org/10.1021/ja045995q.Suche in Google Scholar PubMed
20. Ohashi, M.; Koshiyama, T.; Ueno, T.; Yanase, M.; Fujii, H.; Watanabe, Y. Preparation of Artificial Metalloenzymes by Insertion of Chromium(III) Schiff Base Complexes into Apomyoglobin Mutants. Angew. Chem., Int. Ed. 2003, 42, 1005–1008. https://doi.org/10.1002/anie.200390256.Suche in Google Scholar PubMed
21. Hirota, S.; Lin, Y.-W. Design of Artifcial Metalloproteins/Metalloenzymes by Tuning Noncovalent Interactions. J. Biol. Inorg. Chem. 2018, 23, 7–25. https://doi.org/10.1007/s00775-017-1506-8.Suche in Google Scholar PubMed
22. Kraka, E.; Zou, W.; Tao, Y. Decoding Chemical Information from Vibrational Spectroscopy Data: Local Vibrational Mode Theory. WIREs: Comput. Mol. Sci. 2020, 10, 1480. https://doi.org/10.1002/wcms.1480.Suche in Google Scholar
23. Kraka, E.; Quintano, M.; La Force, H. W.; Antonio, J. J.; Freindorf, M. The Local Vibrational Mode Theory and Its Place in the Vibrational Spectroscopy Arena. J. Phys. Chem. A 2022, 126, 8781–8798. https://doi.org/10.1021/acs.jpca.2c05962.Suche in Google Scholar PubMed
24. Kelley, J. D.; Leventhal, J. J. Normal Modes and Coordinates. In Problems in Classical and Quantum Mechanics: Extracting the Underlying Concepts; Springer International Publishing, 2017; pp 95–117.10.1007/978-3-319-46664-4_4Suche in Google Scholar
25. Wilson, E. B.; Decius, J. C.; Cross, P. C. Molecular Vibrations: The Theory of Infrared and Raman Vibrational Spectra; McGraw-Hill: New York, 1955.10.1149/1.2430134Suche in Google Scholar
26. Wilson, E. B. Some Mathematical Methods for the Study of Molecular Vibrations. J. Chem. Phys. 1941, 9, 76–84. https://doi.org/10.1063/1.1750829.Suche in Google Scholar
27. Barone, V.; Alessandrini, S.; Biczysko, M.; Cheeseman, J. R.; Clary, D. C.; McCoy, A. B.; DiRisio, R. J.; Neese, F.; Melosso, M.; Puzzarini, C. Computational Molecular Spectroscopy. Nat. Rev. Methods Primers 2021, 1, 38. https://doi.org/10.1038/s43586-021-00034-1.Suche in Google Scholar
28. Peluzo, B. M. T. C.; Makoś, M. Z.; Moura, R. T.Jr.; Freindorf, M.; Kraka, E. Linear Versus Bent Uranium(II) Metallocenes – A Local Vibrational Mode Study. Inorg. Chem. 2023, 62, 12510–12524. https://doi.org/10.1021/acs.inorgchem.3c01761.Suche in Google Scholar PubMed
29. Antonio, J. J.; Kraka, E. Metal–Ring Interactions in Group 2 ansa-metallocenes: Assessed with the Local Vibrational Mode Theory. Phys. Chem. Chem. Phys. 2024, 26, 15143–15155. https://doi.org/10.1039/d4cp00225c.Suche in Google Scholar PubMed
30. Antonio, J. J.; Kraka, E. Non-Covalent π–interactions in Mutated aquomet-Myoglobin Proteins: A QM/MM and Local Vibrational Mode Study. Biochemistry 2023, 62, 2325–2337. https://doi.org/10.1021/acs.biochem.3c00192.Suche in Google Scholar PubMed
31. Freindorf, M.; Antonio, J.; Kraka, E. Hydrogen Sulfide Ligation in Hemoglobin I of Lucina pectinata – a QM/MM and Local Mode Study. J. Phys. Chem. A 2023, 127, 8316–8329. https://doi.org/10.1021/acs.jpca.3c04399.Suche in Google Scholar PubMed
32. Freindorf, M.; Antonio, J.; Kraka, E. Iron–Histidine Bonding in Bishistidyl Hemoproteins – a Local Vibrational Mode Study. J. Comput. Chem. 2024, 45, 574–588. https://doi.org/10.1002/jcc.27267.Suche in Google Scholar PubMed
33. Freindorf, M.; Kraka, E. A Closer Look at the FeS Heme Bonds in Azatobacter Vinelandii Bacterioferritin: QM/MM and Local Mode Analysis. J. Comput. Chem. 2025, 46, e70012; https://doi.org/10.1002/jcc.70012.Suche in Google Scholar PubMed PubMed Central
34. Freindorf, M.K. F.; Fleming, K.; Kraka, E. Iron-Histidine Coordination in Cytochrome b5: A Local Vibrational Mode Study. ChemPhysChem 2025, 26, e202401098; https://doi.org/10.1002/cphc.202401098.Suche in Google Scholar PubMed
35. Freindorf, M.; Kraka, E. Metal-ligand and Hydrogen Bonding in the Active Site of Fe(III)-, Mn(III)- and Co(III)-myoglobins. Dalton Trans. 2025, 54, 4096–4111; https://doi.org/10.1039/D4DT03246B.Suche in Google Scholar PubMed
36. Verma, N.; Tao, Y.; Zou, W.; Chen, X.; Chen, X.; Freindorf, M.; Kraka, E. A Critical Evaluation of Vibrational Stark Effect (VSE) Probes with the Local Vibrational Mode Theory. Sensors 2020, 20, 2358. https://doi.org/10.3390/s20082358.Suche in Google Scholar PubMed PubMed Central
37. Machado, F. C.; Quintano, M.; Santos, C. V.Jr.; Neto, A. N. C.; Kraka, E.; Longo, R. L.; Moura, R. T.Jr. Theoretical Insights into the Vibrational Spectra and Chemical Bonding of Ln(III) Complexes with a Tripodal N4O3 Ligand Along the Lanthanide Series. Phys. Chem. Chem. Phys. 2025, 27, 17984–1803. https://doi.org/10.1039/d4cp03677h.Suche in Google Scholar PubMed
38. Quintano, M.; Delgado, A. A. A.; MouraJr.R. T.; Freindorf, M.; Kraka, E. Local Mode Analysis of Characteristic Vibrational Coupling in Nucleobases and Watson–Crick Base Pairs of DNA. Electron. Struct. 2022, 4, 044005-1–044005-17. https://doi.org/10.1088/2516-1075/acaa7a.Suche in Google Scholar
39. Quintano, M.; Moura, R. T.Jr.; Kraka, E. Exploring Jahn-Teller Distortions: A Local Vibrational Mode Perspective. J. Mol. Model. 2024, 30, 102-1–102-12. https://doi.org/10.1007/s00894-024-05882-8.Suche in Google Scholar PubMed PubMed Central
40. Cremer, D.; Kraka, E. From Molecular Vibrations to Bonding, Chemical Reactions, and Reaction Mechanism. Curr. Org. Chem. 2010, 14, 1524–1560. https://doi.org/10.2174/138527210793563233.Suche in Google Scholar
41. Kraka, E.; Larsson, J. A.; Cremer, D. Generalization of the Badger Rule Based on the Use of Adibatic Vibrational Modes. In Computational Spectroscopy; Grunenberg, J., Ed.; Wiley: New York, 2010; pp 105–149.10.1002/9783527633272.ch4Suche in Google Scholar
42. Mayer, I. Charge, Bond Brder an Valence in the Ab Initio Theory. Chem. Phys. Lett. 1983, 97, 270–274. https://doi.org/10.1016/0009-2614(83)80005-0.Suche in Google Scholar
43. Mayer, I. Bond Orders and Valences from ab Initio Wave Functions. Int. J. Quantum Chem. 1986, 29, 477–483. https://doi.org/10.1002/qua.560290320.Suche in Google Scholar
44. Mayer, I. Bond Order and Valence Indices: A Personal Account. J. Comput. Chem. 2007, 28, 204–221. https://doi.org/10.1002/jcc.20494.Suche in Google Scholar PubMed
45. Bader, R. F. W. Atoms in Molecules. Acc. Chem. Res. 1985, 18, 9–15. https://doi.org/10.1021/ar00109a003.Suche in Google Scholar
46. Bader, R. F. W. Atoms in Molecules: A Quantum Theory; Clarendon Press: Oxford, 1995.Suche in Google Scholar
47. Popelier, P. Atoms in Molecules: An Introduction; Prentice-Hall: Harlow, England, 2000.Suche in Google Scholar
48. Cremer, D.; Kraka, E. Chemical Bonds Without Bonding Electron Density? Does the Difference Electron-Density Analysis Suffice for a Description of the Chemical Bond? Angew. Chem., Int. Ed. 1984, 23, 627–628. https://doi.org/10.1002/anie.198406271.Suche in Google Scholar
49. Cremer, D.; Kraka, E. A Description of the Chemical Bond in Terms of Local Properties of Electron Density and Energy. Croat. Chem. Acta 1984, 57, 1259–1281.Suche in Google Scholar
50. Reed, A. E.; Weinstock, R. B.; Weinhold, F. Natural Population Analysis. J. Chem. Phys. 1985, 83, 735–746. https://doi.org/10.1063/1.449486.Suche in Google Scholar
51. Eswar, N.; Webb, B.; Marti‐Renom, M. A.; Madhusudhan, M.; Eramian, D.; Shen, M.-y.; Pieper, U.; Sali, A. Comparative Protein Structure Modeling Using Modeller. Curr. Protoc. Bioinf. 2006, 15, 5.6.1–5.6.30. https://doi.org/10.1002/0471250953.bi0506s15.Suche in Google Scholar PubMed PubMed Central
52. Meng, E. C.; Goddard, T. D.; Pettersen, E. F.; Couch, G. S.; Pearson, Z. J.; Morris, J. H.; Ferrin, T. E. UCSF ChimeraX: Tools for Structure Building and Analysis. Protein Sci. 2023, 32, e4792. https://doi.org/10.1002/pro.4792.Suche in Google Scholar PubMed PubMed Central
53. Scouras, A. D.; Daggett, V. The Dynameomics Rotamer Library: Amino Acid Side Chain Conformations and Dynamics from Comprehensive Molecular Dynamics Simulations in Water. Protein Sci. 2011, 20, 341–352. https://doi.org/10.1002/pro.565.Suche in Google Scholar PubMed PubMed Central
54. Li, P.; Merz, K. M. J. MCPB.py: A Python Based Metal Center Parameter Builder. J. Chem. Inf. Model. 2016, 56, 599–604. https://doi.org/10.1021/acs.jcim.5b00674.Suche in Google Scholar
55. Chai, J.-D.; Head-Gordon, M. Long-Range Corrected Hybrid Density Functionals with Damped atom-atom Dispersion Corrections. Phys. Chem. Chem. Phys. 2008, 10, 6615–6620. https://doi.org/10.1039/B810189B.Suche in Google Scholar
56. Ditchfield, R.; Hehre, W.; Pople, J. Self-Consistent Molecular-Orbital Methods. IX. an Extended Gaussian-Type Basis for Molecular-Orbital Studies of Organic Molecules. J. Chem. Phys. 1971, 54, 724–728. https://doi.org/10.1063/1.1674902.Suche in Google Scholar
57. Hariharan, P.; Pople, J. The Influence of Polarization Functions on Molecular Orbital Hydrogenation Energies. Thermochim. Acta 1973, 28, 213–222. https://doi.org/10.1007/BF00533485.Suche in Google Scholar
58. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery, J. A.Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B.; Fox, D. J. Gaussian16 Revision C.01; Gaussian Inc: Wallingford CT, 2016.Suche in Google Scholar
59. Seminario, J. M. Calculation of Intramolecular Force Fields from Second-Derivative Tensors. Int. J. Quantum Chem. 1996, 60, 1271–1277. https://doi.org/10.1002/(SICI)1097-461X(1996)60:7%3C1271::AID-QUA8%3E3.0.CO;2-W.10.1002/(SICI)1097-461X(1996)60:7<1271::AID-QUA8>3.0.CO;2-WSuche in Google Scholar
60. Singh, U. C.; Kollman, P. A. An Approach to Computing Electrostatic Charges for Molecules. J. Comput. Chem. 1984, 5, 129–145. https://doi.org/10.1002/jcc.540050204.Suche in Google Scholar
61. Bayly, C. I.; Cieplak, P.; Cornell, W.; Kollman, P. A. A Well-Behaved Electrostatic Potential Based Method Using Charge Restraints for Deriving Atomic Charges: The RESP Model. J. Phys. Chem. 1993, 97, 10269–10280. https://doi.org/10.1021/j100142a004.Suche in Google Scholar
62. Tian, C.; Kasavajhala, K.; Belfon, K. A. A.; Raguette, L.; Huang, H.; Migues, A. N.; Bickel, J.; Wang, Y.; Pincay, J.; Wu, Q.; Simmerling, C. ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained Against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020, 16, 528–552. https://doi.org/10.1021/acs.jctc.9b00591.Suche in Google Scholar
63. Anandakrishnan, R.; Aguilar, B.; Onufriev, A. V. H++ 3.0: Automating pK Prediction and the Preparation of Biomolecular Structures for Atomistic Molecular Modeling and Simulations. Nucleic Acids Res. 2012, 40, 537–541. https://doi.org/10.1093/nar/gks375.Suche in Google Scholar
64. Gordon, J. C.; Myers, J. B.; Folta, T.; Shoja, V.; Heath, L. S.; Onufriev, A. H++: A Server for Estimating P Ka S and Adding Missing Hydrogens to Macromolecules. Nucleic Acids Res. 2005, 33, 368–371. https://doi.org/10.1093/nar/gki464.Suche in Google Scholar
65. Myers, J.; Grothaus, G.; Narayanan, S.; Onufriev, A. A Simple Clustering Algorithm Can be Accurate Enough for Use in Calculations of pKs in Macromolecules. Proteins: Struct., Funct., Bioinf. 2006, 63, 928–938. https://doi.org/10.1002/prot.20922.Suche in Google Scholar PubMed
66. Case, D.; Aktulga, H.; Belfon, K.; Ben-Shalom, I.; Berryman, J.; Brozell, S.; Cerutti, D.; III, T. C.; Cisneros, G.; Cruzeiro, V.; Darden, T.; Forouzesh, N.; Ghazimirsaeed, M.; Giambaşu, G.; Giese, T.; Gilson, M.; Gohlke, H.; Goetz, A.; Harris, J.; Huang, S. I. Z.; Izmailov, S.; Kasavajhala, K.; Kaymak, M.; Kovalenko, A.; Kurtzman, T.; Lee, T.; Li, P.; Li, Z.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Machado, M.; Manathunga, M.; Merz, K.; Miao, Y.; Mikhailovskii, O.; Monard, G.; Nguyen, H.; O’Hearn, K.; Onufriev, A.; Pan, F.; Pantano, S.; Rahnamoun, A.; Roe, D.; Roitberg, A.; Sagui, C.; Schott-Verdugo, S.; Shajan, A.; Shen, J.; Simmerling, C.; Skrynnikov, N.; Smith, J.; Swails, J.; Walker, R.; Wang, J.; Wang, J.; Wu, X.; Wu, Y.; Xiong, Y.; Xue, Y.; York, D.; Zhao, C.; Zhu, Q.; Kollman, P. AMBER 2024; University of California: San Francisco, USA, 2024.Suche in Google Scholar
67. Tao, P.; Schlegel, H. B. A Toolkit to Assist ONIOM Calculations. J. Comput. Chem. 2010, 31, 2363–2369. https://doi.org/10.1002/jcc.21524.Suche in Google Scholar PubMed
68. Vreven, T.; Frisch, M. J.; Kudin, K. N.; Schlegel, H. B.; Morokuma, K. Geometry Optimization with QM/MM Methods II: Explicit Quadratic Coupling. Mol. Phys. 2006, 104, 701–714. https://doi.org/10.1080/00268970500417846.Suche in Google Scholar
69. Bacskay, G. B. A Quadratically Convergent Hartree – Fock (QC-SCF) Method. Application to Closed Shell Systems. Chem. Phys. 1981, 61, 385–404. https://doi.org/10.1016/0301-0104(81)85156-7.Suche in Google Scholar
70. Zou, W.; Moura, R.Jr.; Santos, C. V.Jr.; Quintano, M.; Bodo, F.; Freindorf, M.; Cremer, D.; Kraka, E. LModeA2025, Computational and Theoretical Chemistry Group (CATCO); Southern Methodist University: Dallas, TX, USA, 2025.Suche in Google Scholar
71. Keith, T. A. AIMAll (Version 19.10.12); TK Gristmill Software: Overland Park KS, USA, 2019.Suche in Google Scholar
72. Glendening, E. D.; Badenhoop, J. K.; Reed, A. E.; Carpenter, J. E.; Bohmann, J. A.; Morales, C. M.; Karafiloglou, P.; Landis, C. R.; Weinhold, F. NBO 7.0. Theoretical Chemistry Institute; University of Wisconsin: Madison, 2018.Suche in Google Scholar
73. Kraka, E.; Cremer, D. Chemical Implication of Local Features of the Electron Density Distribution. In Theoretical Models of Chemical Bonding. The Concept of the Chemical Bond; Maksic, Z. B., Ed.; Springer Verlag: Heidelberg, Vol. 2, 1990; pp 453–542.10.1515/9783112611746-013Suche in Google Scholar
74. Kraka, E.; Cremer, D. Dieter Cremer’s Contribution to the Field of Theoretical Chemistry. Int. J. Quantum Chem. 2019, 119, e25849. https://doi.org/10.1002/qua.25849.Suche in Google Scholar
75. Tao, Y.; Zou, W.; Nanayakkara, S.; Freindorf, M.; Kraka, E. A Revised Formulation of the Generalized Subsystem Vibrational Analysis (GSVA). Theor. Chem. Acc. 2021, 140, 31-1–31-5. https://doi.org/10.1007/s00214-021-02727-y.Suche in Google Scholar PubMed PubMed Central
76. Moura, R. T.Jr.; Quintano, M.; Antonio, J. J.; Freindorf, M.; Kraka, E. Automatic Generation of Local Vibrational Mode Parameters: from Small to Large Molecules and QM/MM Systems. J. Phys. Chem. A 2022, 126, 9313–9331. https://doi.org/10.1021/acs.jpca.2c07871.Suche in Google Scholar PubMed
77. Quintano, M.; Moura, R. T.Jr.; Kraka, E. Frontier Article: Local Vibrational Mode Theory Meets Graph Theory: Complete and Non-Redundant Local Mode Sets. Chem. Phys. Lett. 2024, 849, 141416-1–1141416-13. https://doi.org/10.1016/j.cplett.2024.141416.Suche in Google Scholar
78. Ding, K.; Yin, S.; Li, Z.; Jiang, S.; Yang, Y.; Zhou, W.; Zhang, Y.; Huang, B. Observing Noncovalent Interactions in Experimental Electron Density for Macromolecular Systems: A Novel Perspective for Protein – Ligand Interaction Research. J. Chem. Inf. Model. 2022, 62, 1734–1743. https://doi.org/10.1021/acs.jcim.1c01406.Suche in Google Scholar PubMed
79. Van Stappen, C.; Deng, Y.; Liu, Y.; Heidari, H.; Wang, J.-X.; Zhou, Y.; Ledray, A. P.; Lu, Y. Designing Artificial Metalloenzymes by Tuning of the Environment Beyond the Primary Coordination Sphere. Chem. Rev. 2022, 122, 11974–12045. https://doi.org/10.1021/acs.chemrev.2c00106.Suche in Google Scholar PubMed PubMed Central
80. Adhav, V. A.; Saikrishnan, K. The Realm of Unconventional Noncovalent Interactions in Proteins: Their Significance in Structure and Function. ACS Omega 2023, 8, 22268–22284. https://doi.org/10.1021/acsomega.3c00205.Suche in Google Scholar PubMed PubMed Central
81. Jeong, W. J.; Lee, J.; Eom, H.; Song, W. J. A Specific Guide for Metalloenzyme Designers: Introduction and Evolution of Metal-Coordination Spheres Embedded in Protein Environments. Acc. Chem. Res. 2023, 56, 2416–2425. https://doi.org/10.1021/acs.accounts.3c00336.Suche in Google Scholar PubMed
82. Kumbhar, S.; Fischer, F. D.; Waller, M. P. Assessment of Weak Intermolecular Interactions Across QM/MM Noncovalent Boundaries. J. Chem. Inf. Model. 2012, 52, 93–98. https://doi.org/10.1021/ci200406s.Suche in Google Scholar PubMed
83. Humphrey, W.; Dalke, A.; Schulten, K. VMD – Visual Molecular Dynamics. J. Mol. Graphics 1996, 14, 33–38. https://doi.org/10.1016/0263-7855(96)00018-5.Suche in Google Scholar PubMed
84. Cremer, D.; Pople, J. A. General Definition of Ring Puckering Coordinates. J. Am. Chem. Soc. 1975, 97, 1354–1358; https://doi.org/10.1021/ja00839a011.Suche in Google Scholar
85. Böselt, L.; Thürlemann, M.; Riniker, S. Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems. J. Chem. Theory Comput. 2021, 17, 2641–2658. https://doi.org/10.1021/acs.jctc.0c01112.Suche in Google Scholar PubMed
86. Bereau, T.; DiStasio, R. A.Jr.; Tkatchenko, A.; von Lilienfeld, O. A. Non-Covalent Interactions Across Organic and Biological Subsets of Chemical Space: Physics-Based Potentials Parametrized from Machine Learning. J. Chem. Phys. 2018, 148, 241706. https://doi.org/10.1063/1.5009502.Suche in Google Scholar PubMed
87. Cao, Y.; Romero, J.; Olson, J. P.; Degroote, M.; Johnson, P. D.; Kieferová, M.; Kivlichan, I. D.; Menke, T.; Peropadre, B.; Sawaya, N. P. D.; Sim, S.; Veis, L.; Aspuru-Guzik, A. Quantum Chemistry in the Age of Quantum Computing. Chem. Rev. 2019, 119, 10856–10915. https://doi.org/10.1021/acs.chemrev.8b00803.Suche in Google Scholar PubMed
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/pac-2025-0454).
© 2025 IUPAC & De Gruyter