Structure-Guided Approach for the Development of MUC1-Glycopeptide-Based Cancer Vaccines with Predictable Responses

  1. Bermejo, Iris A. 4
  2. Guerreiro, Ana 2
  3. Eguskiza, Ander 1
  4. Martínez-Sáez, Nuria 411
  5. Lazaris, Foivos S. 4
  6. Asín, Alicia 4
  7. Somovilla, Víctor J. 4
  8. Compañón, Ismael 4
  9. Raju, Tom K. 5
  10. Tadic, Srdan 5
  11. Garrido, Pablo 5
  12. García-Sanmartín, Josune 5
  13. Mangini, Vincenzo 12
  14. Grosso, Ana S. 910
  15. Marcelo, Filipa 910
  16. Avenoza, Alberto 4
  17. Busto, Jesús H. 4
  18. García-Martín, Fayna 4
  19. Hurtado-Guerrero, Ramón 678
  20. Peregrina, Jesús M. 4
  21. Bernardes, Gonçalo J. L. 23
  22. Martínez, Alfredo 5
  23. Fiammengo, Roberto 112
  24. Corzana, Francisco 4
  1. 1 Department of Biotechnology, University of Verona, Verona 37134, Italy
  2. 2 Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa 1649-028, Portugal
  3. 3 Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.
  4. 4 Department of Chemistry and Instituto de Investigación en Química de la Universidad de La Rioja (IQUR), Universidad de La Rioja, Logroño 26006, Spain
  5. 5 Angiogenesis Group, Oncology Area, Center for Biomedical Research of La Rioja (CIBIR), Logroño 26006, Spain
  6. 6 Institute of Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza 50018, Spain
  7. 7 Copenhagen Center for Glycomics, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, University of Copenhagen, Copenhagen 2200, Denmark
  8. 8 Fundación ARAID, Zaragoza 50018, Spain
  9. 9 Applied Molecular Biosciences Unit UCIBIO, Department of Chemistry, NOVA School of Science and Technology, Caparica 2829-516, Portugal
  10. 10 Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Caparica 2829-516, Portugal
  11. 11 Departamento de Tecnología y Química Farmacéuticas, Universidad de Navarra, Pamplona 31008, Spain
  12. 12 Center for Biomolecular Nanotechnologies@UniLe, Istituto Italiano di Tecnologia (IIT), Arnesano, Lecce 73010, Italy
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Revista:
JACS Au

ISSN: 2691-3704,  2691-3704

Año de publicación: 2024

Volumen: 4

Número: 1

Páginas: 150-163

Tipo: Artículo

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DOI: 10.1021/JACSAU.3C00587 GOOGLE SCHOLAR lock_openAcceso abierto editor

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Resumen

Mucin-1 (MUC1) glycopeptides are exceptional candidates for potential cancer vaccines. However, their autoantigenic nature often results in a weak immune response. To overcome this drawback, we carefully engineered synthetic antigens with precise chemical modifications. To be effective and stimulate an anti-MUC1 response, artificial antigens must mimic the conformational dynamics of natural antigens in solution and have an equivalent or higher binding affinity to anti-MUC1 antibodies than their natural counterparts. As a proof of concept, we have developed a glycopeptide that contains noncanonical amino acid (2S,3R)-3-hydroxynorvaline. The unnatural antigen fulfills these two properties and effectively mimics the threonine-derived antigen. On the one hand, conformational analysis in water shows that this surrogate explores a landscape similar to that of the natural variant. On the other hand, the presence of an additional methylene group in the side chain of this analog compared to the threonine residue enhances a CH/π interaction in the antigen/antibody complex. Despite an enthalpy–entropy balance, this synthetic glycopeptide has a binding affinity slightly higher than that of its natural counterpart. When conjugated with gold nanoparticles, the vaccine candidate stimulates the formation of specific anti-MUC1 IgG antibodies in mice and shows efficacy comparable to that of the natural derivative. The antibodies also exhibit cross-reactivity to selectively target, for example, human breast cancer cells. This investigation relied on numerous analytical (e.g., NMR spectroscopy and X-ray crystallography) and biophysical techniques and molecular dynamics simulations to characterize the antigen–antibody interactions. This workflow streamlines the synthetic process, saves time, and reduces the need for extensive, animal-intensive immunization procedures. These advances underscore the promise of structure-based rational design in the advance of cancer vaccine development.

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Referencias bibliográficas

  • Taylor-Papadimitriou, J.; Burchell, J. M.; Graham, R.; Beatson, R. Latest developments in MUC1 immunotherapy. Biochem. Soc. Trans. 2018, 46, 659– 668, DOI: 10.1042/BST20170400
  • Apostolopoulos, V.; Stojanovska, L.; Gargosky, S. E. MUC1 (CD227): a multi-tasked molecule. Cell. Mol. Life Sci. 2015, 72, 4475– 4500, DOI: 10.1007/s00018-015-2014-z
  • Kufe, D. W. Mucins in cancer: function, prognosis and therapy. Nat. Rev. Cancer 2009, 9, 874– 885, DOI: 10.1038/nrc2761
  • Nath, S.; Mukherjee, P. MUC1: a multifaceted oncoprotein with a key role in cancer progression. Trends Mol. Med. 2014, 20, 332– 342, DOI: 10.1016/j.molmed.2014.02.007
  • Pinho, S. S.; Reis, C. A. Glycosylation in cancer: mechanisms and clinical implications. Nat. Rev. Cancer 2015, 15, 540– 555, DOI: 10.1038/nrc3982
  • González-Ramírez, A. M.; Grosso, A. S.; Yang, Z.; Compañón, I.; Coelho, H.; Narimatsu, Y.; Clausen, H.; Marcelo, F.; Corzana, F.; Hurtado-Guerrero, R. Structural basis for the synthesis of the core 1 structure by C1GalT1. Nat. Commun. 2022, 13, 2398, DOI: 10.1038/s41467-022-29833-0
  • Karsten, U. Binding patterns of DTR-specific antibodies reveal a glycosylation-conditioned tumor-specific epitope of the epithelial mucin (MUC1). Glycobiology 2004, 14, 681– 692, DOI: 10.1093/glycob/cwh090
  • Yoshimura, Y.; Denda-Nagai, K.; Takahashi, Y.; Nagashima, I.; Shimizu, H.; Kishimoto, T.; Noji, M.; Shichino, S.; Chiba, Y.; Irimura, T. Products of Chemoenzymatic Synthesis Representing MUC1 Tandem Repeat Unit with T-, ST- or STn-antigen Revealed Distinct Specificities of Anti-MUC1 Antibodies. Sci. Rep. 2019, 9, 16641, DOI: 10.1038/s41598-019-53052-1
  • Coelho, H.; Matsushita, T.; Artigas, G.; Hinou, H.; Cañada, F. J.; Lo-Man, R.; Leclerc, C.; Cabrita, E. J.; Jiménez-Barbero, J.; Nishimura, S.-I. The Quest for Anticancer Vaccines: Deciphering the Fine-Epitope Specificity of Cancer-Related Monoclonal Antibodies by Combining Microarray Screening and Saturation Transfer Difference NMR. J. Am. Chem. Soc. 2015, 137, 12438– 12441, DOI: 10.1021/jacs.5b06787
  • Ju, T.; Otto, V. I.; Cummings, R. D. The Tn antigen-structural simplicity and biological complexity. Angew. Chem., Int. Ed. 2011, 50, 1770– 1791, DOI: 10.1002/anie.201002313
  • Blixt, O.; Bueti, D.; Burford, B.; Allen, D.; Julien, S.; Hollingsworth, M.; Gammerman, A.; Fentiman, I.; Taylor-Papadimitriou, J.; Burchell, J. M. Autoantibodies to aberrantly glycosylated MUC1 in early stage breast cancer are associated with a better prognosis. Breast Cancer Res. 2011, 13, R25, DOI: 10.3389/fimmu.2022.1035402
  • Chen, H.; Werner, S.; Tao, S.; Zörnig, I.; Brenner, H. Blood autoantibodies against tumor-associated antigens as biomarkers in early detection of colorectal cancer. Cancer Lett. 2014, 346, 178– 187, DOI: 10.1016/j.canlet.2014.01.007
  • Wilson, R. M.; Danishefsky, S. J. A Vision for Vaccines Built from Fully Synthetic Tumor-Associated Antigens: From the Laboratory to the Clinic. J. Am. Chem. Soc. 2013, 135, 14462– 14472, DOI: 10.1021/ja405932r
  • Wolfert, M. A.; Boons, G. J. Adaptive immune activation: glycosylation does matter. Nat. Chem. Biol. 2013, 9, 776– 784, DOI: 10.1038/nchembio.1403
  • Buskas, T.; Thompson, P.; Boons, G.-J. Immunotherapy for cancer: synthetic carbohydrate-based vaccines. Chem. Commun. 2009, 5335– 5349, DOI: 10.1039/b908664c
  • Gaidzik, N.; Westerlind, U.; Kunz, H. The development of synthetic antitumour vaccines from mucin glycopeptide antigens. Chem. Soc. Rev. 2013, 42, 4421– 4442, DOI: 10.1039/c3cs35470a
  • Stergiou, N.; Urschbach, M.; Gabba, A.; Schmitt, E.; Kunz, H.; Besenius, P. The Development of Vaccines from Synthetic Tumor-Associated Mucin Glycopeptides and their Glycosylation-Dependent Immune Response. Chem. Rec. 2021, 21, 3313– 3331, DOI: 10.1002/tcr.202100182
  • Feng, D.; Shaikh, A. S.; Wang, F. Recent Advance in Tumor-associated Carbohydrate Antigens (TACAs)-based Antitumor Vaccines. ACS Chem. Biol. 2016, 11, 850– 863, DOI: 10.1021/acschembio.6b00084
  • Pifferi, C.; Aguinagalde, L.; Ruiz-de-Angulo, A.; Sacristán, N.; Baschirotto, P. T.; Poveda, A.; Jiménez-Barbero, J.; Anguita, J.; Fernández-Tejada, A. Development of synthetic, self-adjuvanting, and self-assembling anticancer vaccines based on a minimal saponin adjuvant and the tumor-associated MUC1 antigen. Chem. Sci. 2023, 14, 3501– 3513, DOI: 10.1039/D2SC05639A
  • Roy, R.; Mousavifar, L. Carrier diversity and chemical ligations in the toolbox for designing tumor-associated carbohydrate antigens (TACAs) as synthetic vaccine candidates. Chem. Soc. Rev. 2023, 52, 3353– 3396, DOI: 10.1039/D2CS01032A
  • Gao, T.; Cen, Q.; Lei, H. A review on development of MUC1-based cancer vaccine. Biomed. Pharmacother. 2020, 132, 110888 DOI: 10.1016/j.biopha.2020.110888
  • Ro̷mer, T. B.; Aasted, M. K. M.; Dabelsteen, S.; Groen, A.; Schnabel, J.; Tan, E.; Pedersen, J. W.; Haue, A. D.; Wandall, H. H. Mapping of truncated O-glycans in cancers of epithelial and non-epithelial origin. Br. J. Cancer 2021, 125, 1239– 1250, DOI: 10.1038/s41416-021-01530-7
  • Asín, A.; García-Martín, F.; Busto, H. J.; Avenoza, A.; Peregrina, M. J.; Corzana, F. Structure-based Design of Anti-cancer Vaccines: The Significance of Antigen Presentation to Boost the Immune Response. Curr. Med. Chem. 2021, 29, 1258– 1270, DOI: 10.2174/0929867328666210810152917
  • Martinez-Saez, N.; Peregrina, J. M.; Corzana, F. Principles of mucin structure: implications for the rational design of cancer vaccines derived from MUC1-glycopeptides. Chem. Soc. Rev. 2017, 46, 7154– 7175, DOI: 10.1039/C6CS00858E
  • Nativi, C.; Papi, F.; Roelens, S. Tn antigen analogues: the synthetic way to “upgrade” an attracting tumour associated carbohydrate antigen (TACA). Chem. Commun. 2019, 55, 7729– 7736, DOI: 10.1039/C9CC02920F
  • Richichi, B.; Thomas, B.; Fiore, M.; Bosco, R.; Qureshi, H.; Nativi, C.; Renaudet, O.; BenMohamed, L. A Cancer Therapeutic Vaccine based on Clustered Tn-Antigen Mimetics Induces Strong Antibody-Mediated Protective Immunity. Angew. Chem., Int. Ed. 2014, 53, 11917– 11920, DOI: 10.1002/anie.201406897
  • Martinez-Saez, N.; Supekar, N. T.; Wolfert, M. A.; Bermejo, I. A.; Hurtado-Guerrero, R.; Asensio, J. L.; Jimenez-Barbero, J.; Busto, J. H.; Avenoza, A.; Boons, G. J. Mucin architecture behind the immune response: design, evaluation and conformational analysis of an antitumor vaccine derived from an unnatural MUC1 fragment. Chem. Sci. 2016, 7, 2294– 2301, DOI: 10.1039/C5SC04039F
  • Companon, I.; Guerreiro, A.; Mangini, V.; Castro-Lopez, J.; Escudero-Casao, M.; Avenoza, A.; Busto, J. H.; Castillon, S.; Jimenez-Barbero, J.; Asensio, J. L. Structure-Based Design of Potent Tumor-Associated Antigens: Modulation of Peptide Presentation by Single-Atom O/S or O/Se Substitutions at the Glycosidic Linkage. J. Am. Chem. Soc. 2019, 141, 4063– 4072, DOI: 10.1021/jacs.8b13503
  • Bermejo, I. A.; Navo, C. D.; Castro-López, J.; Guerreiro, A.; Jiménez-Moreno, E.; Sánchez Fernández, E. M.; García-Martín, F.; Hinou, H.; Nishimura, S.-I.; García Fernández, J. M. Synthesis, conformational analysis and in vivo assays of an anti-cancer vaccine that features an unnatural antigen based on an sp2-iminosugar fragment. Chem. Sci. 2020, 11, 3996– 4006, DOI: 10.1039/C9SC06334J
  • Martinez-Saez, N.; Castro-Lopez, J.; Valero-Gonzalez, J.; Madariaga, D.; Companon, I.; Somovilla, V. J.; Salvado, M.; Asensio, J. L.; Jimenez-Barbero, J.; Avenoza, A. Deciphering the Non-Equivalence of Serine and Threonine O-Glycosylation Points: Implications for Molecular Recognition of the Tn Antigen by an anti-MUC1 Antibody. Angew. Chem., Int. Ed. 2015, 54, 9830– 9834, DOI: 10.1002/anie.201502813
  • Plattner, C.; Höfener, M.; Sewald, N. One-Pot Azidochlorination of Glycals. Org. Lett. 2011, 13, 545– 547, DOI: 10.1021/ol102750h
  • Tovillas, P.; García, I.; Oroz, P.; Mazo, N.; Avenoza, A.; Corzana, F.; Jiménez-Osés, G.; Busto, J. H.; Peregrina, J. M. Tn Antigen Mimics by Ring-Opening of Chiral Cyclic Sulfamidates with Carbohydrate C1-S- and C1-O-Nucleophiles. J. Org. Chem. 2018, 83, 4973– 4980, DOI: 10.1021/acs.joc.7b03225
  • Corzana, F.; Busto, J. H.; Jiménez-Osés, G.; García De Luis, M.; Asensio, J. L.; Jiménez-Barbero, J.; Peregrina, J. M.; Avenoza, A. Serine versus Threonine Glycosylation: The Methyl Group Causes a Drastic Alteration on the Carbohydrate Orientation and on the Surrounding Water Shell. J. Am. Chem. Soc. 2007, 129, 9458– 9467, DOI: 10.1021/ja072181b
  • Corzana, F.; Busto, J. H.; Jiménez-Osés, G.; Asensio, J. L.; Jiménez-Barbero, J.; Peregrina, J. M.; Avenoza, A. New Insights into α-GalNAc–Ser Motif: Influence of Hydrogen Bonding versus Solvent Interactions on the Preferred Conformation. J. Am. Chem. Soc. 2006, 128, 14640– 14648, DOI: 10.1021/ja064539u
  • Coltart, D. M.; Royyuru, A. K.; Williams, L. J.; Glunz, P. W.; Sames, D.; Kuduk, S. D.; Schwarz, J. B.; Chen, X.-T.; Danishefsky, S. J.; Live, D. H. Principles of Mucin Architecture: Structural Studies on Synthetic Glycopeptides Bearing Clustered Mono-, Di-, Tri-, and Hexasaccharide Glycodomains. J. Am. Chem. Soc. 2002, 124, 9833– 9844, DOI: 10.1021/ja020208f
  • Dziadek, S.; Griesinger, C.; Kunz, H.; Reinscheid, U. M. Synthesis and Structural Model of an α(2,6)-Sialyl-T Glycosylated MUC1 Eicosapeptide under Physiological Conditions. Chem.─Eur. J. 2006, 12, 4981– 4993, DOI: 10.1002/chem.200600144
  • Rangappa, S.; Artigas, G.; Miyoshi, R.; Yokoi, Y.; Hayakawa, S.; Garcia-Martin, F.; Hinou, H.; Nishimura, S.-I. Effects of the multiple O-glycosylation states on antibody recognition of the immunodominant motif in MUC1 extracellular tandem repeats. MedChemComm 2016, 7, 1102– 1122, DOI: 10.1039/C6MD00100A
  • Blixt, O.; Cló, E.; Nudelman, A. S.; So̷rensen, K. K.; Clausen, T.; Wandall, H. H.; Livingston, P. O.; Clausen, H.; Jensen, K. J. A high-throughput O-glycopeptide discovery platform for seromic profiling. J. Proteome Res. 2010, 9, 5250– 5261, DOI: 10.1021/pr1005229
  • Mayer, M.; Meyer, B. Group epitope mapping by saturation transfer difference NMR to identify segments of a ligand in direct contact with a protein receptor. J. Am. Chem. Soc. 2001, 123, 6108– 6117, DOI: 10.1021/ja0100120
  • Meyer, B.; Peters, T. NMR spectroscopy techniques for screening and identifying ligand binding to protein receptors. Angew. Chem., Int. Ed. 2003, 42, 864– 890, DOI: 10.1002/anie.200390233
  • Macías-León, J.; Bermejo, I. A.; Asín, A.; García-García, A.; Compañón, I.; Jiménez-Moreno, E.; Coelho, H.; Mangini, V.; Albuquerque, I. S.; Marcelo, F. Structural characterization of an unprecedented lectin-like antitumoral anti-MUC1 antibody. Chem. Commun. 2020, 56, 15137– 15140, DOI: 10.1039/D0CC06349E
  • Movahedin, M.; Brooks, T. M.; Supekar, N. T.; Gokanapudi, N.; Boons, G.-J.; Brooks, C. L. Glycosylation of MUC1 influences the binding of a therapeutic antibody by altering the conformational equilibrium of the antigen. Glycobiology 2016, 27, 677– 687, DOI: 10.1093/glycob/cww131
  • Dyson, H. J.; Wright, P. E. Defining Solution Conformations of Small Linear Peptides. Annu. Rev. Biophys. Biophys. Chem. 1991, 20, 519– 538, DOI: 10.1146/annurev.bb.20.060191.002511
  • Andersson, C.; Engelsen, S. B. The mean hydration of carbohydrates as studied by normalized two-dimensional radial pair distributions. J. Mol. Graphics Modell. 1999, 17, 101– 105, DOI: 10.1016/S1093-3263(99)00022-4
  • Pearlman, D. How is an NMR structure best defined? An analysis of molecular dynamics distance-based approaches. J. Biomol. NMR 1994, 4, 1– 16, DOI: 10.1007/BF00178332
  • Tachibana, Y.; Fletcher, G. L.; Fujitani, N.; Tsuda, S.; Monde, K.; Nishimura, S.-I. Antifreeze Glycoproteins: Elucidation of the Structural Motifs That Are Essential for Antifreeze Activity. Angew. Chem., Int. Ed. 2004, 43, 856– 862, DOI: 10.1002/anie.200353110
  • Wiberg, K. B.; Bailey, W. F.; Lambert, K. M.; Stempel, Z. D. The Anomeric Effect: It’s Complicated. J. Org. Chem. 2018, 83, 5242– 5255, DOI: 10.1021/acs.joc.8b00707
  • Stenutz, R.; Carmichael, I.; Widmalm, G.; Serianni, A. S. Hydroxymethyl Group Conformation in Saccharides: Structural Dependencies of 2JHH, 3JHH, and 1JCH Spin–Spin Coupling Constants. J. Org. Chem. 2002, 67, 949– 958, DOI: 10.1021/jo010985i
  • Asensio, J. L.; Ardá, A.; Cañada, F. J.; Jiménez-Barbero, J. Carbohydrate–Aromatic Interactions. Acc. Chem. Res. 2013, 46, 946– 954, DOI: 10.1021/ar300024d
  • Searle, M. S.; Williams, D. H. The cost of conformational order: entropy changes in molecular associations. J. Am. Chem. Soc. 1992, 114, 10690– 10697, DOI: 10.1021/ja00053a002
  • Dokurno, P.; Bates, P. A.; Band, H. A.; Stewart, L. M.; Lally, J. M.; Burchell, J. M.; Taylor-Papadimitriou, J.; Snary, D.; Sternberg, M. J.; Freemont, P. S. Crystal structure at 1.95 A resolution of the breast tumour-specific antibody SM3 complexed with its peptide epitope reveals novel hypervariable loop recognition. J. Mol. Biol. 1998, 284, 713– 728, DOI: 10.1006/jmbi.1998.2209
  • Haji-Ghassemi, O.; Blackler, R. J.; Martin Young, N.; Evans, S. V. Antibody recognition of carbohydrate epitopes†. Glycobiology 2015, 25, 920– 952, DOI: 10.1093/glycob/cwv037
  • Avrameas, S.; Antoine, J. C.; Ternynck, T.; Petit, C. Development of immuneeoglobulin and antibody-forming cells in different stages of the immun response. Ann. Immunol. 1976, 127, 551– 571
  • Lacroix, M.; Haibe-Kains, B.; Hennuy, B.; Laes, J. F.; Lallemand, F.; Gonze, I.; Cardoso, F.; Piccart, M.; Leclercq, G.; Sotiriou, C. Gene regulation by phorbol 12-myristate 13-acetate in MCF-7 and MDA-MB-231, two breast cancer cell lines exhibiting highly different phenotypes. Oncol. Rep. 2004, 12, 701– 707, DOI: 10.3892/or.12.4.701
  • Case, D. A. B.-S. I.Y.; Brozell, S. R.; Cerutti, D. S.; Cheatham, T. E..; III, Cruzeiro, V. W. D.; Darden, T. A.; Duke, R. E.; Ghoreishi, D.; Gilson, M. K.; Gohlke, H.; Goetz, A. W.; Greene, D.; Harris, R.; Homeyer, N.; Izadi, S.; Kovalenko, A.; Kurtzman, T.; Lee, T. S.; LeGrand, S.; Li, P.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Mermelstein, D. J.; Merz, K. M.; Miao, Y.; Monard, G.; Nguyen, C.; Nguyen, H.; Omelyan, I.; Onufriev, A.; Pan, F.; Qi, R.; Roe, D. R.; Roitberg, A.; Sagui, C.; Schott-Verdugo, S.; Shen, J.; Simmerling, C. L.; Smith, J.; Salomon-Ferrer, R.; Swails, J.; Walker, R. C.; Wang, J.; Wei, H.; Wolf, R. M.; Wu, X.; Xiao, L.; York, D. M.; Kollman, P. A. AMBER 2018; University of California: San Francisco, 2018.
  • Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11, 3696– 3713, DOI: 10.1021/acs.jctc.5b00255
  • Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157– 1174, DOI: 10.1002/jcc.20035
  • Kirschner, K. N.; Yongye, A. B.; Tschampel, S. M.; González-Outeiriño, J.; Daniels, C. R.; Foley, B. L.; Woods, R. J. GLYCAM06: a generalizable biomolecular force field Carbohydrates. J. Comput. Chem. 2008, 29, 622– 655, DOI: 10.1002/jcc.20820
  • Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J. Comput. Chem. 2002, 23, 1623– 1641, DOI: 10.1002/jcc.10128
  • Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926– 935, DOI: 10.1063/1.445869
  • Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089– 10092, http://doi.org/10.1063/1.464397