Computational nanochemistry study of the alisporivir and cyclosporin antimicrobial peptides through conceptual DFT-based computational peptidology and pharmacokinetics
Resumen
This paper reports the results of a computational nanochemistry study of the chemical reactivities and bioactivity properties of two antimicrobial peptides using a CDFT-based computational peptidology (CDFT-CP) methodology, which is derived from the combination of the chemical reactivity descriptors derived from conceptual density functional theory (CDFT) and some cheminformatics tools useful in the design of therapeutic drugs. This is complemented by an examination of the bioactivity and pharmacokinetics indices of the peptides in relation to the ADMET (absorption, distribution, metabolism, excretion, and toxicity) features. These findings provide further evidence of the superiority of the MN12SX density functional in fulfilling the Janak and ionization energy theorems using an earlier proposed KID methodology for validation. This has proven to be beneficial in accurately predicting CDFT indices, which is of help in the understanding of the chemical reactivity. The computational pharmacokinetics study revealed the potential ability of both cyclopeptides as therapeutic drugs through the interaction with different target receptors. The ADMET indices confirmed this assertion through the absence of toxicity and good absorption and distribution properties.
Citas
Ayers, P. and Parr, R. (2000). The variational principles for describing chemical reactions: The Fukui function and chemical hardness revisited. Journal of the American Chemical Society, 122: 2010-2018. https://doi.org/10.1021/ja9924039
Bajorath, J. (ed.) (2014). Chemoinformatics for drug discovery. WI- LEY, A. New Jersey: John Wiley & Sons Publication, Hoboken.
Begam, B. F. and Kumar, J. S. (2012). A study on cheminformatics and its applications on modern drug discovery. Procedia Engineering, 38: 1264-1275. https://doi.org/10.1016/j.proeng.2012.06.156
Benjamin, B. (2015). Basic principles of drug discovery and development. Amsterdam Netherlands: Academic Press.
Chakraborty, A., Pan, S. and Chattaraj, P. K. (2012). Biological activity and toxicity: A conceptual DFT approach. In Structure and Bonding. Berlin, Heidelberg: Springer, 143-179.
Chattaraj, P., Chakraborty, A. and Giri, S. (2009). Net electrophilicity. Journal of Physical Chemistry A, 113(37): 10068-10074. https://doi.org/10.1021/jp904674x
Cramer, C. (2004). Essentials of computational chemistry – Theories and models, 2nd ed. Chichester, England: John Wiley & Sons.
Daina, A., Michielin, O. and Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1). https://doi.org/10.1038/srep42717
Daina, A., Michielin, O. and Zoete, V. (2019). Swiss target prediction: Updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1): W357-W364. https://doi.org/10.1093/nar/gkz382
Domingo, L. R., Aurell, M., Pérez, P. and Contreras, R. (2002). Quantitative characterization of the global electrophilicity power of common diene/dienophile pairs in Diels-Alder reactions. Tetrahedron, 58(22): 4417-4423. https://doi.org/10.1016/S0040-4020(02)00410-6
Domingo, L. R., Chamorro, E. and Pérez, P. (2008). Understanding the reactivity of captodative ethylenes in polar cycloaddition reactions. a theoretical study. The Journal of Organic Chemistry, 73(12): 4615-4624. https://doi.org/10.1021/jo800572a
Domingo, L. R. and Pérez, P. (2011). The nucleophilicity n index in organic chemistry. Organic and Biomolecular Chemistry, 9: 7168-7175. https://doi.org/10.1039/C1OB05856H
Domingo, L. R., Ríos-Gutiérrez, M. and Pérez, P. (2016). Applications of the conceptual density functional theory indices to organic chemistry reactivity. Molecules, 21: 748. https://doi.org/10.3390/molecules21060748
Domingo, L. R. and Sáez, J. A. (2009). Understanding the mechanism of polar Diels-Alder reactions. Organic and Biomolecular Chemistry, 7(17): 3576-3583. https://doi.org/10.1039/B909611F
Engel, T. and Gasteiger, J., (eds.) (2018a). Applied chemoinformatics: achievements and future opportunities. Weinheim, Germany: Wiley-VCH.
Engel, T. and Gasteiger, J., (eds.) (2018b). Chemoinformatics: basic concepts and methods. Wiley-VCH, Weinheim.
Flores-Holguín, N., Frau, J. and Glossman-Mitnik, D. (2020a). A fast and simple evaluation of the chemical reactivity properties of the pristinamycin family of antimicrobial peptides. Chemical Physics Letters, 739: 137021. https://doi.org/10.1016/j.cplett.2019.137021
Flores-Holguín, N., Frau, J. and Glossman-Mitnik, D. (2020b). Conceptual DFT-based computational peptidology of marine natural compounds: discodermins A–H. Molecules, 25(18): 4158. https://doi.org/10.3390/molecules25184158
Flores-Holguín, N., Frau, J. and Glossman-Mitnik, D. (2020c). Virtual screening of marine natural compounds by means of chemoinformatics and CDFT-based computational peptidology. Marine Drugs, 18(9): 478. https://doi.org/10.3390/md18090478
Flores-Holguín, N., Frau, J. and Glossman-Mitnik, D. (2021a). A CDFT-based computational peptidology (CDFT-CP) Study of the chemical reactivity and bioactivity of the marine-derived alternaramide cyclopentadepsipeptide. Journal of Chemistry, 2021: 1-11. https://doi.org/10.1155/2021/2989611
Flores-Holguín, N., Frau, J. and Glossman-Mitnik, D. (2021b). Conceptual DFT as a helpful chemoinformatics tool for the study of the clavanin family of antimicrobial marine peptides. In De Lazaro, S. R., Da Silveira Lacerda, L. H. and Pontes Ribeiro, R. A. (eds.), Density functional theory. London, UK: IntechOpen, chap. 3, 57-67.
Frau, J., Hernández-Haro, N. and Glossman-Mitnik, D. (2017). Computational prediction of the pKas of small peptides through conceptual DFT descriptors. Chemical Physics Letters, 671: 138-141. https://doi.org/10.1016/j.cplett.2017.01.038
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, Jr., J. A., 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. and Fox, D. J. (2016). Gaussian 16 Revision C.01. Gaussian Inc. Wallingford CT.
Gázquez, J., Cedillo, A. and Vela, A. (2007). Electrodonating and electroaccepting powers. Journal of Physical Chemistry A, 111(10): 1966-1970. https://doi.org/10.1021/jp065459f
Geerlings, P., Chamorro, E., Chattaraj, P. K., Proft, F. D., Gázquez, J. L., Liu, S., Morell, C., Toro-Labbé, A., Vela, A. and Ayers, P. (2020). Conceptual density functional theory: status, prospects, issues. Theoretical Chemistry Accounts, 139(2): 36. https://doi.org/10.1021/jp065459f
Geerlings, P., De Proft, F. and Langenaeker, W. (2003). Conceptual density functional theory. Chemical Reviews, 103: 1793-1873. https://doi.org/10.1021/cr990029p
Guha, R. and Bender, A., (eds.) (2012). Computational approaches in cheminformatics and bioinformatics. Wiley, Hoboken, N. J.
Halgren, T. A. (1996a). Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry, 17(5-6): 490-519. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6C490::AID-JCC13E3.0.CO;2-P
Halgren, T. A. (1996b). Merck Molecular Force Field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions. Journal of Computational Chemistry, 17(5-6): 520-552. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/63C520::AID-JCC23E3.0.CO;2-W
Halgren, T. A. (1996c). Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules. Journal of Computational Chemistry, 17(5-6): 616-641. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/63C616::AID-JCC53E3.0.CO;2-X
Halgren, T. A. (1999). MMFF VI. MMFF94s Option for energy minimization studies. Journal of Computational Chemistry, 20(7): 720-729. https://doi.org/10.1002/(SICI)1096-987X(199905)20:73C720::AID-JCC73E3.0.CO;2-X
Halgren, T. A. and Nachbar, R. B. (1996). Merck molecular force field. IV. Conformational energies and geometries for MMFF94. Journal of Computational Chemistry, 17(5-6): 587-615. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/63C587::AID-JCC43E3.0.CO;2-Q
Janak, J. (1978). Proof that ∂E/∂ni = E in density functional theory. Physical Review B, 18: 7165-7168. https://doi.org/10.1103/PhysRevB.18.7165
Jaramillo, P., Domingo, L. R., Chamorro, E. and Pérez, P. (2008). A further exploration of a nucleophilicity index based on the gas-phase ionization potentials. Journal of Molecular Structure: THEOCHEM, 865(1-3): 68-72. https://doi.org/10.1016/j.theochem.2008.06.022
Jensen, F. (2007). Introduction to computational chemistry, 2nd ed. Chichester, England: John Wiley & Sons.
Kanchanakungwankul, S. and Truhlar, D. G. (2021). Examination of how well long-range-corrected density functionals satisfy the ionization energy theorem. Journal of Chemical Theory and Computation, 17(8): 4823-4830. https://doi.org/10.1021/acs.jctc.1c00440
Kar, R., Song, J.-W. and Hirao, K. (2013). Long-range corrected functionals satisfy Koopmans’ theorem: calculation of correlation and relaxation energies. Journal of Computational Chemistry, 34(11): 958-964. https://doi.org/10.1002/jcc.23222
Lampel, A., Ulijn, R. V. and Tuttle, T. (2018). Guiding principles for peptide nanotechnology through directed discovery. Chemical Society Reviews, 47(10): 3737-3758. https://doi.org/10.1039/C8CS00177D
Lewars, E. (2003). Computational chemistry – Introduction to the theory and applications of molecular and quantum mechanics. Dordrecht: Kluwer Academic Publishers.
Mahlapuu, M., Håkansson, J., Ringstad, L. and Björn, C. (2016). Antimicrobial peptides: an emerging category of therapeutic agents. Frontiers in Cellular and Infection Microbiology, 6(104): 1-12. https://doi.org/10.3389/fcimb.2016.00194
Marenich, A., Cramer, C. J. and Truhlar, D. G. (2009). Universal solvation model based on solute electron density and a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. Journal of Physical Chemistry B, 113: 6378-6396. https://doi.org/10.1021/jp810292n
Martínez-Araya, J. I. (2012). Explaining reaction mechanisms using the dual descriptor: a complementary tool to the molecular electrostatic potential. Journal of Molecular Modeling, 19(7): 2715-2722. https://doi.org/10.1007/s00894-012-1520-2
Martínez-Araya, J. I. (2012). Revisiting caffeate’s capabilities as a complexation agent to silver cation in mining processes by means of the dual descriptor – A conceptual DFT approach. Journal of Molecular Modeling, 18: 4299-4307. https://doi.org/10.1007/s00894-012-1405-4
Martínez-Araya, J. I. (2015). Why is the dual descriptor a more accurate local reactivity descriptor than Fukui functions? Journal of Mathematical Chemistry, 53(2): 451-465. https://doi.org/10.1007/s10910-014-0437-7
Medina-Franco, J. L. and Saldívar-González, F. I. (2020). Cheminformatics to characterize pharmacologically active natural products. Biomolecules, 10(11): 1566. https://doi.org/10.3390/biom10111566
Morell, C., Grand, A. and Toro-Labbé, A. (2005). New dual descriptor for chemical reactivity. Journal of Physical Chemistry A, 109: 205-212. https://doi.org/10.1021/jp046577a
Morell, C., Grand, A. and Toro-Labbé, A. (2006). Theoretical support for using the ∆f (r) descriptor. Chemical Physics Letters, 425: 342-346. https://doi.org/10.1016/j.cplett.2006.05.003
Parr, R. and Yang, W. (1989). Density-functional theory of atoms and molecules. New York Oxford University Press.
Pawlotsky, J.-M. (2020). COVID-19 pandemic: time to revive the cyclophilin inhibitor alisporivir. Clinical Infectious Diseases, 71(16): 2191-2194. https://doi.org/10.1093/cid/ciaa587
Pérez, P., Domingo, L. R., Aurell, M. J. and Contreras, R. (2003). Quantitative characterization of the global electrophilicity pattern of some reagents involved in 1,3-dipolar cycloaddition reactions. Tetrahedron, 59(17): 3117-3125. https://doi.org/10.1016/S0040-4020(03)00374-0
Peverati, R. and Truhlar, D. G. (2012). Screened-exchange desity functionals with broad accuracy for chemistry and solid-state physics. Physical Chemistry Chemical Physics, 14(47): 16187-16191. https://doi.org/10.1039/C2CP42576A
Pires, D. E. V., Blundell, T. L., and Ascher, D. B. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9): 4066-4072. https://doi.org/10.1021/acs.jmedchem.5b00104
Poater, A., Saliner, A. G., Carbó-Dorca, R., Poater, J., Solá, M., Cavallo, L. and Worth, A. P. (2009). Modeling the structure-property relationships of nanoneedles: a journey toward nanomedicine. Journal of Computational Chemistry, 30(2): 275-284. https://doi.org/10.1002/jcc.21041
Poater, A., Saliner, A. G., Solá, M., Cavallo, L. and Worth, A. P. (2010). Computational methods to predict the reactivity of nanoparticles through structure-property relationships. Expert Opinion on Drug Delivery, 7(3): 295-305. https://doi.org/10.1517/17425240903508756
Toro-Labbé, A. (ed.) (2007). Theoretical aspects of chemical reactivity. Amsterdam: Elsevier Science.
Tsuneda, T. and Hirao K. (2014). Long-range correction for density functional theory. Wiley Interdisciplinary Reviews: Computational Molecular Science, 4(4): 375-390. https://doi.org/10.1002/wcms.1178
Tsuneda, T., Song, J.-W., Suzuki, S. and Hirao, K. (2010). On Koopmans’ theorem in density functional theory. The Journal of Chemical Physics, 133(17): 174101. https://doi.org/10.1063/1.3491272
Ulijn, R. V. and Jerala, R. (2018). Peptide and protein nanotechnology into the 2020s: Beyond biology. Chemical Society Reviews, 47(10): 3391-3394. https://doi.org/10.1039/C8CS90055H
Varnek, A. and Tropsha, A. (eds.) (2008). Chemoinformatics approaches to virtual screening. Cambridge, UK: Royal Society of Chemistry.
Weigend, F. (2006). Accurate coulomb-fitting basis sets for H to R. Physical Chemistry Chemical Physics, 8: 1057-1065. https://doi.org/10.1039/B515623H
Weigend, F. and Ahlrichs, R. (2005). Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. Physical Chemistry Chemical Physics, 7: 3297-3305. https://doi.org/https://doi.org/10.1039/B508541A
Young, D. (2001). Computational chemistry – A practical guide for applying techniques to real-world problems. New York: John Wiley & Sons.
Mundo Nano. Revista Interdisciplinaria en Nanociencias y Nanotecnología, editada por la Universidad Nacional Autónoma de México, se distribuye bajo una Licencia Creative Commons Atribución-NoComercial 4.0 Internacional.
Basada en una obra en http://www.mundonano.unam.mx.
