Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs
Abstract
1. Introduction
2. Methodology
2.1. Density Functional Theory (DFT) Calculations
2.2. Computational Pharmacokinetics and ADMET Report
3. Results and Discussion
3.1. Conceptual DFT-Based Computational Peptidology
3.2. Computational Pharmacokinetics and ADMET Report
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Vasarri, M.; Biasi, A.M.D.; Barletta, E.; Pretti, C.; Degl’Innocenti, D. An Overview of New Insights into the Benefits of the Seagrass Posidonia oceanica for Human Health. Mar. Drugs 2021, 19, 476. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Catanesi, M.; Caioni, G.; Castelli, V.; Benedetti, E.; d’Angelo, M.; Cimini, A. Benefits under the Sea: The Role of Marine Compounds in Neurodegenerative Disorders. Mar. Drugs 2021, 19, 24. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Müller, W.E.G.; Schröder, H.C.; Wang, X. (Eds.) Blue Biotechnology; Springer International Publishing: Cham, Switzerland, 2017. [Google Scholar] [CrossRef] [Scilit]
- D’Annessa, I.; Leva, F.S.D.; Teana, A.L.; Novellino, E.; Limongelli, V.; Marino, D.D. Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We? Front. Mol. Biosci. 2020, 7, 66. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Zorzi, A.; Deyle, K.; Heinis, C. Cyclic Peptide Therapeutics: Past, Present and Future. Curr. Opin. Chem. Biol. 2017, 38, 24–29. [Google Scholar] [CrossRef] [Scilit]
- Joo, S.H. Cyclic Peptides as Therapeutic Agents and Biochemical Tools. Biomol. Ther. 2012, 20, 19–26. [Google Scholar] [CrossRef] [Scilit]
- Gang, D.; Kim, D.; Park, H.S. Cyclic Peptides: Promising Scaffolds for Biopharmaceuticals. Genes 2018, 9, 557. [Google Scholar] [CrossRef] [Scilit]
- Sivanathan, S.; Scherkenbeck, J. Cyclodepsipeptides: A Rich Source of Biologically Active Compounds for Drug Research. Molecules 2014, 19, 12368–12420. [Google Scholar] [CrossRef] [Scilit]
- Salvador, L.A.; Biggs, J.S.; Paul, V.J.; Luesch, H. Veraguamides A–G, Cyclic Hexadepsipeptides from a Dolastatin 16-Producing Cyanobacterium Symploca cf. hydnoides from Guam. J. Nat. Prod. 2011, 74, 917–927. [Google Scholar] [CrossRef] [Scilit]
- Qamar, H.; Hussain, K.; Soni, A.; Khan, A.; Hussain, T.; Chénais, B. Cyanobacteria as Natural Therapeutics and Pharmaceutical Potential: Role in Antitumor Activity and as Nanovectors. Molecules 2021, 26, 247. [Google Scholar] [CrossRef] [Scilit]
- Gogineni, V.; Hamann, M.T. Marine Natural Product Peptides with Therapeutic Potential: Chemistry, Biosynthesis, and Pharmacology. Biochim. Biophys. Acta Gen. Subj. 2018, 1862, 81–196. [Google Scholar] [CrossRef] [Scilit]
- Kang, H.K.; Choi, M.C.; Seo, C.H.; Park, Y. Therapeutic Properties and Biological Benefits of Marine-Derived Anticancer Peptides. Int. J. Mol. Sci. 2018, 19, 919. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Mi, Y.; Zhang, J.; He, S.; Yan, X. New Peptides Isolated from Marine Cyanobacteria, an Overview over the Past Decade. Mar. Drugs 2017, 15, 132. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Negi, B.; Kumar, D.; Rawat, D.S. Marine Peptides as Anticancer Agents: A Remedy to Mankind by Nature. Curr. Protein Pept. Sci. 2017, 18, 885–904. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Flores-Holguín, N.; Frau, J.; Glossman-Mitnik, D. A Fast and Simple Evaluation of the Chemical Reactivity Properties of the Pristinamycin Family of Antimicrobial Peptides. Chem. Phys. Lett. 2020, 739, 137021. [Google Scholar] [CrossRef] [Scilit]
- Flores-Holguín, N.; Frau, J.; Glossman-Mitnik, D. Conceptual DFT-Based Computational Peptidology of Marine Natural Compounds: Discodermins A–H. Molecules 2020, 25, 4158. [Google Scholar] [CrossRef] [Scilit]
- Flores-Holguín, N.; Frau, J.; Glossman-Mitnik, D. Virtual Screening of Marine Natural Compounds by Means of Chemoinformatics and CDFT-Based Computational Peptidology. Mar. Drugs 2020, 18, 478. [Google Scholar] [CrossRef] [Scilit]
- Flores-Holguín, N.; Frau, J.; Glossman-Mitnik, D. Conceptual DFT as a Helpful Chemoinformatics Tool for the Study of the Clavanin Family of Antimicrobial Marine Peptides. In Density Functional Theory; Chapter 3; De Lazaro, S.R., Da Silveira Lacerda, L.H., Pontes Ribeiro, R.A., Eds.; IntechOpen: London, UK, 2021; pp. 57–67. [Google Scholar]
- Flores-Holguín, N.; Frau, J.; Glossman-Mitnik, D. A CDFT-Based Computational Peptidology (CDFT-CP) Study of the Chemical Reactivity and Bioactivity of the Marine-Derived Alternaramide Cyclopentadepsipeptide. J. Chem. 2021, 2021, 2989611. [Google Scholar] [CrossRef] [Scilit]
- Janak, J. Proof that ∂E/∂ni = ϵ in Density Functional Theory. Phys. Rev. B 1978, 18, 7165–7168. [Google Scholar] [CrossRef] [Scilit]
- Kar, R.; Song, J.W.; Hirao, K. Long-Range Corrected Functionals Satisfy Koopmans’ Theorem: Calculation of Correlation and Relaxation Energies. J. Comput. Chem. 2013, 34, 958–964. [Google Scholar] [CrossRef] [Scilit]
- Tsuneda, T.; Song, J.W.; Suzuki, S.; Hirao, K. On Koopmans’ Theorem in Density Functional Theory. J. Chem. Phys. 2010, 133, 174101. [Google Scholar] [CrossRef] [Scilit]
- Tsuneda, T.; Hirao, K. Long-Range Correction for Density Functional Theory. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2014, 4, 375–390. [Google Scholar] [CrossRef] [Scilit]
- Kanchanakungwankul, S.; Truhlar, D.G. Examination of How Well Long-Range-Corrected Density Functionals Satisfy the Ionization Energy Theorem. J. Chem. Theory Comput. 2021, 17, 4823–4830. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Parr, R.; Yang, W. Density-Functional Theory of Atoms and Molecules; Oxford University Press: New York, NY, USA, 1989. [Google Scholar]
- Chermette, H. Chemical Reactivity Indexes in Density Functional Theory. J. Comput. Chem. 1999, 20, 129–154. [Google Scholar] [CrossRef]
- Geerlings, P.; De Proft, F.; Langenaeker, W. Conceptual Density Functional Theory. Chem. Rev. 2003, 103, 1793–1873. [Google Scholar] [CrossRef] [Scilit]
- Gázquez, J.; Cedillo, A.; Vela, A. Electrodonating and Electroaccepting Powers. J. Phys. Chem. A 2007, 111, 1966–1970. [Google Scholar] [CrossRef] [Scilit]
- Chattaraj, P.; Chakraborty, A.; Giri, S. Net Electrophilicity. J. Phys. Chem. A 2009, 113, 10068–10074. [Google Scholar] [CrossRef] [Scilit]
- Geerlings, P.; Chamorro, E.; Chattaraj, P.K.; Proft, F.D.; Gázquez, J.L.; Liu, S.; Morell, C.; Toro-Labbé, A.; Vela, A.; Ayers, P. Conceptual Density Functional Theory: Status, Prospects, Issues. Theor. Chem. Accounts 2020, 139, 36. [Google Scholar] [CrossRef] [Scilit]
- Engel, T.; Gasteiger, J. (Eds.) Applied Chemoinformatics: Achievements and Future Opportunities; Wiley-VCH: Weinheim, Germany, 2018. [Google Scholar]
- Engel, T.; Gasteiger, J. (Eds.) Chemoinformatics: Basic Concepts and Methods; Wiley-VCH: Weinheim, Germany, 2018. [Google Scholar]
- Bajorath, J. (Ed.) Chemoinformatics for Drug Discovery; WILEY, A John Wiley & Sons Publication: Hoboken, NJ, USA, 2014. [Google Scholar]
- Varnek, A.; Tropsha, A. (Eds.) Chemoinformatics Approaches to Virtual Screening; Royal Society of Chemistry: Cambridge, UK, 2008. [Google Scholar]
- Guha, R.; Bender, A. (Eds.) Computational Approaches in Cheminformatics and Bioinformatics; Wiley: Hoboken, NJ, USA, 2012. [Google Scholar]
- Benjamin, B. Basic Principles of Drug Discovery and Development; Academic Press: Amsterdam, The Netherlands, 2015. [Google Scholar]
- Medina-Franco, J.L.; Saldívar-González, F.I. Cheminformatics to Characterize Pharmacologically Active Natural Products. Biomolecules 2020, 10, 1566. [Google Scholar] [CrossRef] [Scilit]
- Begam, B.F.; Kumar, J.S. A Study on Cheminformatics and its Applications on Modern Drug Discovery. Procedia Eng. 2012, 38, 1264–1275. [Google Scholar] [CrossRef] [Scilit]
- Daina, A.; Michielin, O.; Zoete, V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef] [Scilit]
- Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J. Med. Chem. 2015, 58, 4066–4072. [Google Scholar] [CrossRef] [Scilit]
- Chakraborty, A.; Pan, S.; Chattaraj, P.K. Biological Activity and Toxicity: A Conceptual DFT Approach. In Structure and Bonding; Springer: Berlin/Heidelberg, Germany, 2012; pp. 143–179. [Google Scholar]
- Lewars, E. Computational Chemistry—Introduction to the Theory and Applications of Molecular and Quantum Mechanics; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2003. [Google Scholar]
- Young, D. Computational Chemistry—A Practical Guide for Applying Techniques to Real-World Problems; John Wiley & Sons: New York, NY, USA, 2001. [Google Scholar]
- Jensen, F. Introduction to Computational Chemistry, 2nd ed.; John Wiley & Sons: Chichester, UK, 2007. [Google Scholar]
- Cramer, C. Essentials of Computational Chemistry—Theories and Models, 2nd ed.; John Wiley & Sons: Chichester, UK, 2004. [Google Scholar]
- Domingo, L.R.; Chamorro, E.; Perez, P. Understanding the Reactivity of Captodative Ethylenes in Polar Cycloaddition Reactions. A Theoretical Study. J. Org. Chem. 2008, 73, 4615–4624. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Jaramillo, P.; Domingo, L.R.; Chamorro, E.; Pérez, P. A Further Exploration of a Nucleophilicity Index Based on the Gas-Phase Ionization Potentials. J. Mol. Struct. THEOCHEM 2008, 865, 68–72. [Google Scholar] [CrossRef] [Scilit]
- Domingo, L.R.; Sáez, J.A. Understanding the Mechanism of Polar Diels-Alder Reactions. Org. Biomol. Chem. 2009, 7, 3576–3583. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Domingo, L.R.; Perez, P. The Nucleophilicity N Index in Organic Chemistry. Org. Biomol. Chem. 2011, 9, 7168–7175. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Domingo, L.R.; Ríos-Gutiérrez, M.; Pérez, P. Applications of the Conceptual Density Functional Theory Indices to Organic Chemistry Reactivity. Molecules 2016, 21, 748. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Halgren, T.A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94. J. Comput. Chem. 1996, 17, 490–519. [Google Scholar] [CrossRef] [Scilit]
- Halgren, T.A. Merck Molecular Force Field. II. MMFF94 van der Waals and Electrostatic Parameters for Intermolecular Interactions. J. Comput. Chem. 1996, 17, 520–552. [Google Scholar] [CrossRef] [Scilit]
- Halgren, T.A. MMFF VI. MMFF94s Option for Energy Minimization Studies. J. Comput. Chem. 1999, 20, 720–729. [Google Scholar] [CrossRef] [Scilit]
- Halgren, T.A.; Nachbar, R.B. Merck Molecular Force Field. IV. Conformational Energies and Geometries for MMFF94. J. Comput. Chem. 1996, 17, 587–615. [Google Scholar] [CrossRef] [Scilit]
- Halgren, T.A. Merck Molecular Force field. V. Extension of MMFF94 Using Experimental Data, Additional Computational Data, and Empirical Rules. J. Comput. Chem. 1996, 17, 616–641. [Google Scholar] [CrossRef] [Scilit]
- 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.; et al. Gaussian 16 Revision C.01; Gaussian Inc.: Wallingford, CT, USA, 2016. [Google Scholar]
- Peverati, R.; Truhlar, D.G. Screened-Exchange Density Functionals with Broad Accuracy for Chemistry and Solid-State Physics. Phys. Chem. Chem. Phys. 2012, 14, 16187–16191. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Weigend, F.; Ahlrichs, R. Balanced Basis Sets of Split Valence, Triple Zeta Valence and Quadruple Zeta Valence Quality for H to Rn: Design and Assessment of Accuracy. Phys. Chem. Chem. Phys. 2005, 7, 3297–3305. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Weigend, F. Accurate Coulomb-fitting Basis Sets for H to R. Phys. Chem. Chem. Phys. 2006, 8, 1057–1065. [Google Scholar] [CrossRef] [Scilit]
- Marenich, A.; Cramer, C.; Truhlar, D. 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. J. Phys. Chem. B 2009, 113, 6378–6396. [Google Scholar] [CrossRef] [Scilit]
- Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: Updated Data and New Features for Efficient Prediction of Protein Targets of Small Molecules. Nucleic Acids Res. 2019, 47, W357–W364. [Google Scholar] [CrossRef] [Scilit]
- Becke, A. Density-Functional Thermochemistry. III. The Role of Exact Exchange. J. Chem. Phys. 1993, 98, 5648–5652. [Google Scholar] [CrossRef] [Scilit]
- Lee, C.; Yang, W.; Parr, R. Development of the Colle-Salvetti Correlation-Energy Formula into a Functional of the Electron Density. Phys. Rev. B 1988, 37, 785–789. [Google Scholar] [CrossRef] [Scilit]
- Vosko, S.; Wilk, L.; Nusair, M. Accurate Spin-Dependent Electron Liquid Correlation Energies for Local Spin Density Calculations: A Critical Analysis. Can. J. Phys. 1980, 58, 1200–1211. [Google Scholar] [CrossRef] [Scilit]
- Adamo, C.; Barone, V. Toward Reliable Density Functional Methods without Adjustable Parameters: The PBE0 Model. Chem. Phys. 1999, 110, 6158–6169. [Google Scholar] [CrossRef] [Scilit]
- Ernzerhof, M.; Scuseria, G.E. Assessment of the Perdew-Burke-Ernzerhof Exchange-Correlation Functional. J. Chem. Phys. 1999, 110, 5029–5036. [Google Scholar] [CrossRef] [Scilit]
- Becke, A. Density-Functional Exchange-Energy Approximation with Correct Asymptotic-Behavior. Phys. Rev. A 1988, 38, 3098–3100. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Stephens, P.; Devlin, F.; Frisch, M.; Chabalowski, C. Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields. J. Phys. Chem. 1994, 98, 11623–11627. [Google Scholar] [CrossRef] [Scilit]
- Perdew, J.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77, 3865–3868. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Iikura, H.; Tsuneda, T.; Yanai, T.; Hirao, K. A Long-Range Correction Scheme for Generalized-Gradient-Approximation Exchange Functionals. J. Chem. Phys. 2001, 115, 3540–3544. [Google Scholar] [CrossRef] [Scilit]
- Yanai, T.; Tew, D.P.; Handy, N.C. A New Hybrid Exchange-Correlation Functional Using the Coulomb-Attenuating Method (CAM-B3LYP). Chem. Phys. Lett. 2004, 393, 51–57. [Google Scholar] [CrossRef] [Scilit]
- Henderson, T.M.; Izmaylov, A.F.; Scalmani, G.; Scuseria, G.E. Can Short-Range Hybrids Describe Long-Range-Dependent Properties? J. Chem. Phys. 2009, 131, 044108. [Google Scholar] [CrossRef] [Scilit]
- Chai, J.; Head-Gordon, M. Long-Range Corrected Hybrid Density Functionals with Damped Atom-Atom Dispersion Corrections. Phys. Chem. Chem. Phys. 2008, 10, 6615–6620. [Google Scholar] [CrossRef] [Scilit]
- Brémond, É.; Pérez-Jiménez, Á.J.; Sancho-García, J.C.; Adamo, C. Range-Separated Hybrid Density Functionals Made Simple. J. Chem. Phys. 2019, 150, 201102. [Google Scholar] [CrossRef] [Scilit]
- Domingo, L.R.; Aurell, M.; Pérez, P.; Contreras, R. Quantitative Characterization of the Global Electrophilicity Power of Common diene/Dienophile Pairs in Diels-Alder Reactions. Tetrahedron 2002, 58, 4417–4423. [Google Scholar] [CrossRef] [Scilit]
- Pérez, P.; Domingo, L.R.; Aurell, M.J.; Contreras, R. Quantitative Characterization of the Global Electrophilicity Pattern of Some Reagents Involved in 1,3-Dipolar Cycloaddition Reactions. Tetrahedron 2003, 59, 3117–3125. [Google Scholar] [CrossRef] [Scilit]
- Toro-Labbé, A. (Ed.) Theoretical Aspects of Chemical Reactivity; Elsevier Science: Amsterdam, The Netherlands, 2007. [Google Scholar]
- Morell, C.; Grand, A.; Toro-Labbé, A. New Dual Descriptor for Chemical Reactivity. J. Phys. Chem. A 2005, 109, 205–212. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Morell, C.; Grand, A.; Toro-Labbé, A. Theoretical Support for Using the Δf(r) Descriptor. Chem. Phys. Lett. 2006, 425, 342–346. [Google Scholar] [CrossRef] [Scilit]
- Martínez-Araya, J.I. Revisiting Caffeate’s Capabilities as a Complexation Agent to Silver Cation in Mining Processes by means of the Dual Descriptor—A Conceptual DFT Approach. J. Mol. Model. 2012, 18, 4299–4307. [Google Scholar] [CrossRef] [Scilit]
- Martínez-Araya, J.I. Explaining Reaction Mechanisms Using the Dual Descriptor: A Complementary Tool to the Molecular Electrostatic Potential. J. Mol. Model. 2012, 19, 2715–2722. [Google Scholar] [CrossRef] [Scilit]
- Martínez-Araya, J.I. Why is the Dual Descriptor a More Accurate Local Reactivity Descriptor than Fukui Functions? J. Math. Chem. 2015, 53, 451–465. [Google Scholar] [CrossRef] [Scilit]
- Frau, J.; Hernández-Haro, N.; Glossman-Mitnik, D. Computational Prediction of the pKas of Small Peptides through Conceptual DFT Descriptors. Chem. Phys. Lett. 2017, 671, 138–141. [Google Scholar] [CrossRef] [Scilit]




| Solvent | 1/ | LC-BLYP | LC-PBE | MN12SX | CAM-B3LYP | LC-HPBE |
|---|---|---|---|---|---|---|
| NMF | 0.0055 | 4.17 | 4.16 | 0.02 | 2.15 | 3.88 |
| Formamide | 0.0092 | 4.18 | 4.17 | 0.02 | 2.16 | 3.89 |
| H2O | 0.0128 | 4.16 | 4.16 | 0.01 | 2.15 | 3.87 |
| Methanol | 0.0307 | 4.09 | 4.08 | 0.05 | 2.08 | 3.80 |
| Ethanol | 0.0402 | 4.00 | 3.99 | 0.11 | 2.00 | 3.81 |
| Acetone | 0.0488 | 3.73 | 3.72 | 0.27 | 1.77 | 3.45 |
| DCE | 0.0988 | 3.62 | 3.61 | 0.37 | 1.65 | 3.33 |
| THF | 0.1347 | 3.45 | 3.44 | 0.48 | 1.50 | 3.16 |
| DBE | 0.3282 | 2.95 | 2.94 | 0.85 | 1.03 | 2.67 |
| Cyclohexane | 0.4959 | 2.56 | 2.55 | 1.14 | 0.66 | 2.29 |
| n-Hexane | 0.5314 | 2.48 | 2.47 | 2.03 | 0.58 | 2.21 |
| Gas | 1.0000 | 1.36 | 1.35 | 2.08 | 0.48 | 1.09 |
| Solvent | 1/ | B97XD | M11 | RSX-PBE | RSX-PBE0 | RSX-PBE0-1/3 |
| NMF | 0.0055 | 2.91 | 3.54 | 4.06 | 4.24 | 4.32 |
| Formamide | 0.0092 | 2.92 | 3.55 | 4.08 | 4.25 | 4.33 |
| H2O | 0.0128 | 2.90 | 3.53 | 4.06 | 4.23 | 4.32 |
| Methanol | 0.0307 | 2.84 | 3.46 | 3.99 | 4.16 | 4.24 |
| Ethanol | 0.0402 | 2.76 | 3.38 | 3.38 | 4.07 | 4.16 |
| Acetone | 0.0488 | 2.53 | 3.12 | 3.01 | 3.80 | 3.88 |
| DCE | 0.0988 | 2.42 | 3.01 | 2.89 | 3.68 | 3.77 |
| THF | 0.1347 | 2.26 | 2.84 | 2.72 | 3.51 | 3.59 |
| DBE | 0.3282 | 1.80 | 2.36 | 2.60 | 3.02 | 3.10 |
| Cyclohexane | 0.4959 | 1.43 | 1.98 | 2.47 | 2.63 | 2.71 |
| n-Hexane | 0.5314 | 1.35 | 1.90 | 2.39 | 2.55 | 2.63 |
| Gas | 1.0000 | 0.28 | 0.79 | 1.26 | 1.42 | 1.50 |
| Molecule | HOMO | LUMO | SOMO | H-L Gap | J(I) | J(A) | J(HL) | SL |
|---|---|---|---|---|---|---|---|---|
| Veraguamide A | −6.635 | −0.884 | −0.892 | 5.751 | 0.006 | 0.003 | 0.006 | 0.003 |
| Veraguamide B | −6.637 | −0.942 | −0.912 | 5.695 | 0.008 | 0.012 | 0.014 | 0.030 |
| Veraguamide C | −6.812 | −0.993 | −0.946 | 5.819 | 0.003 | 0.017 | 0.017 | 0.048 |
| Veraguamide D | −6.661 | −0.748 | −0.759 | 5.913 | 0.010 | 0.005 | 0.011 | 0.011 |
| Veraguamide E | −6.873 | −0.717 | −0.702 | 6.156 | 0.002 | 0.007 | 0.007 | 0.014 |
| Veraguamide F | −6.717 | −1.016 | −0.972 | 5.702 | 0.002 | 0.015 | 0.015 | 0.019 |
| Veraguamide G | −6.697 | −0.771 | −0.768 | 5.926 | 0.009 | 0.001 | 0.009 | 0.003 |
| Molecule | S | N | ||||||
|---|---|---|---|---|---|---|---|---|
| Veraguamide A | 3.760 | 5.751 | 1.229 | 0.174 | 2.157 | 4.697 | 0.937 | 5.634 |
| Veraguamide B | 3.790 | 5.695 | 1.261 | 0.176 | 2.156 | 4.772 | 0.983 | 5.755 |
| Veraguamide C | 3.903 | 5.819 | 1.309 | 0.172 | 1.981 | 4.932 | 1.030 | 5.962 |
| Veraguamide D | 3.704 | 5.913 | 1.160 | 0.169 | 2.132 | 4.543 | 0.838 | 5.381 |
| Veraguamide E | 3.795 | 6.156 | 1.170 | 0.162 | 1.920 | 4.621 | 0.827 | 5.448 |
| Veraguamide F | 3.867 | 5.702 | 1.311 | 0.175 | 2.075 | 4.912 | 1.045 | 5.957 |
| Veraguamide G | 3.734 | 5.926 | 1.177 | 0.169 | 2.096 | 4.590 | 0.856 | 5.447 |
| Molecule | pKa |
|---|---|
| Veraguamide A | 12.36 |
| Veraguamide B | 12.40 |
| Veraguamide C | 12.55 |
| Veraguamide D | 12.58 |
| Veraguamide E | 12.60 |
| Veraguamide F | 12.50 |
| Veraguamide G | 12.62 |
| Molecule | GPCR | Ion Channel | Nuclear Receptor | Kinase | Protease | Enzyme |
|---|---|---|---|---|---|---|
| Ligand | Modulator | Ligand | Inhibitor | Inhibitor | Inhibitor | |
| Veraguamide A | −0.42 | −1.45 | −1.21 | −1.18 | 0.05 | −0.83 |
| Veraguamide B | −0.33 | −1.31 | −1.07 | −1.02 | 0.13 | −0.73 |
| Veraguamide C | −0.29 | −1.21 | −1.06 | −0.96 | 0.20 | −0.62 |
| Veraguamide D | −0.42 | −1.39 | −1.22 | −1.18 | 0.12 | −0.76 |
| Veraguamide E | −0.55 | −1.57 | −1.38 | −1.35 | 0.02 | −0.91 |
| Veraguamide F | −0.64 | −1.78 | −1.55 | −1.43 | −0.09 | −1.09 |
| Veraguamide G | −0.35 | −1.32 | −1.12 | −1.04 | 0.13 | −0.72 |
| Property | Veraguamides | ||||||
|---|---|---|---|---|---|---|---|
| A | B | C | D | E | F | G | |
| HI Absorption | + | + | + | + | + | + | + |
| BBB Permeability | + | + | + | + | + | + | + |
| Caco-2 | - | - | - | - | - | - | - |
| P-gp Substrate | + | + | + | + | + | + | + |
| P-gp Inhibitor | + | + | + | + | + | + | + |
| CYP2C9 Substrate | - | - | - | - | - | + | - |
| CYP2D6 Substrate | - | - | - | - | - | - | - |
| CYP3A4 Substrate | + | + | + | + | + | + | + |
| CYP1A2 Inhibitor | - | - | - | - | - | - | - |
| CYP2C19 Inhibitor | - | - | - | - | - | - | - |
| CYP2C9 Inhibitor | - | - | - | - | - | - | - |
| CYP2D6 Inhibitor | - | - | - | - | - | - | - |
| CYP3A4 Inhibitor | - | - | - | - | - | - | - |
| OCT2 Substrate | - | - | - | - | - | - | - |
| AMES Toxicity | - | - | - | - | - | - | - |
| hERG Inhibitor | - | - | - | - | - | - | - |
| Hepatotoxicity | + | + | + | + | + | + | + |
| Skin Sensitization | - | - | - | - | - | - | - |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Flores-Holguín, N.; Ortega-Castro, J.; Frau, J.; Glossman-Mitnik, D. Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs. Mar. Drugs 2022, 20, 97. https://doi.org/10.3390/md20020097
Flores-Holguín N, Ortega-Castro J, Frau J, Glossman-Mitnik D. Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs. Marine Drugs. 2022; 20(2):97. https://doi.org/10.3390/md20020097
Chicago/Turabian StyleFlores-Holguín, Norma, Joaquín Ortega-Castro, Juan Frau, and Daniel Glossman-Mitnik. 2022. "Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs" Marine Drugs 20, no. 2: 97. https://doi.org/10.3390/md20020097
APA StyleFlores-Holguín, N., Ortega-Castro, J., Frau, J., & Glossman-Mitnik, D. (2022). Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs. Marine Drugs, 20(2), 97. https://doi.org/10.3390/md20020097

