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BY 4.0 license Open Access Published by De Gruyter Open Access December 10, 2019

Conceptual DFT as a Novel Chemoinformatics Tool for Studying the Chemical Reactivity Properties of the Amatoxin Family of Fungal Peptides

  • Norma Flores-Holguín , Juan Frau and Daniel Glossman-Mitnik EMAIL logo
From the journal Open Chemistry

Abstract

The chemical structures and molecular reactivities of the Amatoxin group of fungi-derived peptides have been determined by means of the consideration of a model chemistry that has been previously validated as well-behaved for our purposes. The reactivity descriptors were calculated on the basis of a methodological framework built around the concepts that are the outcome of the so called Conceptual Density Functional Theory (CDFT). This procedure in connection with the different Fukui functions allowed to identify the chemically active regions within the molecules. By considering a simple protocol designed by our research group for the estimation of the pKa of peptides through the information coming from the chemical hardness, these property has been established for the different molecular systems explored in this research. The information reported through this work could be of interest for medicinal chemistry researchers in using this knowledge for the design of new medicines based on the studied peptides or as a help for the understanding of the toxicity mechanisms exerted by them.

1 Introduction

Amatoxins are a group of deadly toxins found in Amanita fungi consisting of nine octapeptides with a bicycle structure which are named as α-amanitin, β-amanitin, γ-amanitin, ϵ-amanitin, amanullinic acid, amanin, amaninamide, amanullin and proamanullin. These toxins are often associated with another group of bicyclic peptides, the phallotoxins, which only contain seven amino acids in the ring [1].

Amatoxins are found in mushrooms of the genera Amanita, Galerina, Lepiota, and Conocybe from the fungal phylum Basidiomycetes. α-amanitin poisoning causes irreversible liver damage and is responsible for 90% of fatal incidents with mushrooms. Amatoxins are synthesized as a 35 amino acid precursor, which is then cleaved, macrocyclized and further modified including the introduction of the covalent tryptathionine bond between a cysteine and a tryptophan, the defining feature of amatoxins. The identity of the enzymes and the order in which these reactions take place remain experimentally undetermined. Precursor peptides containing the N-terminal signature sequence MSDIN have been identified, as well as the protease that catalyzes the cleavage of the leader sequence (POPA) and the macrocyclase enzyme (POPB). These are the only enzymes involved in amanitin biosynthesis characterized to date, and both belong to the prolyl oligopeptidase super-family. An extensive kinetic characterization of the macrocyclase from Galerina marginata revealed similarities with other prolyl oligopeptidases, as well as pronounced product inhibition caused by the long recognition sequence [2, 3].

Chemoinformatics is an important field of research which involves several procedures for the management of chemical information and can be considered a wonderful instrument for the design of new medicines in the pharmaceutical industry. The majority of the applications dependent on Chemoinformatics look to make forecasts about the natural properties of molecular systems starting from a background built over their associated chemical structures, and the computational displaying of them by considering a tight coupling of biological and organic data. For this reason, the connections among biology and chemistry portrayed by the techniques assoaciated to Computational Chemistry are very important. For a given molecular system, the structure and its assoaciated chemical properties may be estimated and visualized on the basis of its related electron density, and it is in this way that molecular descriptors will without a doubt be identified with molecular properties; nonetheless, the degree of this link will rely upon the particular descriptors, properties and group of molecules considered in the analysis [4].

This research seeks to obtain the chemical reactivity information of the fungal peptides under study by means of the consideration of the Density Functional Theory (DFT) derived concepts. There is a lot of research where the Conceptual DFT is used to relate the reactivity of several compounds with biological activity [5, 6, 7, 8, 9, 10, 11]. The understanding of the chemical reactivity properties of the Amatoxin molecules will be crucially achieved by means of the consideration of the Fukui functions to extract the information about the reactivity of the peptides which can be of potential utility in the process of designin new pharmaceutical drugs [12, 13, 14, 15, 16]. The information reported through this work could be of interest for medicinal chemistry researchers in using this knowledge for the developing of new medicines based on the studied peptides or as a help for the understanding of the toxicity mechanisms exerted by them.

2 Computational Methodology

The determination of the conformers of the nine fungal molecules belonging to the Amatoxin group was performed by using the ChemAxon Calculator plugins included in MarvinView 17.15 available from ChemAxon (Budapest,Hungary), a graphical display software considered of utility for the study of chemical structures and reactions. The procedure stated by choosing the most stable conformer for each peptide by doing Molecular Mechanicscalculations through the overall MMFF94 force field [17, 18, 19, 20, 21]. The resulting lowest energy conformers for each peptide obtained during this process were then reoptimized through the Density Functional Tight Binding (DFTBA) functionality accesible within the Gaussian 09 software [22]. By considering the experience acquired in the previous research of our group [11, 23, 24, 25, 26, 27, 28, 29, 30], the model chemistry based on the association of the MN12SX functional with the Def2TZVP basis set using water as the solvent was considered for the final optimization of the resulting molecular structures because it has been shown that it allows the verification of the ’Koopmans in DFT’ (KID) procedure [11, 23, 24, 25, 26, 27, 28, 29, 30]. In the same way, the process for the calculation of the electronic properties and the chemical reactivity descriptors of the fungal peptides involved the use of MN12SX/Def2TZVP/H2O model chemistry through the consideration of the previously optimized molecular structures.

3 Results and Discussion

As mentioned in the Computational Methodology section, the MN12SX/DefTZVP/H2O model chemistry combined with the SMD (Solvent Model Based on the Density) solvent model [31] was used for the calculation of the electronic properties of each peptide after using calculation analysis procedures to determine whether all the structures agree with the minimum energy requirements. All the calculations were performed in the presence of water as the solvent because the potential bioactivity and toxicity of this fungal peptides is intimately related with the absorption, distribution, metabolism and excretion that take place within the organisms. The graphical sketches of the molecular structures of the Amatoxins are shown in Figure 1 below.

Figure 1 Graphical sketches of the molecular structures of a) α-amanitin; b) β-amanitin; c) γ-amanitin; d) ϵ-amanitin; e) amanin, f) amaninamide, g) amanullinic acid, h) amanullin and i) proamanullin
Figure 1

Graphical sketches of the molecular structures of a) α-amanitin; b) β-amanitin; c) γ-amanitin; d) ϵ-amanitin; e) amanin, f) amaninamide, g) amanullinic acid, h) amanullin and i) proamanullin

Following Becke’s ideas [32] and the studies by Baerends et al concluding that the HOMO-LUMOgap of the Kohn-Sham (KS) system can be used as an effective measure of the molecular optical gap [33, 34], ground state calculationswere used for the determination of the maximum absorption wavelength that belongs to the fungal peptides of the Amatoxin family to find the respective λmax values through the application of chosen model chemistry to determine the HOMO-LUMO gaps. As we have shown in our previous research [23, 24, 25, 26, 27, 28, 29], the KID procedure is also valid in the presence of water as the solvent and represents an advantage over the use of the vertical I and A for the calculation of the global descriptors because it avoids the separate calculation of the radical cation and anion which could be difficult for molecules of the size considered here. Therefore, the results for the calculation of the electronic properties of the Amatoxins fungal peptides are displayed Table 1.

Table 1

Electronic energies (in au) of the Amatoxins, HOMO, LUMO and HOMO-LUMO gap (in eV), and the maximum absorption wavelengths λmax (in nm) calculated with the MN12SX/Def2TZVP/H2O model chemistry

MoleculeTotal Electronic EnergyHOMOLUMOHOMO-LUMO Gapλmax
α-amanitin−3512.5105−5.4760−1.16574.3103288
β-amanitin−3532.3745−5.5987−1.35434.2444292
γ-amanitin−3437.4086−5.6172−1.38564.2316293
ϵ-amanitin−3457.2548−5.5495−1.31104.2384293
amanullinic acid−3382.1470−5.5792−1.31294.2662291
amanin−3457.2226−5.8959−1.49444.4014282
amaninamide−3437.3836−5.8730−1.45884.4142281
amanullin−3362.2831−5.5615−1.29724.2643291
proamanullin−3287.1706−5.5547−1.31844.2363293

3.1 Calculation of the Global Reactivity Descriptors of the Amatoxins

It has been shown by Frau and Glossman-Mitnik [11, 23, 24, 25, 26, 27, 28, 29, 30] that the HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) energies obtained with the MN12SX/Def2TZVP/H2O model chemistry allows the verification of the KID procedure, that is, rendering an approximate Koopmans behavior. With the aid of the KID technique and the finite difference approximation [11, 23, 24, 25, 26, 27, 28, 29, 30], the following expressions can be used to define the global reactivity descriptors [14, 15, 16, 35, 36]:

Electronegativity

χ=12(I+A)12(ϵL+ϵH)

Global Hardness

η=(IA)(ϵLϵH)

Electrophilicity

ω=μ22η=(I+A)24(IA)(ϵL+ϵH)24(ϵLϵH)

Electrodonating Power

ω=(3I+A)216(IA)(3ϵH+ϵL)216η

Electroaccepting Power

ω+=(I+3A)216(IA)(ϵH+3ϵL)216η

Net Electrophilicity

Δω±=ω+(ω)=ω++ω

being ϵH and ϵL the HOMO and LUMO orbital energies.

The calculated values for these global reactivity descriptors using the MN12SX/Def2TZVP/H2O model chemistry and the associated HOMO and LUMO energies are displayed in Table 2.

Table 2

Global reactivity descriptors of the Amatoxins calculated with the MN12SX density functional with the Def2TZVP basis set and the SMD solvation model using water as the solvent

MoleculeElectronegativityGlobal HardnessElectrophilicity
α-amanitin3.32094.31031.2793
β-amanitin3.47654.24441.4238
γ-amanitin3.50144.23161.4486
ϵ-amanitin3.43034.23841.3881
amanullinic acid3.44614.26621.3918
amanin3.69524.40141.5511
amaninamide3.66594.41421.5222
amanullin3.42934.26431.3789
proamanullin3.43654.23631.3939
MoleculeElectrodonating PowerElectroaccepting PowerNet Electrophilicity
α-amanitin4.48841.16755.6560
β-amanitin4.85111.37466.2257
γ-amanitin4.91241.41106.3234
ϵ-amanitin4.75621.32606.0822
amanullinic acid4.77321.32726.1004
amanin5.22491.52976.7546
amaninamide5.15331.48746.6407
amanullin4.73901.30976.0487
proamanullin4.77081.33436.1051

As expected from the analysis of the molecular and electronic structure of these peptides, their electrodonating powers are larger than their accepting powers. However, the differences in the chemical reactivity between them are not to large. The global hardness may be regarded approximately as the inverse of the polarizability and then is related to the deformability of the global electronic density. This means that a small hardness implies great reactivity and viceversa. Although the small differences, our methodology allowed to classify amanin and amaninamide as the lowest reacting peptides while γ-amanitin, ϵ-amanitin and proamanullin are the greatest reacting peptides of the group considered here. The same conclusions can be obtained from the analysis of the values of the global electrophilicity which follows from the relation between the electronegativity and the global hardness, although in this case, the differences are more marked.

3.2 Calculation of the pKas of the Amatoxin Family of Fungal Peptides

During a previous study of amino acids and peptides [37], a relationship between the pKa and the global hardness η has been developed in the form of pKa = 16.3088 - 0.8268 × η which is expected to be useful for the prediction of the pKa of larger peptides. The computation of the pKa values for all the peptides has been based on the η values presented in Table 2 and the results for the Amatoxin molecules are shown in Table 3. As to the best of our knowledge, the experimental pKas of the peptides considered in this work have not been reported and our results represent an approximate prediction of what those values could be.

Table 3

pKas of the Amatoxin family of fungal peptides

MoleculepKa
α-amanitin12.75
β-amanitin12.80
γ-amanitin12.81
ϵ-amanitin12.80
amanullinic acid12.78
amanin12.67
amaninamide12.66
amanullin12.78
proamanullin12.81

The estimated pKa values displayed in Table 3 validate that the QSAR relationship utilized as successful in the differentiation of the particular pKa values for every peptide independent of the significance of the difference. The pKa estimations of these peptides could be of interest in the development of pharmaceutical medications by clarifying the drug delivery procedures and their respective action mechanisms.

3.3 Local Reactivity Descriptors Calculation

Applying the same ideas as before, the definitions for the local reactivity descriptors will be [14, 15, 16]: Nucleophilic Fukui Function

f+(r)=ρN+1(r)ρN(r)

Electrophilic Fukui Function

f(r)=ρN(r)ρN1(r)

which are relationships between the electronic densities of the neutral, positive and negative species.

The Electrophilic Fukui functions f(r) and Nucleophilic Fukui functions f+(r) for the Amatoxin peptides are shown in Figure 2, where the colored regions allow to distinguish the electrophilic and nucleophilic regions within each of the studied peptides which could be of importance for the designing of new pharmaceutical drugs based on these moieties and also for getting and understanding of their toxicological properties.

Figure 2 Graphical representation of the Electrophilic Fukui function f−(r) (left column) and Nucleophilic Fukui function f+(r) (right column) of the Amatoxins
Figure 2

Graphical representation of the Electrophilic Fukui function f(r) (left column) and Nucleophilic Fukui function f+(r) (right column) of the Amatoxins

4 Conclusions

Throughout this study, the reactivity properties of nine molecules belonging to the Amatoxin group of fungal peptides was considered by making use of the Conceptual DFT model as an instrument to understand the electrophilic and nucleophilic interactions.

The data about the global and local chemical reactivity descriptors of the fungal peptides gained in this work could be useful to aid the plan of new pharmaceutical drugs relying on these information. The analysis of the molecular and electronic structure of these peptides revealed that their electrodonating powers are larger than their accepting powers. Moreover, although the differences in the chemical reactivity between them are not to large, a chemical reactivity order could be deduced based in our methodology that allowed to classify amanin and amaninamide as the lowest reacting peptides while γ-amanitin, ϵ-amanitin and proamanullin are the greatest reacting peptides of the group considered in this work. And the same conclusions were obtained from the analysis of the values of the global electrophilicity which follows from the relation between the electronegativity and the global hardness, although in this case, the differences were more marked.

The results related to the pKa could be of fundamental importance because it could give new information involving the drugs solubility. In this way, if the experimental values of the pKa the considered molecular systems are not available, the approximate previously developed QSAR equation employed in this study could be considered a nice predictive tool for the estimation of the pKas of small and large peptides.

As mentioned before, the information reported through this work could be of interest for medicinal chemistry researchers in using this knowledge for the developing of new therapeutic drugs based on the studied peptides or as a help for the understanding of the toxicity mechanisms exerted by them.

Acknowledgement

Daniel Glossman-Mitnik gratefully acknowledges support from the University of the Balearic Islands where part of this work has been conducted while being a Visiting Lecturer.

  1. Disclosure Statement: The authors declare no conflict of interest regarding the publication of this paper.

  2. Funding: CONACYT (Mexico) through Grant 219566-2014 and MINECO (Spain) and the European Fund for Regional Development through Grant CTQ2014-55835-R were the financial supporters of this study.

  3. Notes on Contributors: Daniel Glossman-Mitnik conceived and designed the research and headed, wrote, and revised the manuscript, Norma Flores-Holguín and Juan Frau contributed to the analysis of the results and the writing and the revision of the article.

References

[1] Gilbert J, Senyuva HZ, editors, Bioactive Compounds in Foods, Blackwell Pub, Oxford, 2008.10.1002/9781444302288Search in Google Scholar

[2] Walton J, The Cyclic Peptide Toxins of Amanita and other Poisonous Mushrooms, Springer, Cham, Switzerland, 2018.10.1007/978-3-319-76822-9Search in Google Scholar

[3] Wieland T, Bodanszky M, The World of Peptides: A Brief History of Peptide Chemistry, Springer, Berlin-Heidelberg, 1991.10.1007/978-3-642-75850-8Search in Google Scholar

[4] Guha R, Bender A, editors, Computational Approaches in Cheminformatics and Bioinformatics, Wiley, Hoboken NJ, 2012.10.1002/9781118131411Search in Google Scholar

[5] Chakraborty A, Pan S, Chattaraj PK, Biological Activity and Toxicity: A Conceptual DFT Approach. In: Structure and Bonding. Springer, Berlin-Heidelberg, 2012, p. 143–179.10.1007/978-3-642-32750-6_5Search in Google Scholar

[6] Pan S, Gupta A, Subramanian V, et al, Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density Functional Theory-based Reactivity Descriptors. In: Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk assessment, IGI Global, 2015, p. 123–179.10.4018/978-1-4666-8136-1.ch004Search in Google Scholar

[7] Pan S, Gupta A, Roy D, et al., Application of Conceptual Density Functional Theory in Developing QSAR Models and their Usefulness in the Prediction of Biological Activity and Toxicity of Molecules, In: Chemometrics Applications and Research, Apple Academic Press, 2016, p. 183–214.Search in Google Scholar

[8] Koné MGR, N’dri JS, Kodjo CG, et al, Combining of DFT and QSAR Results to Predict the Antibacterial Activity of a Series of Azetidinones derived from Dapsone as Inhibitors of Bacillus Subtilis and Pseudomonas Aeruginosa, SDRP Journal of Computational Chemistry & Molecular Modelling, 2018, 2(2), 1–8.10.25177/JCCMM.2.2.2Search in Google Scholar

[9] Zermeño-Macías M, González-Chávez M, Méndez F, et al, Theoretical Reactivity Study of Indol-4-Ones and Their Correlation with Antifungal Activity, Molecules, 2017, 22(3), 427.10.3390/molecules22030427Search in Google Scholar PubMed PubMed Central

[10] Frau J, Muñoz F, Glossman-Mitnik D, A Molecular Electron Density Theory Study of the Chemical Reactivity of cis- and trans-Resveratrol, Molecules, 2016, 21(12), 1650.10.3390/molecules21121650Search in Google Scholar PubMed PubMed Central

[11] Flores-Holguín N, Frau J, Glossman-Mitnik D, Computational Peptidology Assisted by Conceptual Density Functional Theory for the Study of Five New Antifungal Tripeptides, ACS Omega, 2019, 4(7), 12555–12560.10.1021/acsomega.9b01463Search in Google Scholar PubMed PubMed Central

[12] Gupta GK, Kumar V, Chemical Drug Design, Walter de Gruyter GmbH, Berlin, 2016.10.1515/9783110368826Search in Google Scholar

[13] Gore M, Jagtap UB, Computational Drug Discovery and Design, Springer Science+Business Media, LLC, New York, 2018.10.1007/978-1-4939-7756-7Search in Google Scholar

[14] Parr R, Yang W, Density-Functional Theory of Atoms and Molecules, Oxford University Press, New York, 1989.Search in Google Scholar

[15] Chermette H, Chemical Reactivity Indexes in Density Functional Theory, Journal of Computational Chemistry, 1999, 20, 129–154.10.1002/(SICI)1096-987X(19990115)20:1<129::AID-JCC13>3.0.CO;2-ASearch in Google Scholar

[16] Geerlings P, De Proft F, Langenaeker W, Conceptual Density Functional Theory, Chemical Reviews, 2003, 103, 1793–1873.10.1201/9781420065442.ch27Search in Google Scholar

[17] Halgren TA, Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94, Journal of Computational Chemistry, 1996, 17(5-6), 490–519.10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-PSearch in Google Scholar

[18] Halgren TA, Merck Molecular Force Field. II. MMFF94 van der Waals and Electrostatic Parameters for Intermolecular Interactions, Journal of Computational Chemistry,1996, 17(5-6), 520–552.10.1002/(SICI)1096-987X(199604)17:5/6<520::AID-JCC2>3.0.CO;2-WSearch in Google Scholar

[19] Halgren TA, MMFF VI. MMFF94s Option for Energy Minimization Studies, Journal of Computational Chemistry, 1999, 20(7), 720–729.10.1002/(SICI)1096-987X(199905)20:7<720::AID-JCC7>3.0.CO;2-XSearch in Google Scholar

[20] Halgren TA, Nachbar RB, Merck Molecular Force Field. IV. Conformational Energies and Geometries for MMFF94, Journal of Computational Chemistry, 1996, 17(5-6), 587–615.10.1002/(SICI)1096-987X(199604)17:5/6<587::AID-JCC4>3.0.CO;2-QSearch in Google Scholar

[21] Halgren TA. Merck Molecular Force field. V. Extension of MMFF94 Using Experimental Data, Additional Computational Data, and Empirical Rules, Journal of Computational Chemistry, 1996, 17(5-6), 616–641.10.1002/(SICI)1096-987X(199604)17:5/6<616::AID-JCC5>3.0.CO;2-XSearch in Google Scholar

[22] Frisch MJ, Trucks GW, Schlegel HB, et al, Gaussian 09 Revision E.01, Gaussian Inc., Wallingford CT, 2016.Search in Google Scholar

[23] Frau J, Glossman-Mitnik D, Molecular Reactivity and Absorption Properties of Melanoidin Blue-G1 through Conceptual DFT, Molecules, 2018, 23(3), 559–15.10.3390/molecules23030559Search in Google Scholar

[24] Frau J, Glossman-Mitnik D, Conceptual DFT Study of the Local Chemical Reactivity of the Dilysyldipyrrolones A and B Intermediate Melanoidins, Theoretical Chemistry Accounts, 2018, 137(5), 1210.10.1007/s00214-018-2244-xSearch in Google Scholar

[25] Frau J, Glossman-Mitnik D, Conceptual DFT Study of the Local Chemical Reactivity of the Colored BISARG Melanoidin and Its Protonated Derivative, Frontiers in Chemistry, 2018, 6(136), 1–9.10.3389/fchem.2018.00136Search in Google Scholar PubMed PubMed Central

[26] Frau J, Glossman-Mitnik D, Molecular Reactivity of some Maillard Reaction Products Studied through Conceptual DFT, Contemporary Chemistry, 2018, 1(1), 1–14.10.1155/2018/3172412Search in Google Scholar

[27] Frau J, Glossman-Mitnik D, Computational Study of the Chemical Reactivity of the Blue-M1 Intermediate Melanoidin, Computational and Theoretical Chemistry, 2018,1134, 22–29.10.1016/j.comptc.2018.04.018Search in Google Scholar

[28] Frau J, Glossman-Mitnik D, Chemical Reactivity Theory Applied to the Calculation of the Local Reactivity Descriptors of a Colored Maillard Reaction Product, Chemical Science International Journal, 2018, 22(4), 1–14.10.9734/CSJI/2018/41452Search in Google Scholar

[29] Frau J, Glossman-Mitnik D, Blue M2: An Intermediate Melanoidin Studied via Conceptual DFT, Journal of Molecular Modeling, 2018, 24(138), 1–13.10.1007/s00894-018-3673-0Search in Google Scholar PubMed

[30] 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 Calculations, IntechOpen, Rijetia, 2019, p. 1–11.Search in Google Scholar

[31] 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, Journal of Physical Chemistry B, 2009, 113, 6378–6396.10.1021/jp810292nSearch in Google Scholar PubMed

[32] Becke AD, Vertical Excitation Energies From the Adiabatic Connection, The Journal of Chemical Physics, 2016, 145(19), 194107.10.1063/1.4967813Search in Google Scholar PubMed

[33] Baerends EJ, Gritsenko OV, van Meer R, The Kohn-Sham Gap, the Fundamental Gap and the Optical Gap: The Physical Meaning of Occupied and Virtual Kohn-Sham Orbital Energies, Physical Chemistry Chemical Physics, 2013, 15(39), 16408–16425.10.1039/c3cp52547cSearch in Google Scholar PubMed

[34] van Meer R, Gritsenko OV, Baerends EJ, Physical Meaning of Virtual Kohn-Sham Orbitals and Orbital Energies: An Ideal Basis for the Description of Molecular Excitations, Journal of Chemical Theory and Computation, 2014, 10(10), 4432–4441.10.1021/ct500727cSearch in Google Scholar PubMed

[35] Gázquez J, Cedillo A, Vela A, Electrodonating and Electroaccepting Powers, Journal of Physical Chemistry A, 2007, 111(10), 1966–1970.10.1021/jp065459fSearch in Google Scholar PubMed

[36] Chattaraj P, Chakraborty A, Giri S, Net Electrophilicity, Journal of Physical Chemistry A, 2009, 113(37), 10068–10074.10.1021/jp904674xSearch in Google Scholar PubMed

[37] Frau J, Hernández-Haro N, Glossman-Mitnik D, Computational Prediction of the pKas of Small Peptides through Conceptual DFT Descriptors, Chemical Physics Letters, 2017, 671, 138–141.10.1016/j.cplett.2017.01.038Search in Google Scholar

Received: 2019-09-12
Accepted: 2019-10-24
Published Online: 2019-12-10

© 2019 N. Flores-Holguín et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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