Computer-Aided Design of Novel Active Components in Plant

Protection

VESNA RASTIJA

Faculty of Agrobiotechnical Sciences Osijek

Josip Juraj Strossmayer University of Osijek

Vladimira Preloga 1, HR-31000 Osijek

CROATIA

Abstract: - The production demands highly specific environmentally and toxicologically acceptable plant

protection products are increasing. Computer-aided molecular design of new active components has a great deal

in developing plant protection products to avoid that long-lasting and expensive process. Computational design

of future compounds and their synthesis, evaluation of their effectiveness on harmful and beneficial organisms

in the soil, as well as detailed research mechanism of action at the molecular level, represents an initial stage in

the long-lasting and expensive process of plant protection products. In this paper, the recent advances in

quantitative structure-activity relationship (QSAR) studies, molecular docking, and calculation of “Pesticide-

likeness properties “, as well, have been reviewed. QSAR models for antifungal activities against

phytopathological fungi were obtained for the thiazoline and coumarine derivatives, coumarinyl Schiff bases,

and coumarin-1,2,4-triazoles. A molecular docking study revealed that antifungal activities of fluorinated

pyrazole aldehydes are related to the inhibition of proteinase K, coumarinyl Schiff bases with endoglucanase and

pectinase, hybrids of coumarins and 1,2,4-triazoles with sterol 14α-demethylase inhibition, 3-gydroxycoumarin

chitin synthase, while γ-thionins strongly binds to fungal membrane moieties.

Key-Words: - plant protection, QSAR, molecular docking, pesticide-likness, antifungal, insect pests

Received: February 12, 2023. Revised: February 5, 2024. Accepted: April 7, 2024. Published: May 9, 2024.

1 Introduction

Organic compounds have always been and still are,

of vital importance for the protection of crops. Since

the world population continues to grow, there is a

requirement for increased crop yields and better-

quality food.

Although the present use of synthetic compounds in

plant protection has limited the occurrence and

development of plant diseases and pests, they have

shown environmental and health hazards. That

indicates an urgent need for new, safe active

ingredients of plant protection products. Therefore,

during the last decades, the development of new and

selective active ingredients of plant protection

products has taken place with special emphasis on the

assessment of the behavior of these chemicals in the

environment. Although the use of synthetic

compounds in plant protection has reduced the

occurrence and development of plant diseases and

pests, great problems arise because of the resistance

to pesticides and their environmental and health

hazards. These compounds must be effective at

extremely low doses, easily degradable, having the

least side effects on human health, non-target

organisms, and the environment. Non-target

organisms are mainly non-vertebrates that plays an

essential role in ecosystems as pest controllers

(predators), pollinators, detrivores and saprophages,

[1].

Computer-aided molecular design (CAMD) is a

modern strategy for the development of plant

protection substances for its high efficiency in the

design of new compounds. Applying CAMD reduces

the economic costs and development time of new

plant protection products reducing the number of

chemical synthesis and biological tests. The REACH

(Registration, Evaluation, and Authorization of

Chemicals) regulation from 2011 promotes non-

animal test methods in testing chemicals' impact on

human health and the environment. Therefore, the

European Chemicals Agency (ECHA) suggested that

animal tests can be avoided by calculating hazardous

MOLECULAR SCIENCES AND APPLICATIONS

DOI: 10.37394/232023.2024.4.1

Vesna Rastija

E-ISSN: 2732-9992

Volume 4, 2024

properties using computer models of the quantitative

structure-activity relationship (QSAR) approach, [2].

QSAR technique relates chemical structure and

biological activity providing information on

structural features relevant to the observed activity.

Quality predictive QSAR model allows the design

and development of new molecules with improved

activity, [3]. Molecular docking is a molecular

modeling technique used to elucidate the mode of

action of active compounds that interact with

receptors (enzyme, protein) related to the observed

biological activity. The molecular docking allows the

screening of the binding affinity of the ligand

according to the scoring function (binding

energy), [4].

This study aims to review a recent advance in the use

of CAMD techniques for the development of a novel

active component of the future plant protection

product.

2 Methodology

2.1 QSAR Method

QSAR development process includes several steps:

1. Drawing chemical structures. 2. Generation of the

3D chemical structure using different software, such

as Avogadro, [5], and PyMol, [6] 3. Optimization of

molecular structures using different methods:

molecular mechanics force field (MM+), [7],

semiempirical methods, [8], density functional

theory (DFT), [9]. 4. Calculation of molecular

descriptor calculation performed using different web

platforme, such as, Parameter Client, [10] and

OCHEM, [11]. 5. Reducing the number of

descriptors from the initial set; generation of the

QSAR models using the descriptor sets; splitting

molecules in training and test set; generation of

QSAR models (multiple linear regression) could be

performed by QSARINS-Chem 2.2.1. (University of

Insubria, Varese, Italy), [12] 6. Generation of QSAR

models. 7. Obtained QSAR models are assessed by

fitting criteria; internal cross-validation using the

leave-one-out (LOO) method and external validation.

The robustness of QSAR models is tested by the Y-

randomisation test. The applicability of the obtained

models is checked by the residual plots and Williams

plots using QSARINS. 8. Interpretation of the QSAR

model relieves elucidation of important

physicochemical and structural requirements for the

biological activity of heterocyclic compounds. 9.

Drawing structures of the future molecules. 10.

Predict the activity for the future molecules using

obtained QSAR models.

2.2 The Molecular Docking Method

Protein crystal structures in complex with docked

ligands are downloaded from the Protein Data Bank

(PDB, https://www.rcsb.org/). Various programs are

used for the molecular docking of compounds, such

as AutoDock Vina, [13], Glide, [14], or

iGEMDOCK, [15]. For the target enzymes without a

known three-dimensional structure, the homology

modeling method is applied and carried out using

MODELLER program, [16]. The screening

compounds are ranked based on the energy of

interactions with amino acid residues of the binding

site.

2.3 Calculation of Pesticide-Likeness,

Environmental and Health Hazards Properties of

Compounds

“Pesticide-likeness“ are physical-chemical

properties that characterize compounds as potentially

successful plant protection agents, [17]. According to

this role leading candidate for the development of

pesticide candidates should have: molecular weight

(MW ≤ 435 Da); lipophilicity (ClogP ≤ 6), number of

H-bond acceptors (HBA ≤ 6), the number of H-bond

donors (HBD ≤2), number of rotatable bonds (ROB

≤9), and the number of aromatic bonds (ARB≤ 17).

For the calculation of pesticide-like properties, and

environmental and health hazards properties of

compounds, as well, these are several free-available

software: ADMETlab 2.0, [18], SwissADME, [19],

and (T.E.S.T. (Toxicity Estimation Software Tool),

[20]. Software T.E.S.T. estimates toxicity using

several advanced QSAR methodologies (hierarchical

clustering; multiple linear regression model; group

contribution approach; nearest neighbor; mode of

action (MOA) method; consensus method): T.E.S.T

estimates the value for several endpoints such

toxicity for: Pimephales promelas, Daphnia magna,

Tetrahymena pyriformis; oral rat toxicity;

bioaccumulation factor; developmental toxicity;

mutagenicity (Ames test), [20]. Estimation of blood-

brain barrier penetration, carcinogenicity in rodents;

and mutagenicity in Salmonella typhimurium could

be calculated by Lazar online toxicity predictions

software, [21].

3 Recent advances in computer-aided

molecular design of novel active

components of plant protection

MOLECULAR SCIENCES AND APPLICATIONS

DOI: 10.37394/232023.2024.4.1

Vesna Rastija

E-ISSN: 2732-9992

Volume 4, 2024

Song et al. (2008), [22] developed QSAR models for

fungicidal activities of thiazoline derivatives to

identify important structural factors for fungicidal

activities. The five-descriptor MLR model showed

lower quality (R2 = 0.829), than non-linear

relationships obtained by NN (R2 = 0.966).

Comparative molecular field analysis (CoMFA) and

comparative molecular similarity indices analysis

(CoMSIA) have been used to establish QSAR models

for the fungicidal activity of 38 N-nitrourea

derivatives. Both models shown good prediction

capability (R2 = 0.959; and R2 = 0.936, respectively),

[23]. Also, CoMFA and CoMSIA models were

developed for the antifungal activities of coumarin

derivatives against phytopathogenic fungi Valsa mali

(correlation coefficients were 0.918 and 0.949,

respectively). The models have shown that

electropositivity of substituents are favorable for the

antifungal activity of coumarins, [24].

The nonlinear methods least-squares support vector

machine (LS-SVM) and project pursuit regression

(PPR) have been shown as good modeling tools for

the modeling of fungicidal activities against the rice

blast disease of thiazoline derivatives, [25].

Coumarine derivatives showed notable antifungal

effects against phytopathogenic fungi

Macrophomina phaseolina and Sclerotinia

sclerotiorum. QSAR analysis was performed for the

activities against fungi, and the predictive QSAR

model were obtained for M. phaseolina (R2tr = 0.78;

R2ext = 0.67). Generated QSAR models indicated the

importance of multiple electron-withdrawal groups,

especially at position C-3, which enhanced antifungal

activity against M. phaseolina. The increased

antifungal activity against S. sclerotiourum

contributes hydrophobic benzoyl group at the pyrone

ring, as well as the methoxy group at the benzene

ring. To elucidate the mechanism of antifungal

action, molecular docking was performed on the

enzymes responsible for the fungal growth and the

plant cell wall-degrading. The results have shown

that coumarine derivatives possibly act inhibitory

against proteinase K, the plant wall-degrading

enzyme (Fig. 1), [26].

Fig. 1. Hydrophobic surface representation of

proteinase K (pdb ID: 2pwb) active site with docked

coumarin derivative.

Coumarinyl Schiff bases have been shown as

promising antifungal effects against M. phaseolina.

Molecular docking indicated that the tested

compounds inhibit the growth of M. phaseolina by

inhibiting enzymes that participate in the breakdown

of plant cell walls, such as endoglucanase and

pectinase, [27].

The fluorinated pyrazole aldehydes showed moderate

antifungal properties against four phytopathogenic

fungi: Fusarium oxysporum, Fusarium culmorum,

M. phaseolina, and S. sclerotiorum. According to the

molecular docking study, their antifungal activity is

possibly related to the inhibition of proteinase K,

[28].

A hybrid compounds, coumarin-1,2,4-triazoles

successfully inhibited the growth of S. sclerotiorum

and F. oxysporum and two predictive QSAR models

were generated for both fungi (R2 = 0.79; and R2 =

0.977, respectively). QSAR models revealed that

longer linkers between triazole and coumarin motifs,

additional tertiary sp2 carbon atom or ether group,

and electronegative substituents could enhance the

antifungal activity of the compounds. These

compounds are possibly acting as sterol 14α-

demethylase inhibitors, according to the molecular

docking results, [29].

3-Hydroxycoumarin has been shown as inhibitors of

the growth of Moniliophthora perniciosa fungus, the

agent of witches’ broom disease in Theobroma cacao

L.. Results of molecular docking suggested that it

inhibits the production of chitin synthase, [30].

Computational methods have also been used for the

design of insecticides. CoMFA and CoMSIA models

have been generated for the insecticidal activity of

coumarine derivatives against carmine spider mite,

Tetranychus cinnabarinus. The 3D-QSQAR models

revealed that C-3, C-6, and C-7 positions in the

MOLECULAR SCIENCES AND APPLICATIONS

DOI: 10.37394/232023.2024.4.1

Vesna Rastija

E-ISSN: 2732-9992

Volume 4, 2024

skeletal structure of the coumarins are the most

suitable active sites. The molecular docking revealed

the interaction between the scopoletin and Ca2+-

ATPase 1 gene (TcPMCA1), which is involved in the

mechanism of its detoxification, [31]. Bingchuan et

al., [32] have applied the stepwise regression analysis

method for building predictive QSAR) model for

acaricidal bioactivity against the plant pest carmine

spider (Tetranychus cinnabarinus) of coumarin

compounds. Based on strong acaricidal activities of

scopoletin phenolic ether derivatives against female

adults of T. cinnabarinus, authors have developed

statistically significant QSAR model (R2 = 0.967)

that reveals the importance of polarizability and

bulkiness of substituents, hydrophobic groups, and

electron positive groups at the specific position for

their activity.

Series 4-amino-5-substituted aryl-3-mercapto-(4H)-

1,2,4-triazoles have shown nematicidal activity

against Meloidogyne incognita and Rotylenchulus

reniformis. According to the multiple linear

regression equation obtained by QSAR analysis,

authors have concluded that nematicidal activity is

influenced by bulkiness, width, and electronic effect

of substituents on the benzene ring, [33].

γ-Thionins are antimicrobial peptides acting in plant

defense against pathogenic fungi and insects that

could be used as natural fungicides and insecticides.

A molecular docking study revealed that γ-thionins

bind on the fungal membrane moieties leading to cell

death [34]. The k-nearest Neighbor (k-NN) method

has been used to develop a QSAR model of acute

contact toxicity of 256 pesticides on honey bees (Apis

mellifera). The obtained model could be used for the

prediction of toxicity of new pesticides, [34].

4 Conclusion

CAMD has great importance in the design of highly

efficient agrochemicals, saving time and costs for

unnecessary synthesis and biological tests on

animals. QSAR models could help to elucidate the

most important structural characteristics studied

series of compounds for biological activities, which

allows to design of the next series of series with

improved activities. Statistically significant QSAR

models should be implemented in existing or new

applications for predicting the activity of compounds

related to plant protection. The models should also be

extended for the prediction of the effects on

beneficial, non-target organisms to avoid the

negative ecotoxicology impact of studied

agrochemicals. Molecular docking is a promising

tool for screening of untested compounds,

elucidating pesticides target-site and their

mechanism of action.

Acknowledgement:

This work was founded by the Faculty of

Agrobiotechnical Sciences Osijek, University of

Osijek, under the project “DEFACTOPlant”, as part

of the research team “Design of bioactive molecules”

References:

[1] Smith, K., Evans, D.A., El-Hiti, G.A. Role of Modern

Chemistry in Sustainable Arable Crop Protection,

Philosophical Transactions: Biological Sciences B,

Vol. 363, No. 1419, 2008, pp. 623-637.

[2] European Chemicals Agency, The Use of Alternatives

to Testing on Animals for the REACH Regulation

2011, ECHA-11-R-004.2-EN, 2011.

[3] Kar, S., Roy, K., Leszczynski, J., “On Applications of

QSARs in Food and Agricultural Sciences: History

and Critical Review of Recent Developments.” in

Advances in QSAR Modeling, Challenges and

Advances in Computational Chemistry and Physics,

edited by K. Roy (Springer International Publishing

AG, New York, 2017.

[4] Meng, X.Y., Zhang, H.-X., Mezei, M., Cui, M.,

Molecular Docking: A Powerful Approach for

Structure-Based Drug Discovery, Current Computer-

Aided Drug Design, Vol. 7, No. 2, 2011, pp. 146–

157.

[5] Hanwell, M.D., Curtis, D.E., Lonie, D.C.,

Vandermeersch, T., Zurek, E., Hutchison, G. R.,

Avogadro: An Advanced Semantic Chemical Editor,

Visualization, and Analysis Platform. Journal of

Cheminformatics, Vol. 4, No. 17, 2012, Article No.

17.

[6] DeLano, W.L., The PyMOL Molecular Graphics

System, 2010.

[7] Hocquet, A., Langgård, M., An Evaluation of the

MM+ Force Field. Journal of Molecular Modeling,

Vol. 4, 1998, pp. 94–112.

[8] Dral, P.O., Hourahine, B., Grimme, S., Modern

Semiempirical Electronic Structure Methods, The

Journal of Chemical Physics, Vol. 160, 2024, Article

No. 040401.

[9] Ozimiński, W.P., Dobrowolski, J. Cz., Mazurek, A.P.,

DFT Studies on Tautomerism of C5-Substituted

1,2,3-Triazoles. Journal of Molecular Structure, Jun

1, 2003, pp. 697–704.

[10] VCCLAB, Virtual Computational Chemistry

Laboratory, https://vcclab.org, 2005, (Accessed

Date: April 29, 2024).

[11] OCHEM, Online chemical database,

https://ochem.eu/home/show.do, (Accessed Date:

April 29, 2024).

[12] Gramatica, P., Chirico, N., Papa, E., Cassani, S.,

Kovarich, S., QSARINS: A New Software for the

Development, Analysis, and Validation of QSAR

MOLECULAR SCIENCES AND APPLICATIONS

DOI: 10.37394/232023.2024.4.1

Vesna Rastija

E-ISSN: 2732-9992

Volume 4, 2024

MLR models. Journal of Computational Chemistry,

Vol. 34, No. 24, 2013, pp. 2121–2132.

[13] Trott, O., Olson, A.J., AutoDock Vina: Improving the

Speed and Accuracy of Docking with a New Scoring

Function, Efficient Optimization, and

Multithreading. Journal of Computational

Chemistry, Vol.31, No. 2, 2010, pp. 455-61.

[14] Halgren, T.A., Murphy, R.B., Friesner, R.A., Beard,

H.S., Frye, L.L., Pollard W.T., Banks, J.L., Glide: a

New Approach for Rapid, Accurate Docking and

Scoring. 2. Enrichment Factors in Database

Screening, Journal of Medicinal Chemistry, Vol. 47,

2004, Article No. 1750e1759.

[15] Hsu, K.-C., Chen, Y.-F., Lin S.-R., Yang, J.-M.,

iGEMDOCK: a Graphical Environment of

Enhancing GEMDOCK using Pharmacological

Interactions and Post-Screening Analysis. BMC

Bioinformatics, Vol. 12, Suppl. 1, (2011), Article No.

S33.

[16] Sali, A., Blundell, T. L., Comparative Protein

Modelling by Satisfaction of Spatial Restraints.

Journal of Molecular Biology. Vol. 234, No. 3, 1993,

pp. 779-815.

[17] Hao G., Dong Q., Yang G. A Comparative Study on

the Constitutive Properties of Marketed Pesticides.

Molecular Informatics, Vol. 30, 2011, pp. 614–622.

[18] ADMETlab 2.0, https://admetmesh.scbdd.com,

(Accessed Date: April 29, 2024).

[19] SwissADME, http://www.swissadme.ch/index.php,

(Accessed Date: April 29, 2024).

[20] Toxicity Estimation Software Tool (TEST),

https://www.epa.gov/comptox-tools/toxicity-

estimation-software-tool-test, (Accessed Date: April

29, 2024).

[21] Lazar toxicity predictions, https://lazar.in-

silico.ch/predict, (Accessed Date: April 29, 2024).

[22] Song, J.S., Moon, T., Nam, K.D,. Lee, J.K., Hahn,

H.G., Choi, E.J., Yoon, C.N., Quantitative structural-

activity relationship (QSAR) study for fungicidal

activities of thiazoline derivatives against rice blast.

Bioorganic & medicinal chemistry letters. Vol. 18,

No. 6, 2008, pp. 2133-2142.

[23] Cao, X., Xu, S., Li, X., Shen, X., Zhang, Q., Li, J.,

Chen, C., N-Nitrourea Derivatives as Novel Potential

Fungicides against Rhizoctonia solani: Synthesis,

Antifungal Activities, and 3D-QSAR, Chemical

Biology & Drug Design, Vol. 80, 2012, pp. 81–89.

[24] Wei, Y., Peng, W., Wang, D., Hao S.-H., Li, W. W.,

Ding, F., Design, synthesis, antifungal activity, and

3D-QSAR of coumarin derivatives. Journal of Pest

Science, Vol. 43, 2018, pp. 88-95.

[25] Du, H., Wang, J., Hu, Z., Yao X., Zhang, X., Journal

of Agricultural and Food Chemistry. Vol. 56, 2008,

pp. 10785-10792.

[26] Rastija, V., Vrandečić, K., Ćosić, J., Majić, I., Kanižai

Šarić, G., Agić, D., Karnaš, M., Lončarić M., Molnar,

M., Biological Activities Related to Plant Protection

and Environmental Effects of Coumarin Derivatives:

QSAR and Molecular Docking Studies, International

Journal of Molecular Sciences, Vol. 22, 2021, Article

No. 7283.

[27] Rastija, V., Vrandečić, K., Ćosić, J., Kanižai Šarić,

Majić, I., G., Agić, D., Šubarić, D., Karnaš, M., Bešlo,

D., Komar, M., Molnar, M., Effects of Coumarinyl

Schiff Bases against Phytopathogenic Fungi, the Soil-

Beneficial Bacteria and Entomopathogenic

Nematodes: Deeper Insight into the Mechanism of

Action. Molecules, Vol. 27, 2022, Article No. 2196.

[28] Rastija, V., Vrandečić, K., Ćosić, J., Kanižai Šarić, G.,

Majić, I., Agić, D., Šubarić, D., Karnaš, M., Bešlo D.,

Brahmbhatt, H., Komar, M., International Journal of

Molecular Sciences, Vol. 24, 2023, Article No. 9335.

[29] Karnaš, M., Rastija, V., Vrandečić, K., Ćosić, J.,

Kanižai Šarić, G., Agić, D., Šubarić D., Molnar, M.,

Synthesis, antifungal, antibacterial activity, and

computational evaluations of some novel coumarin-

1,2,4-triazole hybrid compounds, Journal of Taibah

University for Science, Vol. 18, No., 1, 2024, Article

No. 2331456.

[30] De Andrade Gonçalves, P., dos Santos Junior, M.C.,

do Sacramento Sousa, C., Góes-Neto, A., Luz,

E.D.M.N., Damaceno, V.O., Niella, A.R.R., Filho

J.M.B , de Assis, S., Study of Sodium 3-

Hydroxycoumarin as Inhibitors in Vitro, in Vivo and

in Silico of Moniliophthora perniciosa fungus,

European Journal of Plant Pathology, Vol. 153, 2019,

pp. 15-27.

[31] Hou, Q.-L., Luo, J.-X., Zhang, B.-C., Jiang, G.-F.,

Ding W., Zhang, Y.-Q., 3D-QSAR and Molecular

Docking Studies on the TcPMCA1-Mediated

Detoxification of Scopoletin and Coumarin

DerivativesInt. International Journal of Molecular

Sciences. Vol. 18, 2017, Article No. 1380.

[32] Bingchuan, Z., Jinxiang, L., Ting, L., Dan, W., Wei

D., Yongqiang, Z., Study on Acaricidal Bioactivity

and Quantitative Structure Activity Relationship of

Coumarin Compounds against Tetranychus

cinnabarinus Bois. (Acari: Tetranychidae). Chinese

Journal of Pesticide Science, Vol. 18, 2016, pp. 37-

48.

[33] Lakshman, B., Gupta D., Prasad, D., Quantitative

structure activity relationships for the nematicidal

activity of 4-amino-5-substituted aryl-3-mercapto-

(4H)-1,2,4-triazole. Indian Journal of Chemistry, Vol.

49B, 2010, pp. 1657-1661.

[34] Khaliq, B., Abdalla, M., Mehmood, S., Saeed, A.,

Munawar, A., Saeed, M.Q., Saeed, Q., Ibrahim, M.,

Ali, Z., Hussain, S., Eltayb, W.A., Betzel, C., Akrem,

A. Comprehensive Structural and Functional

Characterization of a Seed γ-Thionin as a Potent

Bioactive Molecule Against Fungal Pathogens and

Insect Pests. Current Medicinal Chemistry. Vol. 29,

No. 42, 2022, pp. 6446-6462

[35] Como, F., Carnesecchi, E., Volani, S., Dorne, J.L.,

Richardson, J., Bassan, A., Pavan, M., Benfenati, E.,

Predicting Acute Contact Toxicity of Pesticides in

Honeybees (Apis mellifera) through a k-Nearest

Neighbor Model. Chemosphere. Vol. 166, 2017, pp.

438–444.

MOLECULAR SCIENCES AND APPLICATIONS

DOI: 10.37394/232023.2024.4.1

Vesna Rastija

E-ISSN: 2732-9992

Volume 4, 2024

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

Vesna Rastija searched the literature and wrote this

paper, on its own..

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

Project of “DEFACTOPlant” of the Faculty of

Agrobiotechnical Sciences Osijek, University of

Osijek

Conflict of Interest

The author has no conflicts of interest to declare that

are relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

MOLECULAR SCIENCES AND APPLICATIONS

DOI: 10.37394/232023.2024.4.1

Vesna Rastija

E-ISSN: 2732-9992

Volume 4, 2024