QSAR Analysis of the Effect of Metal Ions on the Peptidase Bacillus thuringiensis var. israelensis IMV B-7465 Activity

Authors

DOI:

https://doi.org/10.20535/ibb.2021.5.4.243373

Keywords:

peptidase Bacillus thuringiensis var. israelensis IMV B-7465, metal ions, QSAR analysis, random forest, trend vector, enzyme activity

Abstract

Background. The catalytic activity of enzymes, which is their most important characteristic, can change significantly under the influence of effectors, for example, metal ions, and is the subject of special studies that are important for biochemistry, biotechnology, medicine, and other branches of science. Usually, the activity of enzymes in the presence of metals is assessed by the change in the rate of the enzymatic reaction. However, conducting such experimental studies, especially for new enzymes, as in the case of peptidase Bacillus thuringiensis var. israelensis IMV B-7465, requires significant resources and extensive kinetic studies. Therefore, it is advisable to use the methods of computational chemistry, the basic task of which is to search for the structure–property relationship, to build a model that can assess the effect of metal ions on peptidase activity with a high degree of probability.

Objective. We are aimed to develop QSAR models for analysis and prediction of the effect of metal ions on the activity of peptidase Bacillus thuringiensis var. israelensis IMV B-7465.

Methods. The effect of metal ions was studied by determining the proteolytic activity of peptidase after co-incubation for 30 min in 0.0167 M Tris-HCl buffer solution (pH 7.5, 37 °C). The final concentration of metal chlorides Li+; Na+; K+; Cs+; Cu2+; Be2+; Mg2+; Ca2+; Sr2+; Ba2+; Zn2+; Cd2+; Hg2+; Cr3+; Mn2+; Co2+; Ni2+ in the buffer solution was 4 mmol/dm3. To search for the quantitative structure–property relationship, we used the reference data on the properties of metal ions, as well as trend vector and random forest methods.

Results. A study of the effect of metal ions on the proteolytic activity of peptidase Bacillus thuringiensis var. israelensis IMV B-7465 showed that some metal ions (Li+, Mn2+ и Co2+) activated peptidase, while others (Cu2+, Be2+, Cd2+, Hg2+, Cr3) inhibited the enzyme activity. Adequate statistical models without classification errors and activity class prediction errors for the test set were constructed by nonlinear trend vector and random forest methods. Both models show that the most important characteristics of metal ions affecting enzyme activity are electronegativity (ENPol), the first ionization potential (IP1), the entropy of ions in aqueous solution (S), and the electron affinity energy (Eae).

Conclusions. QSAR analysis methods in combination with nonlinear trend vector and random forest methods allow adequately describing the effect of metal ions on the peptidase Bacillus thuringiensis var. israelensis IMV B-7465 activity due to descriptors reflecting a certain balance of their electron-donating and electron-accepting properties (electronegativity, the first ionization potential, the electron affinity energy) and thermodynamic properties in aqueous solution (entropy of solvation). Both statistical methods give similar values of the importance of descriptors, but only the trend vector method allows us to analyze the direction of influence of specific characteristics of ions.

References

Falch EA. Industrial enzymes — developments in production and application. Biotechnol Advan. 1991;9(4):643-58. DOI: 10.1016/0734-9750(91)90736-f

Trincone A. Potential biocatalysts originating from sea environments. J Molec Catal B Enzym. 2010;66(3-4):241-56. DOI: 10.1016/j.molcatb.2010.06.004

Belov AA, Vaniushenkova AA, Dosadina EE, Khanafina AA. New textile dressings based on biodegradable polymers containing proteinases for wounds and burns treatment. Wounds Wound Infect. 2018;15(1):16-26. DOI: 10.25199/2408-9613-2018-5-1-16-26

Varbanets LD, Nidialkova NА, Seifullina II, Pulia AV, Skorokhod LS. Modification Bacillus thuringiensis var. israelensis IMV В-7465 peptidase activity by hydrazide/hydrozonecomplexes of 3d-metals. J Microbiol. 2017;79(3):14-26.

Sayem SM, Abu A, Jahangir H, Mozammel M. Effect of temperature, pH and metal ions on the activity and stability of alkaline protease from novel Bacillus licheniformis MZK03. Proc Pakistan Acad Sci. 2006;43(4):257-62.

Shukor YM, Noor B, Nafisah J, Abdullah J, Shamaan M, Syed NA. An inhibitive determination method for heavy metals using bromelain, a cysteine protease. Appl Biochem Biotechnol. 2008;144(3):283-91. DOI: 10.1007/s12010-007-8063-5

Hatree EF. Determination of protein: a modification of the Lowry method that gives a linear photometric response. J Anal Biochem. 1972;48(2):422-7. DOI: 10.1016/0003-2697(72)90094-2

Petrova IS, Vintsyunaite MN. Determination of proteolytic activity of enzyme preparations of microbial origin. Appl Biochem Nicrobiol. 1966;2(1):322-7.

Volkov AI, Zharskiy IM. Large Chemical Reference Book. Minsk: Sovremennaya Shkola; 2005.

Carhart RE, Smith DH, Venkataraghavan R. Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci. 1985;25(2):64-73. DOI: 10.1021/ci00046a002

Vityuk NV, Kuzmin VK. Mechanistic models in chemometrics for the analysis of multivariate research data. Analog of the method of dipole moments in the analysis of the structure (composition)-property relationship. J Analyt Chem. 1994;49(2):165.

Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Boca Raton: Routledge; 2017. 368 p. DOI: 10.1201/9781315139470

Kuz'min VE, Polishchuk PG, Artemenko AG, Andronati SA. Interpretation of QSAR models based on random forest methods. Mol Inf. 2011;30(6‐7):593-603. DOI: 10.1002/minf.201000173

Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. A classification and regression tool for compound classification and QSAR modeling. J Chem Inform Comp Sci. 2003;43(6):1947-58. DOI: 10.1021/ci034160g

Lapach SN, Chubenko AV, Babich PN. Statistical methods in biomedical research using excel. 2nd edition. Kyiv: Morion; 2001.

Raymond EC, Dennis HS, Venkataraghavan R. Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci. 1985;25(2):64-73. DOI: 10.1021/ci00046a002

Borg I, Groenen PJF. Modern multidimensional scaling: Theory and applications. 2nd edition. New York: Springer; 2005. DOI: 10.1007/0-387-28981-X

Kirillova YM, Mikhailova EO, Balaban N, Mardanova AM. Biosynthesis of the Bacillus intermedius subtilisin-like serine proteinase by the recombinant. Microbiology. 2006;75(2):142-7. DOI: 10.1134/S0026261706020068

Sabirova AR, Rudakova NL, Balaban NP, Ilyinskaya ON, Demidyuk IV, Kostrov SV, et al. A novel secreted metalloproteinase Bacillus intermedius. FEBS Lett. 2010;584(21):4419-25. DOI: 10.1016/j.febslet.2010.09.049

Kublanov IV, Tsirul'nikov KB, Kaliberda EN, Rumsh LD, Haertle T, Bonch-Osmolovskaia EA. Keratinase of an anaerobic thermophilic bacterium Thermoanaerobacter sp. strain 1004-09 isolated from a hot spring in the Baikal Rift zone. Mikrobiologiia. 2009;78(1):79-88.

Matselyukh ОV, Nidialkova NA, Varbanets LD. Purification and physicochemical properties of Bacillus thuringiensis ІМВ В-7324 peptidase with elastolytic and fibrinolytic activity. Ukr Biochem J. 2012;84(2):25-36.

Qader Ul, Sattar SA, Afsheen HA. Effect of metal ions, solvents and surfactants on the activity of protease from Aspergillus niger KIBGE-IB36. J Basic Appl Sci. 2017;13:491-5. DOI: 10.6000/1927-5129.2017.13.80

Wang SL, Yang J, Zhou Y, Xu B. Effects and mechanism of metal ions on enzymatic hydrolysis of wheat straw after pretreatment. Bioresources. 2018;13(2):2617-31. DOI: 10.15376/biores.13.2.2617-2631

Dekina SS, Hovsepyan AM, Artemenko AG, Romanovskaya II, Kuzmin VY. Investigation of metal ions influence on lysozyme activity by the QSAR analysis method. Microbiol Biotechnol. 2012;4:44-51. DOI: 10.18524/2307-4663.2012.4(20).91404

АAndronati SA, Shesterenko EA, Artemenko AG, Polishchuk PG, Muratov EN, Sevastyanov OV, et al. Study of the effect of metal ions on the activity of pig liver carboxylesterase by the QSAR method. Rep Na Acad Sci Ukr. 2012;9:154-8.

Shesterenko YA, Artemenko AG, Polishchuk PG, Muratov EN, Sevastyanov OV, Romanovskaya II, et al. QSPR analysis of the reactivity of tyrosinase substrates by the random forest method. Rep Na Acad Sci Ukr. 2010;5:181-6.

Published

2021-12-29

How to Cite

1.
Artemenko A, Dekina S, Romanovska I, Kuz’min V. QSAR Analysis of the Effect of Metal Ions on the Peptidase Bacillus thuringiensis var. israelensis IMV B-7465 Activity. Innov Biosyst Bioeng [Internet]. 2021Dec.29 [cited 2024Dec.10];5(4):238-46. Available from: https://ibb.kpi.ua/article/view/243373

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