Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese Production
Plant-derived materials from <i>Salvia officinalis</i> L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against <i>Listeria monocytogenes, Escherichia coli</i>...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/14/13/2164 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849704089190924288 |
|---|---|
| author | Dajana Vukić Biljana Lončar Lato Pezo Vladimir Vukić |
| author_facet | Dajana Vukić Biljana Lončar Lato Pezo Vladimir Vukić |
| author_sort | Dajana Vukić |
| collection | DOAJ |
| description | Plant-derived materials from <i>Salvia officinalis</i> L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against <i>Listeria monocytogenes, Escherichia coli</i>, and <i>Staphylococcus aureus</i> was investigated through the development of predictive models that describe the influence of key parameters on antimicrobial efficacy. Molecular modeling techniques were employed to identify the major constituents responsible for the observed inhibitory activity. Epirosmanol, carvacrol, limonene, and thymol were identified as the primary compounds contributing to the antimicrobial effects during cheese production. The highest weighted predicted binding energy was observed for thymol against the KdpD histidine kinase from <i>Staphylococcus aureus</i>, with a value of −33.93 kcal/mol. To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)—were developed and evaluated. Among these, the ANN model demonstrated the highest predictive accuracy and robustness, showing minimal bias and a strong coefficient of determination (R<sup>2</sup> = 0.934). These findings underscore the value of integrating molecular modeling and machine learning approaches for the identification of bioactive compounds in functional food systems. |
| format | Article |
| id | doaj-art-ffd9dbba577142a39ef7b413298bdbc1 |
| institution | DOAJ |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-ffd9dbba577142a39ef7b413298bdbc12025-08-20T03:16:55ZengMDPI AGFoods2304-81582025-06-011413216410.3390/foods14132164Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese ProductionDajana Vukić0Biljana Lončar1Lato Pezo2Vladimir Vukić3Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, SerbiaFaculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, SerbiaInstitute of General and Physical Chemistry, Studentski trg 12/V, 11000 Belgrade, SerbiaFaculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, SerbiaPlant-derived materials from <i>Salvia officinalis</i> L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against <i>Listeria monocytogenes, Escherichia coli</i>, and <i>Staphylococcus aureus</i> was investigated through the development of predictive models that describe the influence of key parameters on antimicrobial efficacy. Molecular modeling techniques were employed to identify the major constituents responsible for the observed inhibitory activity. Epirosmanol, carvacrol, limonene, and thymol were identified as the primary compounds contributing to the antimicrobial effects during cheese production. The highest weighted predicted binding energy was observed for thymol against the KdpD histidine kinase from <i>Staphylococcus aureus</i>, with a value of −33.93 kcal/mol. To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)—were developed and evaluated. Among these, the ANN model demonstrated the highest predictive accuracy and robustness, showing minimal bias and a strong coefficient of determination (R<sup>2</sup> = 0.934). These findings underscore the value of integrating molecular modeling and machine learning approaches for the identification of bioactive compounds in functional food systems.https://www.mdpi.com/2304-8158/14/13/2164antimicrobial potentialsageextractinhibitory activityKdpD histidine kinase |
| spellingShingle | Dajana Vukić Biljana Lončar Lato Pezo Vladimir Vukić Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese Production Foods antimicrobial potential sage extract inhibitory activity KdpD histidine kinase |
| title | Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese Production |
| title_full | Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese Production |
| title_fullStr | Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese Production |
| title_full_unstemmed | Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese Production |
| title_short | Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in Fresh Cheese Production |
| title_sort | application of predictive modeling and molecular simulations to elucidate the mechanisms underlying the antimicrobial activity of sage i salvia officinalis i l components in fresh cheese production |
| topic | antimicrobial potential sage extract inhibitory activity KdpD histidine kinase |
| url | https://www.mdpi.com/2304-8158/14/13/2164 |
| work_keys_str_mv | AT dajanavukic applicationofpredictivemodelingandmolecularsimulationstoelucidatethemechanismsunderlyingtheantimicrobialactivityofsageisalviaofficinalisilcomponentsinfreshcheeseproduction AT biljanaloncar applicationofpredictivemodelingandmolecularsimulationstoelucidatethemechanismsunderlyingtheantimicrobialactivityofsageisalviaofficinalisilcomponentsinfreshcheeseproduction AT latopezo applicationofpredictivemodelingandmolecularsimulationstoelucidatethemechanismsunderlyingtheantimicrobialactivityofsageisalviaofficinalisilcomponentsinfreshcheeseproduction AT vladimirvukic applicationofpredictivemodelingandmolecularsimulationstoelucidatethemechanismsunderlyingtheantimicrobialactivityofsageisalviaofficinalisilcomponentsinfreshcheeseproduction |