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Foods
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Application of Bioinformatics in Food Science
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Application of Bioinformatics in Food Science

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Foodomics".

Deadline for manuscript submissions: 1 October 2025 | Viewed by 1773

Special Issue Editors

Dr. Vladimir Vukić

E-Mail Website
Guest Editor
Faculty of Technology, University of Novi Sad, Bul. Cara Lazara 1, 21000 Novi Sad, Serbia
Interests: bioinformatics; molecular modeling; food science; bioactive compounds; computational biology
Dr. Dajana Vukić

E-Mail Website
Guest Editor
Faculty of Technology, University of Novi Sad, Blvd. Cara Lazara 1, 21000 Novi Sad, Serbia
Interests: bioinformatics; molecular modeling; food science; bioactive compounds; computational biology
Special Issues, Collections and Topics in MDPI journals
Dr. Zdravko Šumić

E-Mail Website
Guest Editor
Faculty of Technology, University of Novi Sad, Blvd. Cara Lazara 1, 21000 Novi Sad, Serbia
Interests: processing

Special Issue Information

Dear Colleagues,

Bioinformatics is a rapidly evolving field that plays a crucial role in analyzing and interpreting complex biological data. In the context of food science, bioinformatics offers immense potential for innovation. The application of bioinformatic tools can enhance food safety, improve nutritional quality, identify food-derived bioactive compounds, predict flavor profiles, and optimize food processing techniques. Emerging technologies are at the forefront of this movement within the food industry. Bioinformatics can help in developing new products with enhanced nutritional value or reduced allergenic potential.

Therefore, integrating bioinformatic approaches into food science is crucial for advancing our understanding of complex interactions between nutrients and biological systems. This Special Issue on “Application of Bioinformatics in Food Science” seeks high-quality works focusing on innovative applications of bioinformatics in enhancing various aspects of food research. Topics include but are not limited to the following:

  • Genomic Analysis: Applying genomic tools for pathogen detection or strain identification.
  • Nutritional Genomics: Using machine learning models to optimize nutritional content and dietary recommendations.
  • Bioactivity: Identification (with in vitro validation) of food-derived bioactive compounds
  • Food Safety: Developing predictive models for allergenic potential or spoilage prediction.
  • Flavor Profiling: Utilizing computational methods to enhance sensory acceptability.
  • Food Processing Optimization: Leveraging algorithms to streamline production processes while minimizing waste.

While bioinformatics models offer high predictive potential, they are inherently subject to prediction errors. Therefore, experimental validation is crucial to ensure their scientific reliability. Research papers presenting bioinformatics models with a high probability of prediction error (such as molecular docking simulations), without corresponding experimental validation, will not be considered for publication.

Dr. Vladimir Vukić
Dr. Dajana Vukić
Dr. Zdravko Šumić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinformatics
  • food science
  • nutritional genomics
  • food safety
  • molecular modeling
  • bioactive compounds
  • flavor profiling
  • food processing optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

18 pages, 8398 KiB  
Article
Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (Salvia officinalis L.) Components in Fresh Cheese Production
by Dajana Vukić, Biljana Lončar, Lato Pezo and Vladimir Vukić
Foods 2025, 14(13), 2164; https://doi.org/10.3390/foods14132164 - 20 Jun 2025
Viewed by 391
Abstract
Plant-derived materials from Salvia officinalis L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against Listeria monocytogenes, Escherichia coli, and Staphylococcus aureus was investigated through the development of [...] Read more.
Plant-derived materials from Salvia officinalis L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against Listeria monocytogenes, Escherichia coli, and Staphylococcus aureus 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 Staphylococcus aureus, 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 (R2 = 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. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Food Science)
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23 pages, 3172 KiB  
Article
Optimization of Protein Extraction from Rapeseed Oil Cake by Dephenolization Process for Scale-Up Application Using Artificial Neural Networks
by Branislava Đermanovć, Jelena Vujetić, Tea Sedlar, Danka Dragojlović, Ljiljana Popović, Predrag Kojić, Pavle Jovanov and Bojana Šarić
Foods 2025, 14(10), 1762; https://doi.org/10.3390/foods14101762 - 16 May 2025
Cited by 1 | Viewed by 649
Abstract
Rapeseed proteins, due to their quality and wide availability, have great potential for application in human nutrition. However, their high content of antinutritional compounds poses significant economic and environmental challenges for food industry applications. To overcome these obstacles, various extraction and modification techniques, [...] Read more.
Rapeseed proteins, due to their quality and wide availability, have great potential for application in human nutrition. However, their high content of antinutritional compounds poses significant economic and environmental challenges for food industry applications. To overcome these obstacles, various extraction and modification techniques, including enzymatic and ultrasound-assisted methods, were used to enhance protein functionality and improve both nutritional and sensory properties. In this study, the effects of dephenolization on the structural, physicochemical, and functional properties of rapeseed protein isolate obtained from defatted rapeseed cake were investigated through four different protocols. All obtained protein isolates (PIs) exhibited high protein purity (above 65%), with a notable difference in extraction yield. Furthermore, the extraction process was optimized using an artificial neural network (ANN) model, which demonstrated high predictive performance. The optimal extraction conditions for the dephenolization of rapeseed oil cake were 84% ethanol concentration, a solid-to-liquid ratio of 1/60 w/v, and 15 min of ultrasound treatment, resulting in an impressive protein purity of 90.68% with a yield of 29.17%. The obtained proteins were further characterized and compared in terms of protein profile (FTIR and SDS-PAGE), amino acid composition, solubility, and digestibility. The protein isolate (PI) obtained under optimized conditions displayed superior functional properties, underscoring the relevance and necessity of a data-driven, mathematical approach for scale-up and industrial implementation. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Food Science)
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