Biology, Medicine, & Natural Product Chemistry ISSN 2089-6514 (paper) Volume 14, Number 2, October 2025 | Pages: 731-738 | DOI: 10.14421/biomedich.2025.142.731-738 ISSN 2540-9328 (online) The Antivirulence Mechanisms of Phytate Against Pathogenic Bacteria in Skin Infections Nabilatul Zhofiroh1, Rian Ka Praja2*, Elsa Trinovita3, Ysrafil3, Ranintha BR Surbakti4 1Undergraduate Program of Medicine; 2Department of Microbiology, Faculty of Medicine; 3Department of Pharmacotherapy, Faculty of Medicine; 4Department of Clinical Medicine, Faculty of Medicine, Palangka Raya University. Jl. Yos Sudarso, Palangka Raya 73111, Indonesia. Corresponding author* riankapraja@med.upr.ac.id Manuscript received: 04 June, 2025. Revision accepted: 18 September, 2025. Published: 01 October, 2025. Abstract Skin infections caused by the bacteria Staphylococcus aureus, Streptococcus pyogenes, and Propionibacterium acnes are often a common health problem. One treatment is antibiotics, but the cases of antibiotic resistance are increasing. Thus, new treatment alternatives are needed. This study aimed to analyze the molecular mechanism of phytate antivirulence against pathogenic bacteria of skin infection. This study used a bioinformatics approach involving analysis of phytate interactions with bacterial virulent proteins via STITCH, functional classification of proteins with VICMpred, and prediction of virulence properties using VirulentPred. B-cell and MHC epitopes were analyzed using IEDB, while protein subcellular location was determined through PSORTb. The results showed that phytate interacted specifically with virulent proteins in all three bacteria, most of which functioned in cellular and metabolic processes. These virulent proteins also have immunologically relevant epitopes. Subcellular location analysis showed that phytate protein targets were dispersed in the cytoplasmic membrane and cytoplasm. These findings indicated that phytate has a significant antivirulence mechanism by targeting virulent proteins of skin pathogenic bacteria, thus potentially becoming a therapeutic agent to treat skin infections while reducing antibiotic resistance. Keywords: Phytate; Antivirulence; Staphylococcus aureus; Streptococcus pyogenes; Propionibacterium acnes. INTRODUCTION The skin can become infected because microorganisms can penetrate the damaged skin barrier. Skin infections can be caused by viruses, bacteria, fungi, or parasites (Lidjaja, 2022). Based on data from the Demographic Health Survey in Indonesia in 2016, the prevalence of skin diseases was 2.93% to 27.5% (Edison et al., 2023). According to the World Health Organization (WHO), the prevalence of infectious skin diseases in 2020 was reported to be around 300 million cases per year. The prevalence of skin diseases in Indonesia is 4.60%- 12.95%, ranking third out of the top 10 diseases (Sri Rahayu et al., 2023). The bacteria Staphylococcus aureus, Streptococcus pyogenes, and Propionibacterium acnes commonly cause skin infections. Staphylococcus aureus, Streptococcus pyogenes, and Propionibacterium acnes are Gram-positive bacteria. Staphylococcus aureus is a spherical bacterium with a diameter of 0.7-1.2 μm, forms irregular groups resembling grapes, does not form spores, is facultatively anaerobic, and does not move (Devi et al., 2022). Then, Streptococcus pyogenes bacteria are cocci, arranged in chains, and show catalase and oxidase activity (Savitri et al., 2019). Propionibacterium acnes bacteria, on the other hand, belong to the Corynebacteria family but have no toxicity (Zahrah et al., 2018). Antibiotic resistance is the ability of microorganisms to inhibit the action of antimicrobial agents, and this phenomenon occurs when antibiotics lose their efficiency in inhibiting bacterial growth, which is one of the most important public health problems to be solved (Putri et al., 2023). According to the Antimicrobial Resistance Control Committee, bacterial resistance in Indonesia continued to increase from 2013 to 2019 (Marsudi et al., 2021) and according to Suhartini in 2024, the prevalence of antibiotic use in Indonesia is in the high category, which is 40%-60% (Suhartini & Rahmi Makmur, 2024). Uncontrolled use of antibiotics causes resistance to increase. The wrong antibiotics can cause resistant bacteria (Lubis et al., 2019). Phytate (chemically known as myoinositol (1,2,3,4,5,6) hexakisphosphate). Phytate in plants is a source of energy and antioxidant capacity (as a phosphate group donor) but has a major role as a candidate antimicrobial natural material (specifically Cu2+ and Zn2+ cation depots) due to its negative charge at physiological pH (Pires et al., 2023). In a https://doi.org/10.14421/biomedich.2025.142.731-738 mailto:riankapraja@med.upr.ac.id 732 Biology, Medicine, & Natural Product Chemistry 14 (2), 2025: 731-738 bioinformatics study conducted by Hashimoto et al (2022), it was shown that consuming phytate-enriched foods such as rice can induce an increase in epithelial antimicrobial defence mechanisms in the gut, protecting against infection by pathogenic bacteria such as E. coli (Hashimoto-Hill et al., 2022). Then in the study of Sorour et al (2022) they conducted research on the antibacterial properties of pure phytate compound extracts and showed that Gram-positive bacteria were more sensitive to phytate compounds than Gram- negative bacteria (Sorour et al., 2022) MATERIALS AND METHODS Type of Research This study used a computational approach with bioinformatics methods that utilise data analysis as a method to explore and understand more about virulence factors in three types of pathogenic bacteria, namely Staphylococcus aureus Mu50, Streptococcus Pyogenes M1 GAS, and Propionibacterium acnes KPA171202. Population and Sample This study’s samples were phytate and FASTA compounds from the protein sequences of Staphylococcus aureus Mu50, Streptococcus Pyogenes M1 GAS, and Propionibacterium acnes KPA171202. Data Collection STITCH version 5.0 was used as an interaction analysis of Staphylococcus aureus Mu50, Streptococcus Pyogenes M1 GAS, and Propionibacterium acnes KPA171202 targeted to phytate compounds, then FASTA was downloaded from the National Center for Biotechnology Information (NCBI) database. FASTA files were downloaded and renamed according to the protein name of the bacteria targeted by phytate compounds. The FASTA was used in functional class analysis, virulence trait analysis, epitope analysis, and subcellular analysis of proteins from Staphylococcus aureus Mu50, Streptococcus Pyogenes M1 GAS, and Propionibacterium acnes KPA171202 that interact with phytate compounds using VICMPred, VirulentPred, BepiPred version 2.0, MHC-I Binding Prediction, MHC- II Binding Prediction, and PSORTb version 3.0. Data Analysis and Processing Interaction Analysis of Compounds and Bacterial Proteins Web STITCH version 5.0 accessible through http://stitch.embl.de was analyzed in relation to the interaction of compounds and bacteria Staphylococcus aureus Mu50, Streptococcus pyogenes M1 GAS, and Propionibacterium acnes KPA171202 to see the interaction between protein sequences that interact with phytate compounds. The results of the analysis are in the form of a three-dimensional diagram. Then, the FASTA download of protein sequences through NCBI (National Center for Biotechnology Information) data was carried out to be used in the next stage of analysis. Functional Class Analysis Functional class analysis of the protein sequences of Staphylococcus aureus Mu50, Streptococcus pyogenes M1 GAS, and Propionibacterium acnes KPA171202 bacteria was analyzed on the website http://crdd.osdd.net/raghava/vicmpred/ Virulence Trait Analysis The VirulentPred 2.0 website https://bioinfo.icgeb.res.in/virulent2/ was employed to analyze the virulence properties of protein sequences targeted by phytate compounds. B-cell Epitope Analysis The IEDB Analysis Resource website accessible at http://tools.iedb.org/bcell/ was used for B cell epitope analysis. MHC I Epitope Analysis MHC I epitope analysis of protein sequences targeted by phytate using MHC I Binding Predictions accessible on the website http://tools.iedb.org/mhci/. MHC II Epitope Analysis MHC II epitope analysis of protein sequences targeted by phytate compounds using MHC I Binding Predictions accessible on the website http://tools.iedb.org/mhcii/. Subcellular Location Analysis The PSORTb v3.0.3 website which can be accessed through https://www.psort.org/psortb/ was used to analyze the subcellular location of protein sequences. RESULTS AND DISCUSSION Interaction Analysis of Compounds and Bacterial Proteins Analysis of protein interactions using STITCH version 5.0 shows several proteins from the interaction between phytate compounds and Staphylococcus aureus Mu50, Streptococcus pyogenes M1 GAS, and Propionibacterium acnes KPA171202. The analysis diagram is shown in Figure 1. http://stitch.embl.de/ http://crdd.osdd.net/raghava/vicmpred/ https://bioinfo.icgeb.res.in/virulent2/ http://tools.iedb.org/bcell/ http://tools.iedb.org/mhci/ http://tools.iedb.org/mhcii/ https://www.psort.org/psortb/ Zhofiroh et al. – The Antivirulence Mechanisms of Phytate … 733 A B C Figure 1. Phytate Interaction Diagram with (A) Staphylococcus aureus Mu50 (B) Streptococcus pyogenes M1 GAS (C) Propionibacterium acnes KPA171202. Functional Class Analysis and Virulence Traits In the next step, the protein was analyzed using VICMPred and VirulentPred v2.0, and the functional class and virulence properties of each protein were obtained, as shown in Table 1. Table 1. Analysis of Functional Classes and Virulence Properties of Staphylococcus aureus Mu50, Streptococcus pyogenes M1 GAS and Propionibacterium acnes KPA171202 Proteins Interacting with Phytate. Organism Identification Code Proteins That React with Phytate Vicmpred Functional Class Virulent Pred Staphylococcus aureus mu50 gpmA phosphoglyceromutase; Catalyzes the interconversion of 2-phosphoglycerate and 3- phosphoglycerate (By similarity) Cellular process Non-virulent SAV2490 mutator protein mutT Metabolism molecule Non-virulent SAV1499 ADP-ribose pyrophosphatase Metabolism molecule Non-virulent SAV1338 hypothetical protein Cellular process Virulent SAV1794 hypothetical protein Information and Storage Non-virulent SAV0240 flavohemoprotein Cellular process Non-virulent SAV1118 hypothetical protein Information and Storage Virulent gpmA phosphoglyceromutase; Catalyzes the interconversion of 2-phosphoglycerate and 3- phosphoglycerate (By similarity) Cellular process non-virulent Streptococcus pyogenes M1 GAS SPy_2186 hypothetical protein Cellular process virulent Spy_0477 hypothetical protein Cellular process Non-virulent SPy_2170 hypothetical protein Cellular process virulent SPy_0444 hypothetical protein Metabolism Molecule Non-virulent mutT protein mutator Information and Storage Non-virulent mutX 7,8-dihydro-8-oxoguanine-triphosphatase Cellular process Non-virulent gpmA phosphoglyceromutase; Catalyzes the interconversion of 2-phosphoglycerate and 3- phosphoglycerate (By similarity) Cellular process Non-virulent Propionibacterium acnes KPA171202 PPA1717 hypothetical protein Cellular process Non-virulent PPA0225 NTP pyrophosphohydrolase Metabolism Molecule virulent PPA1781 7,8-dihydro-8-oxoguanine-triphosphatase Cellular process Non-virulent PPA0342 hypothetical protein Metabolism Molecule Non-virulent PPA2032 MutT/NUDIX family proteins Cellular process virulent B Cell Epitope Analysis The analysis of B cell epitopes is a continuation of the study of functional classes and virulence properties of Staphylococcus aureus Mu50, Streptococcus pyogenes M1 GAS, and Propionibacterium acnes KPA171202 proteins that interact with phytate compounds, in this 734 Biology, Medicine, & Natural Product Chemistry 14 (2), 2025: 731-738 step using Bepipred with virulent proteins from each interaction. Analysis of each protein revealed the presence of amino acid sequences that can bind to B cells. The study results can be seen from the emergence of graphs with different colors: yellow and green. The yellow graph indicates an interaction between the protein compound and the B cell epitope. The result of Cell Epitops can be seen in Figure 2. A B C D E F Figure 2. Result of B Cell Epitopes on (A) SAV1338, (B) SAV1118, (C) Spy_2186, (D) Spy_2170, (E) PPA0225, (F) PPA2032 Information : a. Yellow peaks indicate sequences that have potential epitopes. b. Green peaks indicate sequences that do not have potential epitopes. MHC I and MHC II Analysis The results of MHC I epitope prediction analysis using the HLA-A*11:01 allele with a peptide strand length of 9 amino acids. Moreover, MHC II epitope analysis, peptides with a length of 15 amino acids were identified using the HLA-DRB1*04:01 allele. The highest score indicates the potential for virulent bacterial proteins to bind to T cells. The resulting binding score also shows strong affinity, so there is a high potential for interaction between proteins and MHC I that allows recognition by T cells. All results are presented in tabular form, and the top five of all peptides with the highest score were taken. The results of the analysis are shown in Table 2 and Table 3. Table 2. MHC I Analysis Results. Protein Allele Start End Length Peptides Score Percentile Rank SAV1118 HLAA*11:01 141 149 9 SINPEPSFK 0.96 0.01 HLAA*11:01 178 186 9 QVYSDQQSK 0.88 0.03 HLAA*11:01 100 108 9 NSYYIVSTK 0.76 0.09 HLAA*11:01 83 91 9 RVYPFRDGY 0.67 0.16 HLAA*11:01 234 242 9 VTNEMRKLK 0.59 0.21 SAV1338 HLAA*11:01 16 24 9 IIAPITEFK 0.91 0.02 HLAA*11:01 84 92 9 VTFNEYGTK 0.73 0.12 HLAA*11:01 228 236 9 KQHQLSTLK 0.55 0.25 HLAA*11:01 239 247 9 KQNSETARK 0.48 0.31 HLAA*11:01 300 308 9 LMNSIGHRK 0.44 0.36 Spy_2170 HLAA*11:01 53 131 9 ILNDESIAK 0.53 0.26 HLAA*11:01 59 67 9 LLFTDPVYY 0.21 0.85 HLAA*11:01 9 17 9 QAKPLGEEK 0.11 1.3 HLAA*11:01 50 58 9 KVFIVPLRQ 0.09 1.4 HLAA*11:01 40 118 9 WGMTAQFTK 0.07 1.7 Spy_2186 HLAA*11:01 5 13 9 LVSPLEDPK 0.30 0.59 HLAA*11:01 27 35 9 GFQSINWIK 0.05 2.0 HLAA*11:01 22 30 9 GGTSLVGEK 0.02 2.9 HLAA*11:01 30 38 9 KTHETVLRE 0.01 3.4 Zhofiroh et al. – The Antivirulence Mechanisms of Phytate … 735 HLAA*11:01 4 12 9 RNGKNFLTR 0.01 3.4 PPA0225 HLAA*11:01 35 43 9 HVLDALLDR 0.35 0.5 HLAA*11:01 10 18 9 TTRHPSGYR 0.24 0.75 HLAA*11:01 9 17 9 ATTRHPSGY 0.18 0.94 HLAA*11:01 5 13 9 SALIASLGR 0.17 0.96 HLAA*11:01 45 53 9 LTRRPLSLR 0.11 1.3 PPA2032 HLAA*11:01 35 43 9 RTCLNVRKK 0.44 0.36 HLAA*11:01 5 13 9 LVLDPDDLK 0.34 0.52 HLAA*11:01 9 17 9 VTWRDGSGR 0.20 0.88 HLAA*11:01 4 12 9 SVQCVVTWR 0.18 0.94 HLAA*11:01 76 84 9 RVIPALQQQ 0.13 1.2 Table 3. MHC II Analysis Result. Protein Allele Start End Length Peptide Score Percentile Rank SAV1118 HLA-DRB1*04:01 202 216 15 IEPYQLNSNSTSEEH 0.90 0.20 HLA-DRB1*04:01 173 187 15 GDIYAQVYSDQQSKK 0.89 0.20 HLA-DRB1*04:01 201 215 15 DIEPYQLNSNSTSEE 0.87 0.28 HLA-DRB1*04:01 172 186 15 YGDIYAQVYSDQQSK 0.82 0.53 HLA-DRB1*04:01 99 113 15 KNSYYIVSTKREEIV 0.81 0.60 SAV1338 HLA-DRB1*04:01 234 248 15 TLKYSKQNSETARKH 0.90 0.20 HLA-DRB1*04:01 233 247 15 STLKYSKQNSETARK 0.87 0.28 HLA-DRB1*04:01 152 166 15 KGRVRYEQNNKEYDV 0.85 0.33 HLA-DRB1*04:01 151 165 15 VKGRVRYEQNNKEYD 0.82 0.49 HLA-DRB1*04:01 235 249 15 LKYSKQNSETARKHS 0.73 1.20 Spy_2170 HLA-DRB1*04:01 134 148 15 PVYYRLEVTPIETTD 0.93 0.13 HLA-DRB1*04:01 133 147 15 DPVYYRLEVTPIETT 0.87 0.26 HLA-DRB1*04:01 132 146 15 TDPVYYRLEVTPIET 0.76 0.96 HLA-DRB1*04:01 135 149 15 VYYRLEVTPIETTDF 0.71 1.30 HLA-DRB1*04:01 106 120 15 VDDWKSIQPNEEVDK 0.71 1.40 Spy_2186 HLA-DRB1*04:01 69 83 15 NIEFHYLVSPLEDPK 0.87 0.28 HLA-DRB1*04:01 68 82 15 HNIEFHYLVSPLEDP 0.79 0.79 HLA-DRB1*04:01 70 84 15 IEFHYLVSPLEDPKL 0.67 1.60 HLA-DRB1*04:01 81 95 15 DPKLEMIENASDRFV 0.64 1.80 HLA-DRB1*04:01 80 94 15 EDPKLEMIENASDRF 0.61 1.90 PPA0225 HLA-DRB1*04:01 136 150 15 RVRLADLANPAARAT 0.65 1.70 HLA-DRB1*04:01 135 149 15 QRVRLADLANPAARA 0.62 1.90 HLA-DRB1*04:01 134 148 15 VQRVRLADLANPAAR 0.43 3.90 HLA-DRB1*04:01 28 42 15 RSSAVLALISEEGND 0.35 5.30 HLA-DRB1*04:01 29 43 15 SSAVLALISEEGNDI 0.32 5.80 PPA2032 HLA-DRB1*04:01 79 93 15 PDDLKHLGTFDAPAA 0.48 3.20 HLA-DRB1*04:01 78 92 15 DPDDLKHLGTFDAPA 0.38 4.60 HLA-DRB1*04:01 109 123 15 WREIWPEPVPDSEIV 0.38 4.70 HLA-DRB1*04:01 108 122 15 NWREIWPEPVPDSEI 0.33 5.70 HLA-DRB1*04:01 52 66 15 GGKIELGETPLEAAI 0.32 5.80 Next, the subcellular location of each virulent protein was analyzed using PSORTB. The results of the study are shown in Table 4. Table 4. Subcellular Location Analysis Results. Organism Identification Code Protein Name Subcellular Location Staphylococcus aureus Mu50 SAV1338 Hypothetical protein Unknown SAV1118 Hypothetical protein Unknown Streptococcus pyogenes M1 GAS SPy_2186 Hypothetical protein Cytoplasmic SPy_2170 Hypothetical protein Cytoplasmic Propionibacterium acnes KPA171202 PPA0225 NTP pyrophosphohydrolase Cytoplasmic membrane PPA2032 MutT/NUDIX family protein Cytoplasmic DISCUSSION In the Staphylococcus aureus Mu50, the identified proteins include gpmA, SAV2490, SAV1499, SAV1338, SAV1794, SAV0240, and SAV1118. Meanwhile, in Streptococcus pyogenes M1 GAS, the proteins involved were SPy_0477, SPy_2186, gpmA, mutX, mutT, SPy_0444, and SPy_2170. Whereas in 736 Biology, Medicine, & Natural Product Chemistry 14 (2), 2025: 731-738 Propionibacterium acnes KPA171202, the proteins involved include PPA0342, PPA2032, PPA1389, gpmA, PPA1717, PPA0225, and PPA1781. In the Staphylococcus aureus Mu50 strain, there is a close relationship between SAV1794, SAV0240, and SAV1118 proteins. According to the theory proposed by Pevsner, interactions between proteins usually occur between proteins with similar functions or structures. This strengthens the possibility that this group of proteins forms a specific functional network that can be disrupted by interactions with phytate compounds (Abdullah et al., 2022). Based on the results, most of the proteins from the three bacteria had major functions in cellular processes and the metabolism of molecules. Proteins such as gpmA found in all three bacterial strains were known to play an important role in the glycolytic pathway. In addition, some proteins were also categorized in the information and storage function, which is related to genetic regulation and biological information storage mechanisms. Phytate can interfere with bacterial metabolic pathways by binding and removing essential metals that support bacterial survival and proliferation. In bioinformatics studies of skin pathogens, phytate was found to interact with several specific proteins in Staphylococcus aureus, Streptococcus pyogenes, and Propionibacterium acnes. Phytate not only functions as an antioxidant and anti-inflammatory agent but also as an antivirulence agent that intervenes in the metabolic pathways of bacteria. By binding to key proteins in energy metabolism and virulence, phytate can weaken the ability of bacteria to survive and cause infection (Pires et al., 2023a). The next analysis looked at the virulent properties of each protein and only had two proteins that had virulent properties, namely: SAV 1338, SAV1118 (Staphylococcus aureus Mu50), SPy_2186, Spy_2170 (Streptococcus pyogenes), and PPA0225, PPA2032 (Propionibacterium acnes KPA171202). This is in line with research conducted by Yamazaki et al, where proteins in Staphylococcus aureus play a role in pathogenesis by regulating immune evasion mechanisms and facilitating biofilm formation, which facilitates infection and bacterial immunity to antibiotic therapy (Yamazaki et al., 2024). Phytate has the potential to interact with these virulent proteins, thereby inhibiting metabolic processes that are essential for bacterial survival. This indicates that phytate has the potential to be an antivirulence agent that can reduce the virulence of bacteria such as Streptococcus pyogenes.(Pires et al., 2023b) B cell epitopes are segments of antigens recognized by antibodies in the immune system, and epitope analysis can identify specific regions on proteins that can trigger humoral immune responses (Sun et al., 2024). The analysis showed various peptide strands that can trigger immune responses, with a length of up to 66 amino acids in some proteins, such as SAV1338 and SAV1118. It is in line with previous studies where epitopes on Staphylococcus aureus, Streptococcus pyogenes and Propionibacterium acnes can be effective targets in the development of antibody-based vaccines (Ozberk et al., 2018). Major Histocompatibility Complex (MHC)-related analysis consisting of two classes: MHC I binding predictions and MHC II binding predictions of virulent proteins. Peptides, or epitopes, are expressed on the surface of nucleated cells by Major Histocompatibility Complex (MHC) molecules in T cells. A key requirement for T cell activation is molecular recognition between the T cell receptor (TCR) expressed on the T cell surface and the peptide-MHC complex (pMHC) described on the surface of other cells. Without T-cell activation, an immune response cannot be mounted and initiated. A mechanism known as central tolerance is responsible for this process (Schaap-Johansen et al., 2021). The results of the MHC epitope analysis presented in Table 2 and Table 3 show that the six proteins derived from the three pathogenic bacteria have strong binding affinities to MHC I and MHC II. MHC I presents itself as an antigen and induces CD8+ T cells, while MHC II presents itself as an antigen and induces CD4+ T cells. After being induced, the cytotoxic antigen-specific immune system of T cells was mediated by the activity of CD8+ T cells, and B cells became B cell memory due to the activity of CD4+ T cells. The combination of the two mechanisms formed antibodies that damage spike proteins so that an adaptive immune system is formed to fight bacteria (Suzana et al., 2022). As described in previous studies, the activation of CD8+ and CD4+ T cells has an important role in fighting bacterial infections, where CD4+ T cells facilitate the production of antibodies by B cells (Shepherd & McLaren, 2020). The interaction between MHC and antigen epitopes on the surface of T cells is essential to enhance the effectiveness of immune responses, which suggests that phytate through its ability to intervene in bacterial metabolic pathways and enhance antigen binding, may play a role in enhancing immune responses to bacterial infections. Subsequently, the subcellular location of the proteins with virulence properties was analyzed using PSORTb. Of most of the six proteins, three proteins were located in the cytoplasmic membrane (SPy_2186, SPy_2170, and PPA2032), one was located in the cytoplasmic membrane (PPA0225), and there were two proteins from Staphylococcus aureus Mu50 that were targeted by phytate compounds, the exact location of the subcellular location was not found. Zhofiroh et al. – The Antivirulence Mechanisms of Phytate … 737 CONCLUSION This bioinformatics study on the antivirulence mechanism of phytate against Staphylococcus aureus strain Mu50, Streptococcus pyogenes M1 GAS, and Propionibacterium acnes strain KPA171202 reveals that phytate interacts molecularly with virulent proteins in these pathogenic bacteria. Functional analysis showed that these proteins are primarily involved in cellular processes and metabolic functions, with additional roles in information and storage for Staphylococcus aureus and Streptococcus pyogenes. Epitopic analysis revealed the presence of B-cell epitopes and peptide strands with T-cell affinity. At the same time, subcellular localization placed virulent proteins in the cytoplasm for Streptococcus pyogenes and in the cytoplasmic membrane and cytoplasm for Propionibacterium acnes. However, localization for Staphylococcus aureus remains unconfirmed. Further experimental validation in the laboratory to confirm these findings is recommended, alongside additional research into the virulence factors of phytate and its effects on other pathogenic bacteria and compounds in rice. Acknowledgements: We would like to express our sincere gratitude to all individuals and institutions that contributed to the success of this research. Special thanks go to the faculty of Medicine Palangka Raya University Wet Biomedical Laboratory for providing the facilities and resources necessary to conduct this study. We also thank colleagues and team members for their invaluable support and collaboration throughout the research process. Authors’ Contributions: Nabilatul Zhofiroh & Rian Ka Praja designed the study. Nabilatul Zhofiroh, Rian Ka Praja and Elsa Trinovita analyzed the data. Nabilatul Zhofiroh, Rian Ka Praja, Elsa Trinovita, Ysrafil, and Ranintha BR Surbakti wrote the manuscript. All authors read and approved the final version of the manuscript. 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