Methods: Zoonomix integrates a curated database of ~25,000 genes related to adherence, biofilm formation, efflux pumps, exotoxins, resistance, integrative and conjugative elements (ICEs), and secretion systems (T3SS, T4SS, and T6SS). It uses BLASTN and a scoring algorithm to assess pathogenicity and HGT risk, classifying bacterial strains into low, moderate, or high risk, with insights into antibiotic resistance migration.

Results: When analyzing 60 whole genome sequences of both zoonotic and non-zoonotic bacterial species using the Zoonomix pipeline, over 90% of the results were accurately classified in accordance with existing literature. Notably, the pipeline predicted a potential future zoonotic and pathogenic capability for bacterial species such as A. pleuropneumoniae and M. haemolytica.

Discussion: Zoonomix offers a comprehensive framework for assessing zoonotic potential and antibiotic resistance by integrating genomics, bioinformatics, and predictive analytics. Its ability to detect current gene status, identify mutation-prone genes, summarize mutation hotspots, and flag horizontal gene transfer events make it a valuable tool for disease surveillance and outbreak prevention.

Conclusion: Zoonomix is a scalable, open-source bioinformatics pipeline designed to assess the zoonotic potential and antimicrobial resistance (AMR) risk in bacterial whole genome sequences. By detecting key genes associated with zoonosis, identifying markers that predict the future pathogenic or zoonotic potential of bacteria, and flagging integrative conjugative element (ICEs)- mediated resistance gene transfer mechanisms, the tool provides comprehensive insights into bacterial threats. The pipeline's source code and documentation are freely available for the research community at the following GitHub repository: https://github.com/Umeshkumarku1/ZoonomiX.]]> https://www.benthamscience.com/article/1540282026-04-24 Background: The estimation of peptides binding to the Human Leukocyte Antigen (HLA) has significant implications for rational vaccine design and other immunotherapies. The present work employs an Inverse folding (IF) approach to estimate HLA-binding peptides and to establish a correlation between experimental binding affinity and the estimated interaction potential score of HLAA* 0201-peptide interactions.

Methods: The interaction potential score of a peptide is computed as the total energy of interaction with the contact residues of the HLA and peptide using statistical pairwise contact potentials, namely Miyazawa-Jernigan (MJ), and Betancourt-Thirumalai (BT).

Results: The results showed a negative correlation between computed MJ and BT interaction potential scores and experimental binding affinity of peptides to the HLA-A*0201 molecule. BT scores correlate better with the experimental binding affinities of peptides, as indicated by Pearson's correlation coefficient (-0.568, R² =0.322, p < 0.001), compared to MJ scores (-0.457, R2 =0.208, p < 0.001).

Discussion: Since the HLA class I molecules have specificity for certain amino acid side chains in distinct binding pockets within the binding cleft, the structural templates of peptide-bound HLA molecules may provide a means to interrogate the interactions between HLA and peptide. IF indirectly explores the underlying fitness landscape by focusing exploration on regions where the protein's backbone fold is preserved. Given the involvement of multiple template structures for combinations of peptides and HLA alleles, the IF approach is expected to be more robust.

Conclusion: The IF approach demonstrates potential for evaluating the intricate assembly of HLA molecules with any novel peptide, which holds significant relevance for vaccine development.]]> https://www.benthamscience.com/article/1520762026-04-24 Introduction: Improvement in food processing can be achieved by applying a bioemulsifier produced from microorganisms isolated from overripe fruit. The study aims to isolate, optimize, characterize, and determine the application of a bioemulsifier from Candida tropicalis MN450877.1, obtained from overripe bananas.

Methods: Candida tropicalis MN450877.1 was identified using 16S rRNA sequencing and BLAST analysis. The autoclaving extraction procedure was optimized using a 5-level, 2-factor central composite design (CCD). Purification was conducted via Sephadex G-50 gel filtration chromatography. Comprehensive characterization was performed using HPLC, SEM-EDX, FTIR, 1H and 13C NMR, and SDS-PAGE. The bioemulsifier was applied as a food additive in mayonnaise production at a concentration of 0.5% (w/v) and evaluated for its sensory and toxicological properties.

Results: The bioemulsifier composition exhibited similarity to structural mannoprotein, with 11.32% protein and 88.68% carbohydrate. The optimized conditions, at 121 ºC for 120 min, yielded 0.232 g per 0.3 g dry cells, with an emulsification index of 55.5%, a protein content of 0.488 mg/mL, and a carbohydrate content of 5.4 mg/g. SDS-PAGE revealed a molecular mass of 38 kDa. FTIR confirmed the presence of glycoprotein, while HPLC revealed various molecular weight fractions, including monosaccharides, polysaccharides, and glycoproteins. SEM-EDX analysis confirmed porous, aggregated biopolymer structures with an elemental composition predominantly consisting of carbon, oxygen, nitrogen, and phosphorus. ¹H NMR validated glycoprotein structures with characteristic functional groups. Toxicity evaluation indicated an LD50 above 5000 mg/kg. Sensory analysis demonstrated functional properties comparable to those of commercial emulsifiers.

Discussion: Physicochemical, structural, and functional analyses jointly support the classification of the purified compound as a glycoprotein-based bioemulsifier. The absence of toxicity at high doses supports its safety; however, long-term assessments and broader application across food matrices are recommended.

Conclusion: The bioemulsifier from Candida tropicalis MN450877.1 demonstrates promising emulsification, physicochemical, toxicological, and sensory properties, supporting its potential use as a food additive.