Phage Endolysins: Advances in the World of Food Safety

This project helps you develop and test bacteriophage‑derived endolysins as new biocontrol agents for food safety. Because antimicrobial resistance is growing, traditional chemical preservatives are becoming less effective. Endolysins work by cutting bacterial cell walls, so they can specifically target foodborne pathogens at any stage of production. Testing these enzymes in the lab is slow and uses a lot of resources. To speed things up, the project guides you in building a computational framework that uses machine learning and high‑throughput sequencing data to predict and evaluate new endolysin candidates. You will design a predictive pipeline that sorts endolysin sequences according to: – the type of bond they cleave (cleavage specificity)

– the range of bacterial hosts they can attack (target host range) – how well they hold up at high temperatures (thermal stability) By combining bioinformatics tools with supervised learning algorithms, the pipeline quickly finds highly stable, food‑grade enzymes. Detailed documentation lets you assess both natural and engineered endolysins, giving you a practical way to apply digital tools to food preservation, pathogen control, and quality assurance.

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