De Novo PETase-X Egehan bayram

Plastic pollution, particularly from polyethylene terephthalate (PET), has remained intact in ecosystems for over acentury, contributing to the escalating global environmental crisis. Existing solutions, such as recycling and awarenesscampaigns, have proven inadequate due to their high costs and excessive reliance on human behavior. This pollutionis projected to pose serious threats to biodiversity, potentially affecting animals, plants, fungi, and, consequently, entireecosystems. The entry of microplastics into water resources and the food chain directly impacts not only nature butalso human health. Furthermore, the lingering of plastics in the environment without decomposing further increasesgreenhouse gas emissions, accelerating global warming. This process is accelerating every year, threatening thehabitability of our planet.In this computational study, a novel enzyme sequence with predicted PET degradation potential was generated usingthe Gen-Medium artificial intelligence model. Molecular simulations were performed with GROMACS, and theinteractions of more than 2,000 ligands with PET components, including terephthalic acid, adipic acid, and bisphenolA, were screened. Multiple sequence alignment (MSA) was performed with the UniRef50 database to identifyconserved regions related to enzyme stability and function.A custom machine learning (ML) model was developed for automated molecular docking and binding affinityassessment. Five protein candidates with the strongest binding scores were computationally shortlisted. Subsequentmolecular dynamics simulations suggested stable interactions with water molecules and potential structural integrityin mutant forms.