PREDICTION OF PLASTIC DEGRADING ENZYMES WITH PROTEIN SEQUENCES AND 3D STRUCTURES INTEGRATION USING CONVOLUTIONAL NEURAL NETWORK

Abstract

The growing problem of plastic waste has made the discovery of plastic-degrading enzymes (PDEs) essential, requiring innovative computational solutions. This study proposes a deep learning framework to predict plastic-degrading enzymes (PDEs) by integrating features from protein sequence embeddings and 3D structures. A curated dataset of 1,791 protein sequences consisting both plastic degrading enzymes and plastic non-degrading enzyme sequences were analyzed. ESM-2 language model representations were obtained for the sequences, while structural features were computed from AlphaFold2-predicted structures via graph neural networks. These multimodal features were fed into a Convolutional Neural Network (CNN) achieving an accuracy of 97.7% and an F1 score of 0.94, representing the state of the art. The trained model was used to predict a list of twenty-one (21) unannotated enzymes. six of these unannotated proteins with UniProt IDs; A0A6J6HCC9, A0A6J7GSX4, A0A6J6XVW8, A0A6J7ECY4, A0A6J6SVN6, AOA6J6T2V9 showed a predictive degradative probability of over 70% probability. This study facilitates the identification of possible PDEs using integrated sequence and structural data, for more accurate enzyme classification, as well as sustainable environmental applications.