PREDICTION OF LYSINE SUCCINYLATION SITE USING PROTEIN LANGUAGE MODEL

Abstract

Lysine succinylation is an important post-translational modification(PTM) that controls protein shape, function, and physiochemical properties and has an effect on metabolic processes, the incidence of many diseases, and their progression. Several experimental and computational approaches have been proposed. This method uses a protein Language Model (pLMs) to extract features from protein sequences and convolution neural network (CNN) with artificial neural networks to predict succinylation. This method used two protein Language Models which are ProtBert and ProtT5. The protein sequences are fed to the model to develop a different set of protein embeddings. The embeddings from different pLMs were found to have negligible similarity. The embeddings are fed to 1DCNN Neural networks and the outputs from the networks are stacked and ANN is trained on top of that. The stacking ensemble has improved the performance of our proposed architecture. On comparison using benchmarking dataset, our method was comparable with other state of the art models on nearly every metric.