PREDICTION OF LYSINE SUCCINYLATION SITE USING PROTEIN LANGUAGE MODEL
Date
2023-03
Authors
THAKUR, ANANDA
SIMKHADA, PRADIPTI
ADHIKARI, SHAMBHAVI
MAHARJAN, SUBIN
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
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.
Description
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.
Keywords
Post-translational modification,, Lysine Succinylation,, Protein Language Model