Succinylated lysine residue prediction revisited

Abstract

MotivationLysine succinylation is a kind of post-translational modification (PTM) which plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy and attention has been given in the literature for developing such models, albeit with only moderate success in the context of different evaluation metrics. One important aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids, while some others used them without considering the inter-dependency among the properties. ResultsThe combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by the combination of all the properties. We propose three deep learning architectures, CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC is outperforming the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we find that we successfully achieve better sensitivity and specificity through varying the threshold value. Availabilityhttps://github.com/Dariwala/Succinylation-with-biophysico-and-deep-learning Contactmsrahman@cse.buet.ac.bd