• Corpus ID: 246294777

Predicting Succinylation Sites in Proteins with Improved Deep Learning Architecture

@article{Odeyomi2021PredictingSS,
  title={Predicting Succinylation Sites in Proteins with Improved Deep Learning Architecture},
  author={Olusola Tolulope Odeyomi and Gergely V. Z{\'a}ruba},
  journal={ArXiv},
  year={2021},
  volume={abs/2201.11215}
}
Post-translational modifications (PTMs) in proteins occur after the process of translation. PTMs account for many cellular processes such as deoxyribonucleic acid (DNA) repair, cell signaling and cell death. One of the recent PTMs is succinylation. Succinylation modifies lysine residue from −1 to +1. Locating succinylation sites using experimental methods, such as mass spectrometry is very laborious. Hence, computational methods are favored using machine learning techniques. This paper proposes… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 10 REFERENCES

DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction

DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure, is developed and results suggest that the method represents a robust and complementary technique for advanced exploration of protein succinylisation.

SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties.

A novel computational tool termed SuccinSite has been developed to predict protein succinylation sites by incorporating three sequence encodings, i.e., k-spaced amino acid pairs, binary and amino acid index properties, and performs significantly better than existing predictors on a comprehensive independent test set.

pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach.

A systematic identification of species-specific protein succinylation sites using joint element features information

The proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset and the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed.

GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features

The generic and nine species-specific succinylation site classifiers are developed through aggregating multiple complementary features using the Wilcoxon-rank feature selection scheme and the resulting predictor termed GPSuc achieved better performance than other existing generic and species- specific succinylated site predictors.

Long Short-Term Memory

A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.

ImageNet classification with deep convolutional neural networks

A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.

Detecting Succinylation sites from protein sequences using ensemble support vector machine

A new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acids composition, with a feature selection scheme is developed.

iSuc-PseAAC: predicting lysine succinylation in proteins by incorporating peptide position-specific propensity

A new predictor called iSuc-PseAAC was proposed by incorporating the peptide position-specific propensity into the general form of pseudo amino acid composition and demonstrated by rigorous leave-one-out on stringent benchmark dataset that the new predictor is quite promising.