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

DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins

(2019) Hongli Fu et al.   BMC BIOINFORMATICS

A deep learning method to more accurately recall known lysine acetylation sites

(2019) Meiqi Wu et al.   BMC BIOINFORMATICS

Detecting Succinylation sites from protein sequences using ensemble support vector machine

(2018) Qiao Ning et al.   BMC BIOINFORMATICS

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

(2018) Md. Mehedi Hasan et al.   PLoS One

DeepPhos: prediction of protein phosphorylation sites with deep learning

(2018) Fenglin Luo et al.   BIOINFORMATICS

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

(2017) Md Mehedi Hasan et al.   International Journal of Nanomedicine

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

(2016) Jianhua Jia et al.   ANALYTICAL BIOCHEMISTRY

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

(2016) Jianhua Jia et al.   JOURNAL OF THEORETICAL BIOLOGY

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

(2016) Md. Mehedi Hasan et al.   Molecular BioSystems

Deep learning

(2015) Yann LeCun et al.   NATURE

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

(2015) Yan Xu et al.   Scientific Reports

Chemical and biological methods to detect post-translational modifications of arginine

(2013) Daniel J. Slade et al.   BIOPOLYMERS

Modeling Symmetric Macromolecular Structures in Rosetta3

(2011) Frank DiMaio et al.   PLoS One

I-TASSER: a unified platform for automated protein structure and function prediction

(2010) Ambrish Roy et al.   Nature Protocols