ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives. Ubiquitination, the conserved proteasome system, is a PTM which relates to numerous biological processes, such as protein degradation, endocytosis, and cell cycle. Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. In fact, some sites that undergo ubiquitination have not been identified. Hence, these machine learning-based prediction methods suffer from no reliable database of non-ubiquitination sites. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models. In this work, we propose an evolutionary screening algorithm (ESA) to select effective negatives from among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy, better than existing prediction methods.

FASTA format (e.g., example.fa)

git clone https://github.com/NctuICLab/ESA-UbiSite.git
cd ESA-UbiSite

build AAIndex

cd src/aaindex
make

build LIBSVM

cd src/libsvm_320
make

create a new folder for the new analysis

mkdir example_output

perl ESAUbiSite_main.pl example.fa example_output

results of example.fa -> example_output/ESAUbiSite_prediction.html ESAUbiSite_prediction.html

• 1st: accession number of proteins
• 2nd: residue
• 3rd: position

• All UbiD protein sequences in FASTA format with sequence identity <= 20%
• Index file of all lysine residues in UbiD
• Index file of ubiquitinated lysine residues in UbiD
• All ubiquitination sites with window size 21
• All non-validated sites with window size 21

ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives. Bioinformatics, 2017 [PMID:28062441]

• Shinn-Ying Ho: [email protected]
• Ming-Ju Tsai: [email protected]
• Jyun-Rong Wang: [email protected]