Name
Title | ||
|---|---|---|
iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. |
Abstract | ||
|---|---|---|
Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProtSub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bib/bby028 | BRIEFINGS IN BIOINFORMATICS |
Keywords | Field | DocType |
protease,substrate,cleavage site,sequence analysis,machine learning,five-step rule | Data mining,Text mining,Biology,Protease,Sequence analysis,Cleavage (embryo) | Journal |
Volume | Issue | ISSN |
20 | 2 | 1467-5463 |
Citations | PageRank | References |
5 | 0.43 | 40 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangning Song | 1 | 374 | 41.93 |
| Yanan Wang | 2 | 44 | 11.61 |
| Fuyi Li | 3 | 97 | 11.25 |
| Tatsuya Akutsu | 4 | 2169 | 216.05 |
| Neil D. Rawlings | 5 | 266 | 28.76 |
| Geoffrey I. Webb | 6 | 3130 | 234.10 |
| Kuo-Chen Chou | 7 | 946 | 64.26 |