Name
Abstract | ||
|---|---|---|
Proteases have central roles in "life and death" processes due to their important ability to catalytically hydrolyze protein substrates, usually altering the function and/or activity of the target in the process. Knowledge of the substrate specificity of a protease should, in theory, dramatically improve the ability to predict target protein substrates. However, experimental identification and characterization of protease substrates is often difficult and time-consuming. Thus solving the "substrate identification" problem is fundamental to both understanding protease biology and the development of therapeutics that target specific protease-regulated pathways. In this context, bioinformatic prediction of protease substrates may provide useful and experimentally testable information about novel potential cleavage sites in candidate substrates. In this article, we provide an overview of recent advances in developing bioinformatic approaches for predicting protease substrate cleavage sites and identifying novel putative substrates. We discuss the advantages and drawbacks of the current methods and detail how more accurate models can be built by deriving multiple sequence and structural features of substrates. We also provide some suggestions about how future studies might further improve the accuracy of protease substrate specificity prediction. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1142/S0219720011005288 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
Proteases,substrate specificity,substrate cleavage site,bioinformatics,sequence analysis,machine learning,support vector machine,feature selection,structural information | Substrate (chemistry),Proteases,Biology,Biochemistry,Target protein,Protease,Bioinformatics,Sequence analysis,Cleavage (embryo) | Journal |
Volume | Issue | ISSN |
9 | 1 | 0219-7200 |
Citations | PageRank | References |
6 | 0.53 | 37 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangning Song | 1 | 374 | 41.93 |
| Hao Tan | 2 | 30 | 3.87 |
| Sarah E Boyd | 3 | 62 | 4.88 |
| Hongbin Shen | 4 | 533 | 48.23 |
| Khalid Mahmood | 5 | 270 | 39.43 |
| Geoffrey I. Webb | 6 | 3130 | 234.10 |
| Tatsuya Akutsu | 7 | 2169 | 216.05 |
| James C Whisstock | 8 | 93 | 7.90 |
| Robert N Pike | 9 | 32 | 2.19 |