| Autor: |
Sai Chetan K. Sukuru, Zhan Deng, Natasja Brooijmans, Jean Quancard, Florian Nigsch, Meir Glick, Martin Renatus, Jeremy L. Jenkins, John W. Davies, Rajiv Chopra, Ulrich Hommel, Dmitri Mikhailov, Allen Cornett |
| Rok vydání: |
2010 |
| Předmět: |
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| Zdroj: |
Protein Science. 19:2096-2109 |
| ISSN: |
0961-8368 |
| Popis: |
We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naive Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1–P1′) but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors—namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naive Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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