Document Detail

A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space.
MedLine Citation:
PMID:  20799349     Owner:  NLM     Status:  MEDLINE    
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 naïve 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 naïve 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.
Sai Chetan K Sukuru; Florian Nigsch; Jean Quancard; Martin Renatus; Rajiv Chopra; Natasja Brooijmans; Dmitri Mikhailov; Zhan Deng; Allen Cornett; Jeremy L Jenkins; Ulrich Hommel; John W Davies; Meir Glick
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Protein science : a publication of the Protein Society     Volume:  19     ISSN:  1469-896X     ISO Abbreviation:  Protein Sci.     Publication Date:  2010 Nov 
Date Detail:
Created Date:  2010-10-27     Completed Date:  2011-02-24     Revised Date:  2014-05-15    
Medline Journal Info:
Nlm Unique ID:  9211750     Medline TA:  Protein Sci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2096-109     Citation Subset:  IM    
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MeSH Terms
Bacterial Proteins / chemistry,  metabolism
Bayes Theorem
Computational Biology / methods*
Computer Simulation
Oligopeptides / chemistry
Peptide Hydrolases / chemistry*,  metabolism
Protein Structure, Tertiary
Reproducibility of Results
Viral Proteins / chemistry,  metabolism
Reg. No./Substance:
0/Bacterial Proteins; 0/Oligopeptides; 0/Viral Proteins; EC 3.4.-/Peptide Hydrolases

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