Characterizing Protease Specificity: How Many Substrates Do We Need?

• Published in: PloS one Vol. 10; no. 11; p. e0142658
• Main Authors: Schauperl, Michael, Fuchs, Julian E, Waldner, Birgit J, Huber, Roland G, Kramer, Christian, Liedl, Klaus R
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.11.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Amino Acid Sequence; Amino acids; Analysis; Bayes Theorem; Bioinformatics; Cleavage; Computational Biology; Data points; Deoxyribonucleic acid; DNA; Drug Design; Drug development; Endopeptidases - chemistry; Endopeptidases - metabolism; Entropy (Information theory); Enzyme regulation; Estimates; Estimation; Factor Xa - chemistry; Humans; Innovations; Linear Models; Mathematical analysis; Mathematical models; Models, Biological; Numerical stability; Observations; Peptide Hydrolases - chemistry; Peptide Library; Peptides - chemistry; Pharmaceutical industry; Properties; Protease; Proteases; Proteinase; Proteins; Stability analysis; Statistical analysis; Statistical analysis of data; Statistical methods; Statistics; Substrate Specificity; Substrates; Technology application; Theory; Thrombin - chemistry; Trypsin - chemistry; Uncertainty; Tyrol (State in Austria); Austria; Singapore
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0142658

Calculation of cleavage entropies allows to quantify, map and compare protease substrate specificity by an information entropy based approach. The metric intrinsically depends on the number of experimentally determined substrates (data points). Thus a statistical analysis of its numerical stability is crucial to estimate the systematic error made by estimating specificity based on a limited number of substrates. In this contribution, we show the mathematical basis for estimating the uncertainty in cleavage entropies. Sets of cleavage entropies are calculated using experimental cleavage data and modeled extreme cases. By analyzing the underlying mathematics and applying statistical tools, a linear dependence of the metric in respect to 1/n was found. This allows us to extrapolate the values to an infinite number of samples and to estimate the errors. Analyzing the errors, a minimum number of 30 substrates was found to be necessary to characterize substrate specificity, in terms of amino acid variability, for a protease (S4-S4') with an uncertainty of 5 percent. Therefore, we encourage experimental researchers in the protease field to record specificity profiles of novel proteases aiming to identify at least 30 peptide substrates of maximum sequence diversity. We expect a full characterization of protease specificity helpful to rationalize biological functions of proteases and to assist rational drug design.