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How to find simple and accurate rules for viral protease cleavage specificities.

Rögnvaldsson T, Etchells TA, You L, Garwicz D, Jarman I, Lisboa PJ - BMC Bioinformatics (2009)

Bottom Line: However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate.The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease.A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users.

View Article: PubMed Central - HTML - PubMed

Affiliation: Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se

ABSTRACT

Background: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.

Results: A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.

Conclusion: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.

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Performance of OSRE rules on the HCV NS3 data set. The performance of the OSRE rules on the 939 peptide HCV NS3 data. The x-axis shows the number of rules used in the prediction. The y-axis shows the out-of-sample accuracy (%). The error bars are 1.96 times the binomial standard deviations. The horizontal lines show the accuracy when all rules are used. The accuracy for zero rules is the default accuracy, when all peptides are classified as the majority class.
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Figure 5: Performance of OSRE rules on the HCV NS3 data set. The performance of the OSRE rules on the 939 peptide HCV NS3 data. The x-axis shows the number of rules used in the prediction. The y-axis shows the out-of-sample accuracy (%). The error bars are 1.96 times the binomial standard deviations. The horizontal lines show the accuracy when all rules are used. The accuracy for zero rules is the default accuracy, when all peptides are classified as the majority class.

Mentions: The OSRE method was applied to the HCV NS3 data in a corresponding way as for the HIV-1 PR data. The CV out-of-sample error as a function of the number of rules is shown in Figure 5. The accuracy when using all rules is 95%. This is close to the performance of a non-rule-based linear support vector machine classifier with tuned "slack", which has an accuracy of 97%, and it is the hitherto best result using a rule based method for HCV NS3. The consensus rules for HCV NS3 protease are listed in Table 6. Their fidelity, shown in Table 7, is similar to the out of sample accuracy (95%).


How to find simple and accurate rules for viral protease cleavage specificities.

Rögnvaldsson T, Etchells TA, You L, Garwicz D, Jarman I, Lisboa PJ - BMC Bioinformatics (2009)

Performance of OSRE rules on the HCV NS3 data set. The performance of the OSRE rules on the 939 peptide HCV NS3 data. The x-axis shows the number of rules used in the prediction. The y-axis shows the out-of-sample accuracy (%). The error bars are 1.96 times the binomial standard deviations. The horizontal lines show the accuracy when all rules are used. The accuracy for zero rules is the default accuracy, when all peptides are classified as the majority class.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2698905&req=5

Figure 5: Performance of OSRE rules on the HCV NS3 data set. The performance of the OSRE rules on the 939 peptide HCV NS3 data. The x-axis shows the number of rules used in the prediction. The y-axis shows the out-of-sample accuracy (%). The error bars are 1.96 times the binomial standard deviations. The horizontal lines show the accuracy when all rules are used. The accuracy for zero rules is the default accuracy, when all peptides are classified as the majority class.
Mentions: The OSRE method was applied to the HCV NS3 data in a corresponding way as for the HIV-1 PR data. The CV out-of-sample error as a function of the number of rules is shown in Figure 5. The accuracy when using all rules is 95%. This is close to the performance of a non-rule-based linear support vector machine classifier with tuned "slack", which has an accuracy of 97%, and it is the hitherto best result using a rule based method for HCV NS3. The consensus rules for HCV NS3 protease are listed in Table 6. Their fidelity, shown in Table 7, is similar to the out of sample accuracy (95%).

Bottom Line: However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate.The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease.A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users.

View Article: PubMed Central - HTML - PubMed

Affiliation: Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se

ABSTRACT

Background: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.

Results: A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.

Conclusion: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.

Show MeSH
Related in: MedlinePlus