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Investigations on inhibitors of hedgehog signal pathway: a quantitative structure-activity relationship study.

Zhu R, Liu Q, Tang J, Li H, Cao Z - Int J Mol Sci (2011)

Bottom Line: Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively.From these, demethylation is the best choice for inhibitor structure modifications.Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China; E-Mails: rxzhu@tongji.edu.cn (R.Z.); qiliu@tongji.edu.cn (Q.L.).

ABSTRACT
The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.

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Related in: MedlinePlus

Six molecules that did not match any of the scaffolds, as mentioned above.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC3116172&req=5

f2-ijms-12-03018: Six molecules that did not match any of the scaffolds, as mentioned above.

Mentions: Since advanced machine learning methods such as ANN [22], Bayesian inference [23], Random Forest [24] and SVM [25] have been successfully applied in QSAR study [26–36], our QSAR models were rebuilt using the SVR method, which is a derived regression model with powerful fitting ability as well as excellent prediction accuracy [36–39]. In anticipating results, this method behaved well in the self-fitting testing of our training data (R2 is nearly 0.9) as well as in the cross-validation testing. Nevertheless, this method still performed badly in the independent test data, which indicates that such machine learning methods may not be generalized enough in the cyclopamine data. This is probably due to the fact that a substantial diversity exists in our dataset. Among the 93 data, four different scaffolds were found (Figure 1). In addition, there were still six molecules that did not match any of the scaffolds (Figure 2).


Investigations on inhibitors of hedgehog signal pathway: a quantitative structure-activity relationship study.

Zhu R, Liu Q, Tang J, Li H, Cao Z - Int J Mol Sci (2011)

Six molecules that did not match any of the scaffolds, as mentioned above.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3116172&req=5

f2-ijms-12-03018: Six molecules that did not match any of the scaffolds, as mentioned above.
Mentions: Since advanced machine learning methods such as ANN [22], Bayesian inference [23], Random Forest [24] and SVM [25] have been successfully applied in QSAR study [26–36], our QSAR models were rebuilt using the SVR method, which is a derived regression model with powerful fitting ability as well as excellent prediction accuracy [36–39]. In anticipating results, this method behaved well in the self-fitting testing of our training data (R2 is nearly 0.9) as well as in the cross-validation testing. Nevertheless, this method still performed badly in the independent test data, which indicates that such machine learning methods may not be generalized enough in the cyclopamine data. This is probably due to the fact that a substantial diversity exists in our dataset. Among the 93 data, four different scaffolds were found (Figure 1). In addition, there were still six molecules that did not match any of the scaffolds (Figure 2).

Bottom Line: Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively.From these, demethylation is the best choice for inhibitor structure modifications.Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China; E-Mails: rxzhu@tongji.edu.cn (R.Z.); qiliu@tongji.edu.cn (Q.L.).

ABSTRACT
The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.

Show MeSH
Related in: MedlinePlus