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dc.contributor.authorHuang, Liang-Tsungen_US
dc.contributor.authorGromiha, M. Michaelen_US
dc.contributor.authorHo, Shinn-Yingen_US
dc.date.accessioned2014-12-08T15:13:36Z-
dc.date.available2014-12-08T15:13:36Z-
dc.date.issued2007-08-01en_US
dc.identifier.issn1610-2940en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s00894-007-0197-4en_US
dc.identifier.urihttp://hdl.handle.net/11536/10510-
dc.description.abstractUnderstanding the mechanism of the protein stability change is one of the most challenging tasks. Recently, the prediction of protein stability change affected by single point mutations has become an interesting topic in molecular biology. However, it is desirable to further acquire knowledge from large databases to provide new insights into the nature of them. This paper presents an interpretable prediction tree method (named iPTREE-2) that can accurately predict changes of protein stability upon mutations from sequence based information and analyze sequence characteristics from the viewpoint of composition and order. Therefore, iPTREE-2 based on a regression tree algorithm exhibits the ability of finding important factors and developing rules for the purpose of data mining. On a dataset of 1859 different single point mutations from thermodynamic database, ProTherm, iPTREE-2 yields a correlation coefficient of 0.70 between predicted and experimental values. In the task of data mining, detailed analysis of sequences reveals the possibility of the compositional specificity of residues in different ranges of stability change and implies the existence of certain patterns. As building rules, we found that the mutation residues in wild type and in mutant protein play an important role. The present study demonstrates that iPTREE-2 can serve the purpose of predicting protein stability change, especially when one requires more understandable knowledge.en_US
dc.language.isoen_USen_US
dc.subjectbioinformaticsen_US
dc.subjectdata miningen_US
dc.subjectdecision treesen_US
dc.subjectpredictionen_US
dc.subjectprotein stabilityen_US
dc.titleSequence analysis and rule development of predicting protein stability change upon mutation using decision tree modelen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s00894-007-0197-4en_US
dc.identifier.journalJOURNAL OF MOLECULAR MODELINGen_US
dc.citation.volume13en_US
dc.citation.issue8en_US
dc.citation.spage879en_US
dc.citation.epage890en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000247428800002-
dc.citation.woscount17-
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