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標題: iStable: off-the-shelf predictor integration for predicting protein stability changes
作者: Chen, Chi-Wei
Lin, Jerome
Chu, Yen-Wei
Project: BMC Bioinformatics, Volume 14
Background: Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a
loss of protein function. Predicting the protein stability changes can provide several possible candidates for the
novel protein designing. Although many prediction tools are available, the conflicting prediction results from
different tools could cause confusion to users.
Results: We proposed an integrated predictor, iStable, with grid computing architecture constructed by using
sequence information and prediction results from different element predictors. In the learning model, several
machine learning methods were evaluated and adopted the support vector machine as an integrator, while not
just choosing the majority answer given by element predictors. Furthermore, the role of the sequence information
played was analyzed in our model, and an 11-window size was determined. On the other hand, iStable is available
with two different input types: structural and sequential. After training and cross-validation, iStable has better
performance than all of the element predictors on several datasets. Under different classifications and conditions
for validation, this study has also shown better overall performance in different types of secondary structures,
relative solvent accessibility circumstances, protein memberships in different superfamilies, and experimental
Conclusions: The trained and validated version of iStable provides an accurate approach for prediction of protein
stability changes.
DOI: 10.1186/1471-2105-14-S2-S5
Appears in Collections:基因體暨生物資訊學研究所

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