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BMCBI
2007

Predicting and improving the protein sequence alignment quality by support vector regression

9 years 11 months ago
Predicting and improving the protein sequence alignment quality by support vector regression
Background: For successful protein structure prediction by comparative modeling, in addition to identifying a good template protein with known structure, obtaining an accurate sequence alignment between a query protein and a template protein is critical. It has been known that the alignment accuracy can vary significantly depending on our choice of various alignment parameters such as gap opening penalty and gap extension penalty. Because the accuracy of sequence alignment is typically measured by comparing it with its corresponding structure alignment, there is no good way of evaluating alignment accuracy without knowing the structure of a query protein, which is obviously not available at the time of structure prediction. Moreover, there is no universal alignment parameter option that would always yield the optimal alignment. Results: In this work, we develop a method to predict the quality of the alignment between a query and a template. We train the support vector regression (SVR)...
Minho Lee, Chan-seok Jeong, Dongsup Kim
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Minho Lee, Chan-seok Jeong, Dongsup Kim
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