Limits...
An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories.

Sun H, Ferhatosmanoglu H, Ota M, Wang Y - BMC Bioinformatics (2008)

Bottom Line: Current computation power enables researchers to produce a huge amount of folding simulation data.Hence there is a pressing need to be able to interpret and identify novel folding features from them.We demonstrate its generality and effectiveness by applying it to aligning multiple protein structures with low similarities.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. sun.82@osu.edu

ABSTRACT

Background: Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produce a huge amount of folding simulation data. Hence there is a pressing need to be able to interpret and identify novel folding features from them.

Results: In this paper, we model each folding trajectory as a multi-dimensional curve. We then develop an effective multiple curve comparison (MCC) algorithm, called the enhanced partial order (EPO) algorithm, to extract features from a set of diverse folding trajectories, including both successful and unsuccessful simulation runs. The EPO algorithm addresses several new challenges presented by comparing high dimensional curves coming from folding trajectories. A detailed case study on miniprotein Trp-cage 1 demonstrates that our algorithm can detect similarities at rather low level, and extract biologically meaningful folding events.

Conclusion: The EPO algorithm is general and applicable to a wide range of applications. We demonstrate its generality and effectiveness by applying it to aligning multiple protein structures with low similarities. For user's convenience, we provide a web server for the algorithm at http://db.cse.ohio-state.edu/EPO.

Show MeSH
An example for merging stage. Empty and solid points are aligned to the nodes oa and ob, respectively, while points in the dotted region should be grouped together.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2571979&req=5

Figure 8: An example for merging stage. Empty and solid points are aligned to the nodes oa and ob, respectively, while points in the dotted region should be grouped together.

Mentions: For example, see Figure 8, where the set of points P (enclosed in the dotted circle) should have been aligned to one node. However, suppose the nodes oa and ob already exist before any point in P is inserted.


An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories.

Sun H, Ferhatosmanoglu H, Ota M, Wang Y - BMC Bioinformatics (2008)

An example for merging stage. Empty and solid points are aligned to the nodes oa and ob, respectively, while points in the dotted region should be grouped together.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2571979&req=5

Figure 8: An example for merging stage. Empty and solid points are aligned to the nodes oa and ob, respectively, while points in the dotted region should be grouped together.
Mentions: For example, see Figure 8, where the set of points P (enclosed in the dotted circle) should have been aligned to one node. However, suppose the nodes oa and ob already exist before any point in P is inserted.

Bottom Line: Current computation power enables researchers to produce a huge amount of folding simulation data.Hence there is a pressing need to be able to interpret and identify novel folding features from them.We demonstrate its generality and effectiveness by applying it to aligning multiple protein structures with low similarities.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. sun.82@osu.edu

ABSTRACT

Background: Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produce a huge amount of folding simulation data. Hence there is a pressing need to be able to interpret and identify novel folding features from them.

Results: In this paper, we model each folding trajectory as a multi-dimensional curve. We then develop an effective multiple curve comparison (MCC) algorithm, called the enhanced partial order (EPO) algorithm, to extract features from a set of diverse folding trajectories, including both successful and unsuccessful simulation runs. The EPO algorithm addresses several new challenges presented by comparing high dimensional curves coming from folding trajectories. A detailed case study on miniprotein Trp-cage 1 demonstrates that our algorithm can detect similarities at rather low level, and extract biologically meaningful folding events.

Conclusion: The EPO algorithm is general and applicable to a wide range of applications. We demonstrate its generality and effectiveness by applying it to aligning multiple protein structures with low similarities. For user's convenience, we provide a web server for the algorithm at http://db.cse.ohio-state.edu/EPO.

Show MeSH