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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.

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Comparison of EPO, EPO-NoMerge and POA. Distribution of aligned nodes produced by the EPO algorithm, EPO-NoMerge (i.e, first stage of the EPO algorithm), and the traditional POA algorithm. The histogram is the number of aligned nodes (y-axis) versus the size of aligned nodes (x-axis).
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Figure 3: Comparison of EPO, EPO-NoMerge and POA. Distribution of aligned nodes produced by the EPO algorithm, EPO-NoMerge (i.e, first stage of the EPO algorithm), and the traditional POA algorithm. The histogram is the number of aligned nodes (y-axis) versus the size of aligned nodes (x-axis).

Mentions: In the first set of experiments, we convert each conformation to a high dimensional point (i.e, a 20 × 20 = 400 dimensional point), based on the distance matrix between all of the alpha-carbon atoms. Figure 3 compares the quality of the alignments of the SuccData by performing the POA algorithm, our EPO algorithm without the merging procedure (EPO-NoMerge), and the EPO algorithm. It shows the number of aligned nodes (y-axis) versus the size of aligned nodes (x-axis). Note that EPO-NoMerge is essentially POA with a clustering preprocessing and the new two-level scoring function.


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)

Comparison of EPO, EPO-NoMerge and POA. Distribution of aligned nodes produced by the EPO algorithm, EPO-NoMerge (i.e, first stage of the EPO algorithm), and the traditional POA algorithm. The histogram is the number of aligned nodes (y-axis) versus the size of aligned nodes (x-axis).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Comparison of EPO, EPO-NoMerge and POA. Distribution of aligned nodes produced by the EPO algorithm, EPO-NoMerge (i.e, first stage of the EPO algorithm), and the traditional POA algorithm. The histogram is the number of aligned nodes (y-axis) versus the size of aligned nodes (x-axis).
Mentions: In the first set of experiments, we convert each conformation to a high dimensional point (i.e, a 20 × 20 = 400 dimensional point), based on the distance matrix between all of the alpha-carbon atoms. Figure 3 compares the quality of the alignments of the SuccData by performing the POA algorithm, our EPO algorithm without the merging procedure (EPO-NoMerge), and the EPO algorithm. It shows the number of aligned nodes (y-axis) versus the size of aligned nodes (x-axis). Note that EPO-NoMerge is essentially POA with a clustering preprocessing and the new two-level scoring function.

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