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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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NMR structure of trp-cage protein 1l2y. Labels on graph mark amino acids(AAs). AA2 to AA7 roughly form an alpha-helix. AA2 to AA19 form a ring-type structure. In particular, AA2 to AA5 and AA16 to AA19 form the "neck" of this ring.
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Figure 2: NMR structure of trp-cage protein 1l2y. Labels on graph mark amino acids(AAs). AA2 to AA7 roughly form an alpha-helix. AA2 to AA19 form a ring-type structure. In particular, AA2 to AA5 and AA16 to AA19 form the "neck" of this ring.

Mentions: Our input dataset includes 200 simulated folding trajectories for a particular protein called Trp-cage. The dataset is provided by the Ota's Lab [4]. The folding simulations were performed at 325 K by using the AMBER99 force field with a small modification and the generalized Born implicit solvent model. Trp-cage (see Figure 2) is a mini-protein consisting of 20 amino acids. It has been widely used for folding study because of its short, simple sequence and its quick folding kinetics. Following the definition from [32], a successful folding event has to satisfy the following two criteria:


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)

NMR structure of trp-cage protein 1l2y. Labels on graph mark amino acids(AAs). AA2 to AA7 roughly form an alpha-helix. AA2 to AA19 form a ring-type structure. In particular, AA2 to AA5 and AA16 to AA19 form the "neck" of this ring.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: NMR structure of trp-cage protein 1l2y. Labels on graph mark amino acids(AAs). AA2 to AA7 roughly form an alpha-helix. AA2 to AA19 form a ring-type structure. In particular, AA2 to AA5 and AA16 to AA19 form the "neck" of this ring.
Mentions: Our input dataset includes 200 simulated folding trajectories for a particular protein called Trp-cage. The dataset is provided by the Ota's Lab [4]. The folding simulations were performed at 325 K by using the AMBER99 force field with a small modification and the generalized Born implicit solvent model. Trp-cage (see Figure 2) is a mini-protein consisting of 20 amino acids. It has been widely used for folding study because of its short, simple sequence and its quick folding kinetics. Following the definition from [32], a successful folding event has to satisfy the following two criteria:

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