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
Folding events. Visualizing of vital events listed the Table 1 during the folding procedure. Purple: α-helix, blue: 3 – 10-helix, cyan: turn, lime: coil. Corresponding to the Table 1, the alignment node IDs in: (a)-(1, 2), (b)-(3, 4), (c)-(5–15), (d)-(16).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Folding events. Visualizing of vital events listed the Table 1 during the folding procedure. Purple: α-helix, blue: 3 – 10-helix, cyan: turn, lime: coil. Corresponding to the Table 1, the alignment node IDs in: (a)-(1, 2), (b)-(3, 4), (c)-(5–15), (d)-(16).

Mentions: Figure 4 displays several groups of critical events identified by the EPO algorithm (corresponding to the aligned nodes as shown in Table 1). In particular, 4(a) includes two closely occurred events during the early stage of the folding procedure (one of the conformations is selected from the aligned node 1, and the other one is from aligned-node 2). At this time, the sequence has started to fold and one can observe the helical structure, but the ring is not yet formed. 4(b) presents two conformations representing aligned nodes 3 and 4, respectively. We observe that the ring has started to form at this point, but is still not stable. 4(c) shows several conformations (one each from aligned nodes 5 to 15) occurred in order in most successful runs. At this stage, the ring is adjusted and stabilized. The adjustment mainly happens around the turn area and for side chains. 4(d) shows the final successful structure.


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)

Folding events. Visualizing of vital events listed the Table 1 during the folding procedure. Purple: α-helix, blue: 3 – 10-helix, cyan: turn, lime: coil. Corresponding to the Table 1, the alignment node IDs in: (a)-(1, 2), (b)-(3, 4), (c)-(5–15), (d)-(16).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Folding events. Visualizing of vital events listed the Table 1 during the folding procedure. Purple: α-helix, blue: 3 – 10-helix, cyan: turn, lime: coil. Corresponding to the Table 1, the alignment node IDs in: (a)-(1, 2), (b)-(3, 4), (c)-(5–15), (d)-(16).
Mentions: Figure 4 displays several groups of critical events identified by the EPO algorithm (corresponding to the aligned nodes as shown in Table 1). In particular, 4(a) includes two closely occurred events during the early stage of the folding procedure (one of the conformations is selected from the aligned node 1, and the other one is from aligned-node 2). At this time, the sequence has started to fold and one can observe the helical structure, but the ring is not yet formed. 4(b) presents two conformations representing aligned nodes 3 and 4, respectively. We observe that the ring has started to form at this point, but is still not stable. 4(c) shows several conformations (one each from aligned nodes 5 to 15) occurred in order in most successful runs. At this stage, the ring is adjusted and stabilized. The adjustment mainly happens around the turn area and for side chains. 4(d) shows the final successful structure.

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