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

Area of Science:

• Computational Biology
• Biophysics
• Structural Biology

Background:

• Protein folding is crucial for understanding molecular life.
• Computational power generates vast amounts of protein folding simulation data.
• Interpreting this data to find novel folding features is a significant challenge.

Purpose of the Study:

• To develop an effective algorithm for extracting features from diverse protein folding trajectories.
• To address the challenges of comparing high-dimensional curves from folding simulations.

Main Methods:

• Modeling folding trajectories as multi-dimensional curves.
• Developing an enhanced partial order (EPO) algorithm for multiple curve comparison (MCC).
• Applying the EPO algorithm to analyze miniprotein Trp-cage 1 folding simulations.

Main Results:

• The EPO algorithm successfully extracts features from diverse folding trajectories, including unsuccessful simulations.
• It can detect subtle similarities at a low level.
• Biologically meaningful folding events were identified in the Trp-cage 1 case study.

Conclusions:

• The EPO algorithm is a general tool applicable to various scientific domains.
• It effectively aligns multiple protein structures even with low similarities.
• A web server is available for public use: http://db.cse.ohio-state.edu/EPO.