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FTIP: an accurate and efficient method for global protein surface comparison
Yuan Zhang1, Xing Sui1, Scott Stagg2
1Department of Statistics.
Bioinformatics (Oxford, England)
|February 6, 2020
Summary
A new method, Farthest Point Sampling (FPS)-enhanced Triangulation-based Iterative-Closest-Point (ICP) (FTIP), improves global protein surface comparison (GPSC). FTIP shows promise for protein identification in cryo-electron tomography (CET) experiments.
Area of Science:
- Structural biology
- Computational biology
- Biophysics
Background:
- Global protein surface comparison (GPSC) has lacked practical applications, limiting its research scope.
- Advances in cryo-electron tomography (CET) highlight the need for effective protein identification methods based on surface shape.
Purpose of the Study:
- To develop and evaluate a novel method for GPSC that utilizes only surface shape information.
- To assess the method's performance in protein classification and its potential application in CET.
Main Methods:
- Developed the Farthest Point Sampling (FPS)-enhanced Triangulation-based Iterative-Closest-Point (ICP) (FTIP) algorithm for GPSC.
- Extracted feature points using FPS and employed a triangulation-based ICP algorithm for alignment.
- Applied nearest-neighbor classification on a benchmark dataset of 2329 proteins.
Main Results:
- FTIP outperformed the state-of-the-art 3D Zernike descriptor method for GPSC.
- The method demonstrated successful protein classification using only surface shape.
- FTIP showed potential for protein identification in real and simulated CET data.
Conclusions:
- FTIP offers a robust and efficient approach to global protein surface comparison.
- The method has significant potential for advancing protein identification in cryo-electron tomography.
- The developed software is publicly available for further research and application.
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