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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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A local average distance descriptor for flexible protein structure comparison.

Hsin-Wei Wang, Chia-Han Chu, Wen-Ching Wang

  • 1Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. twp@mail.ntou.edu.tw.

BMC Bioinformatics
|April 4, 2014
PubMed
Summary
This summary is machine-generated.

We developed a new method using local average distance (LAD) to compare flexible protein structures, outperforming existing methods in identifying similar protein conformations. This approach effectively handles large conformational changes, improving protein structure classification and database searching.

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Biochemistry

Background:

  • Protein structures are dynamic and undergo conformational changes crucial for biological functions.
  • Protein structure comparison is vital for classifying and predicting protein functions.
  • Existing methods struggle to compare proteins with significant conformational flexibility.

Purpose of the Study:

  • To introduce a novel descriptor for flexible protein structure comparison.
  • To evaluate the effectiveness of the proposed method against existing techniques.
  • To improve the retrieval of similar protein structures, especially those with large conformational changes.

Main Methods:

  • Developed a local average distance (LAD) descriptor using geodesic distances (GDs) or Euclidean distances (EDs).
  • Performed pairwise flexible protein structure comparison.
  • Compared LAD with 7 structural alignment methods and 7 shape descriptors on hinge-bending motion datasets.

Main Results:

  • The LAD descriptor outperformed all compared methods in retrieving similar protein structures.
  • Performance was evaluated using precision-recall curves, retrieval success rate, R-precision, mean average precision, and F1-measure.
  • Both ED- and GD-based LAD descriptors effectively handled large bending motions and self-connection issues.

Conclusions:

  • LAD descriptors offer an effective alternative for searching protein structure databases.
  • The ED-based LAD descriptor demonstrates greater robustness compared to the GD-based descriptor.
  • The algorithm facilitates the discovery of novel conformational relationships and aids in protein structure classification.