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Related Concept Videos

Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Improving accuracy of protein contact prediction using balanced network deconvolution.

Hai-Ping Sun1, Yan Huang, Xiao-Fan Wang

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.

Proteins
|December 20, 2014
PubMed
Summary

This study introduces a new balanced network deconvolution (BND) algorithm to improve protein contact prediction accuracy. BND effectively filters false positives from co-evolution methods, enhancing 3D structure determination.

Keywords:
filterpredictorprotein structure predictionresidue co-evolutionresidue contact maptransitive noise

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Network Science

Background:

  • Accurate residue contact maps are crucial for protein 3D structure determination.
  • Current co-evolution methods for contact prediction generate high false positives due to indirect and transitive contacts.

Purpose of the Study:

  • To develop a novel algorithm, balanced network deconvolution (BND), for refining protein residue contact predictions.
  • To improve the accuracy of contact prediction by distinguishing direct dependencies in biological networks.

Main Methods:

  • Developed the balanced network deconvolution (BND) algorithm, extending network deconvolution without eigenvalue limits.
  • Applied BND to filter contact predictions from five established co-evolution methods.
  • Validated the algorithm on benchmark datasets from CASP9, CASP10, and PSICOV.

Main Results:

  • BND significantly improved medium- and long-range contact predictions by 55.59% and 47.68% at the L/5 cutoff, respectively.
  • The improvements were statistically significant (P-value < 5.93 × 10(-3)) with no additional computational cost.
  • BND enhances the utility of co-evolution-based contact prediction for 3D structure modeling.

Conclusions:

  • Balanced network deconvolution (BND) is an effective method for refining protein contact predictions.
  • BND offers a general solution to reduce false positives in co-evolutionary contact prediction.
  • The BND algorithm is freely available for use in protein structure prediction research.