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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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Inferring the qualities of protein-RNA models with graph transformers.

Andrew Jordan Siciliano1, Yifan Bao1, Bishal Shrestha1

  • 1Department of Computer Science, University of Miami, Coral Gables, FL 33124, United States.

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Summary

We developed CARP, a novel graph transformer method for assessing protein-RNA complex models. CARP accurately predicts model quality, outperforming existing tools and aiding in selecting high-quality structures for biological insights.

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Biophysics

Background:

  • Predicting protein-RNA complex structures is crucial for understanding biological mechanisms and diseases.
  • Data scarcity and experimental challenges make accurate prediction difficult.
  • Quality assessment of these complex models is essential for advancing research.

Purpose of the Study:

  • To develop a novel computational method for assessing the quality of protein-RNA complex models.
  • To provide multiple quality perspectives, including overall fold and interface accuracy.
  • To facilitate the selection of high-quality models for further study.

Main Methods:

  • A graph transformer-based approach named CARP (complex quality assessment of RNA and protein) was developed.
  • CARP predicts overall fold, overall interface, and per-protein-RNA interface quality estimates simultaneously.
  • The method was evaluated on a protein-RNA docking benchmark and CASP16 targets.

Main Results:

  • CARP demonstrated superior performance compared to existing scoring tools in ordering and selecting high-quality decoys.
  • CARP consistently selected higher quality models on CASP16 targets, with top rankings for global interface and global protein-RNA interface quality.
  • CARP effectively identified high-quality models predicted by AlphaFold3.

Conclusions:

  • CARP offers a powerful new tool for quality assessment of protein-RNA complex models.
  • The method aids in identifying accurate structural models, advancing drug development and understanding of biological phenomena.
  • CARP is freely available, promoting further research in structural bioinformatics.