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Persistent Tor-algebra for protein-protein interaction analysis.

Xiang Liu1,2, Huitao Feng1,3, Zhi Lü4

  • 1Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, China, 300071.

Briefings in Bioinformatics
|February 15, 2023
PubMed
Summary
This summary is machine-generated.

We introduce a novel Persistent Tor-Algebra (PTA) model for analyzing protein-protein interactions (PPIs). This algebraic approach offers a superior method for characterizing complex multiphysical information in PPIs and predicting binding affinity.

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Area of Science:

  • Computational Biology
  • Algebraic Topology
  • Biophysics

Background:

  • Protein-protein interactions (PPIs) are fundamental to biological processes.
  • Current models struggle to efficiently characterize complex multiphysical information in PPIs.
  • A unified algebraic approach is needed for PPI analysis.

Purpose of the Study:

  • To develop a novel Persistent Tor-Algebra (PTA) model for unified algebraic representation of multiphysical interactions in PPIs.
  • To apply PTA for predicting PPI binding affinity.
  • To demonstrate the efficiency and superiority of the PTA model.

Main Methods:

  • Developed a Persistent Tor-Algebra (PTA) model using face rings and Tor modules to represent protein structures and interactions.
  • Characterized multiphysical information within/between biomolecules using PTA and PTA barcodes.
  • Employed PTA-based ensemble learning for PPI binding affinity prediction on SKEMPI and AB-Bind datasets.

Main Results:

  • The PTA model effectively characterizes multiphysical information within and between biomolecules.
  • PTA-based ensemble learning achieved superior performance in PPI binding affinity prediction compared to existing models.
  • The model provides an efficient mathematical framework for molecular structure and interaction characterization.

Conclusions:

  • The Persistent Tor-Algebra (PTA) model offers a powerful and efficient algebraic framework for analyzing protein-protein interactions.
  • PTA significantly advances the characterization of complex multiphysical information in biological systems.
  • This approach demonstrates high potential for applications in drug discovery and molecular modeling.