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A Probabilistic Programming Approach to Protein Structure Superposition.

Lys Sanz Moreta1, Ahmad Salim Al-Sibahi2, Douglas Theobald3

  • 1Department of Computer Science. University of Copenhagen, Denmark.

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|October 18, 2021
PubMed
Summary
This summary is machine-generated.

We introduce THESEUS-PP, a novel Bayesian model for protein structure superposition using probabilistic programming. This approach enhances accuracy by considering atom correlations and variances, advancing biomolecular structure analysis.

Keywords:
Bayesian modellingdeep probabilistic programmingprotein structure predictionprotein superposition

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Optimal superposition of protein structures is essential for understanding molecular structure, function, dynamics, and evolution.
  • Existing methods often rely on minimizing squared distances, potentially overlooking complex positional correlations and variances.

Purpose of the Study:

  • To investigate the application of probabilistic programming for protein structure superposition using a Bayesian framework.
  • To develop and implement the THESEUS-PP model, an extension of the THESEUS model, for enhanced structural alignment.

Main Methods:

  • Implementation of the THESEUS-PP model in the probabilistic programming language Pyro.
  • Utilizing Bayesian inference for automated maximum a-posteriori (MAP) estimation of rotation, translation, variances, and latent mean structure.
  • Incorporating correlated atom positions and heteroscedasticity into the probabilistic model, unlike conventional methods.

Main Results:

  • Demonstrated the efficacy of probabilistic programming as a powerful paradigm for creating Bayesian probabilistic models in biomolecular structure analysis.
  • The THESEUS-PP model offers automated MAP estimation, providing a more robust approach to superposition compared to traditional methods.
  • Successful application of the model highlights its potential for advanced structural analyses.

Conclusions:

  • Probabilistic programming represents a significant advancement for modeling biomolecular structures.
  • The THESEUS-PP model is well-suited as an error model or likelihood component in Bayesian protein structure prediction, particularly with deep probabilistic programming.
  • This work opens new avenues for accurate and sophisticated analysis of protein and biomolecular structures.