A Probabilistic Programming Approach to Protein Structure Superposition

Lys Sanz Moreta*, Ahmad Salim Al-Sibahi, Douglas Theobald, William Bullock, Basile Nicolas Rommes, Andreas Manoukian, Thomas Hamelryck

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

1 Citation (Scopus)

Abstract

Optimal superposition of protein structures is crucial for understanding their structure, function, dynamics and evolution. We investigate the use of probabilistic programming to superimpose protein structures guided by a Bayesian model. Our model THESEUS-PP is based on the THESEUS model, a probabilistic model of protein superposition based on rotation, translation and perturbation of an underlying, latent mean structure. The model was implemented in the deep probabilistic programming language Pyro. Unlike conventional methods that minimize the sum of the squared distances, THESEUS takes into account correlated atom positions and heteroscedasticity (i.e., atom positions can feature different variances). THESEUS performs maximum likelihood estimation using iterative expectation-maximization. In contrast, THESEUS-PP allows automated maximum a-posteriori (MAP)estimation using suitable priors over rotation, translation, variances and latent mean structure. The results indicate that probabilistic programming is a powerful new paradigm for the formulation of Bayesian probabilistic models concerning biomolecular structure. Specifically, we envision the use of the THESEUS-PP model as a suitable error model or likelihood in Bayesian protein structure prediction using deep probabilistic programming.

Original languageEnglish
Title of host publication2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019
EditorsGiacomo Baruzzo, Sebastian Daberdaku, Barbara Di Camillo, Simone Furini, Emanuele Domenico Giordano, Giuseppe Nicosia
Number of pages5
PublisherIEEE
Publication date2019
Article number8791469
ISBN (Electronic)9781728114620
DOIs
Publication statusPublished - 2019
Event16th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019 - Certosa di Pontignano, Siena, Italy
Duration: 9 Jul 201911 Jul 2019

Conference

Conference16th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019
Country/TerritoryItaly
CityCertosa di Pontignano, Siena
Period09/07/201911/07/2019
SponsorGlaxoSmithKline (GSK), IEEE, IEEE Computational Intelligence Society

Keywords

  • Bayesian modelling
  • deep probabilistic programming
  • protein structure prediction
  • protein superposition

Cite this