Bayesian protein superposition using Hamiltonian Monte Carlo

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

Optimally superimposing protein structures is essential to study their structure, function, dynamics and evolution. We present THESEUS NUTS (No U-Turn Sampler), a Bayesian version of the THESEUS model [1] -[3] which relies on maximum likelihood estimation. The probabilistic model interprets each protein as a rotated and translated noisy observation of a latent mean structure. Unlike conventional methods [4], THESEUS takes into account the differences in correlations between the atoms in the structure. This paper extends the previous THESEUS MAP (Maximum A Posteriori) model, [5] to full Bayesian inference by making use of the iterative NUTS [6], a Hamiltonian Monte Carlo method. The model delivers consistent results and is computationally efficient thanks to its implementation in the probabilistic programming language NumpPyro [7], [8] which in turn relies upon JAX [9], a system for high-performance machine learning.

Original languageEnglish
Title of host publicationProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
PublisherIEEE
Publication dateOct 2020
Pages1-11
Article number9288019
ISBN (Electronic)9781728195742
DOIs
Publication statusPublished - Oct 2020
Event20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States
Duration: 26 Oct 202028 Oct 2020

Conference

Conference20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
LandUnited States
ByVirtual, Cincinnati
Periode26/10/202028/10/2020

    Research areas

  • Bayesian modelling, Hamiltonian Monte Carlo, NUTS, probabilistic programming, protein structure superposition, protein superposition

ID: 255836764