Bayesian protein superposition using Hamiltonian Monte Carlo
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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 language | English |
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Title of host publication | Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 |
Publisher | IEEE |
Publication date | Oct 2020 |
Pages | 1-11 |
Article number | 9288019 |
ISBN (Electronic) | 9781728195742 |
DOIs | |
Publication status | Published - Oct 2020 |
Event | 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States Duration: 26 Oct 2020 → 28 Oct 2020 |
Conference
Conference | 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 |
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Land | United States |
By | Virtual, Cincinnati |
Periode | 26/10/2020 → 28/10/2020 |
- Bayesian modelling, Hamiltonian Monte Carlo, NUTS, probabilistic programming, protein structure superposition, protein superposition
Research areas
ID: 255836764