Bayesian protein superposition using Hamiltonian Monte Carlo
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 |
Forlag | IEEE |
Publikationsdato | okt. 2020 |
Sider | 1-11 |
Artikelnummer | 9288019 |
ISBN (Elektronisk) | 9781728195742 |
DOI | |
Status | Udgivet - okt. 2020 |
Begivenhed | 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, USA Varighed: 26 okt. 2020 → 28 okt. 2020 |
Konference
Konference | 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 |
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Land | USA |
By | Virtual, Cincinnati |
Periode | 26/10/2020 → 28/10/2020 |
ID: 255836764