A Probabilistic Programming Approach to Protein Structure Superposition.

ABSTRACT: Optimal superposition of protein structures or other biological molecules is crucial for understanding their structure, function, dynamics and evolution. Here, 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 probabilistic programming language Pyro. Unlike conventional methods that minimize the sum of the squared distances, THESEUS takes into account correlated atom positions and heteroscedasticity (ie. atom positions can feature different variances). THESEUS performs maximum likelihood estimation using itera

SUBMITTER: Moreta LS 

PROVIDER: S-EPMC8515897 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature