Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures.

ABSTRACT: Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distrib

SUBMITTER: Viswanath S 

PROVIDER: S-EPMC5768525 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature