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From static to dynamic: the need for structural ensembles and a predictive model of RNA folding and function.

ABSTRACT: To understand RNA, it is necessary to move beyond a descriptive categorization towards quantitative predictions of its molecular conformations and functional behavior. An incisive approach to understanding the function and folding of biological RNA systems involves characterizing small, simple components that are largely responsible for the behavior of complex systems including helix-junction-helix elements and tertiary motifs. State-of-the-art methods have permitted unprecedented insight into the conformational ensembles of these elements revealing, for example, that conformations of helix-junction-helix elements are confined to a small region of the ensemble, that this region is highly dependent on the junction's topology, and that the correct alignment of tertiary motifs may be a rare conformation on the overall folding landscape. Further characterization of RNA components and continued development of experimental and computational methods with the goal of quantitatively predicting RNA folding and functional behavior will be critical to understanding biological RNA systems.

SUBMITTER: Herschlag D

PROVIDER: S-EPMC4416989 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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From static to dynamic: the need for structural ensembles and a predictive model of RNA folding and function.

Herschlag Daniel D Allred Benjamin E BE Gowrishankar Seshadri S

Current opinion in structural biology 20150201

To understand RNA, it is necessary to move beyond a descriptive categorization towards quantitative predictions of its molecular conformations and functional behavior. An incisive approach to understanding the function and folding of biological RNA systems involves characterizing small, simple components that are largely responsible for the behavior of complex systems including helix-junction-helix elements and tertiary motifs. State-of-the-art methods have permitted unprecedented insight into t ...[more]

PMID: 25744941

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Project description:This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.

Dataset Information

From static to dynamic: the need for structural ensembles and a predictive model of RNA folding and function.

Publications

From static to dynamic: the need for structural ensembles and a predictive model of RNA folding and function.

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