Exploring the quality of protein structural models from a Bayesian perspective.

We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical C chemical shifts.

Assessing the One-Bond C-H Spin-Spin Coupling Constants in Proteins: Pros and Cons of Different Approaches.

In the present work, we explore three different approaches for the computation of the one-bond spin-spin coupling constants (SSCC) in proteins: density functional theory (DFT) calculations, a Karplus-like equation, and Gaussian process regression. The main motivation of this work is to select the best method for fast and accurate computation of the SSCC, for its use in everyday applications in protein structure validation, refinement, and/or determination. Our initial results showed a poor agreement between the DFT-computed and observed SSCC values.

Azahar: a PyMOL plugin for construction, visualization and analysis of glycan molecules.

Glycans are key molecules in many physiological and pathological processes. As with other molecules, like proteins, visualization of the 3D structures of glycans adds valuable information for understanding their biological function. Hence, here we introduce Azahar, a computing environment for the creation, visualization and analysis of glycan molecules.