Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations.

Many proteins have low thermodynamic stability, which can lead to low expression yields and limit functionality in research, industrial and clinical settings. This article introduces two, web-based tools that use the high-resolution structure of a protein along with the Rosetta molecular modeling program to predict stabilizing mutations. The protocols were recently applied to three genetically and structurally distinct proteins and successfully predicted mutations that improved thermal stability and/or protein yield.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning.

Comparative Analysis of Sulfonium-π, Ammonium-π, and Sulfur-π Interactions and Relevance to SAM-Dependent Methyltransferases.

We report the measurement and analysis of sulfonium-π, thioether-π, and ammonium-π interactions in a β-hairpin peptide model system, coupled with computational investigation and PDB analysis. These studies indicated that the sulfonium-π interaction is the strongest and that polarizability contributes to the stronger interaction with sulfonium relative to ammonium. Computational studies demonstrate that differences in solvation of the trimethylsulfonium versus the trimethylammonium group also contribute to the stronger sulfonium-π interaction.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met.