Trimethylamine N-oxide-derived zwitterionic polymers: A new class of ultralow fouling bioinspired materials.

Materials that resist nonspecific protein adsorption are needed for many applications. However, few are able to achieve ultralow fouling in complex biological milieu. Zwitterionic polymers emerge as a class of highly effective ultralow fouling materials due to their superhydrophilicity, outperforming other hydrophilic materials such as poly(ethylene glycol). Unfortunately, there are only three major classes of zwitterionic materials based on poly(phosphorylcholine), poly(sulfobetaine), and poly(carboxybetaine) currently available. Inspired by trimethylamine N-oxide (TMAO), a zwitterionic osmolyte and the most effective protein stabilizer, we here report TMAO-derived zwitterionic polymers (PTMAO) as a new class of ultralow fouling biomaterials. The nonfouling properties of PTMAO were demonstrated under highly challenging conditions. The mechanism accounting for the extraordinary hydration of PTMAO was elucidated by molecular dynamics simulations. The discovery of PTMAO polymers demonstrates the power of molecular understanding in the design of new biomimetic materials and provides the biomaterials community with another class of nonfouling zwitterionic materials.

A browser-based tool for visualization and analysis of diffusion MRI data.

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable-it runs in any web-browser-it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.

Redefining the Protein-Protein Interface: Coarse Graining and Combinatorics for an Improved Understanding of Amino Acid Contributions to the Protein-Protein Binding Affinity.

The ability to intervene in biological pathways has for decades been limited by the lack of a quantitative description of protein-protein interactions (PPIs). Herein we generate and compare millions of simple PPI models for insight into the mechanisms of specific recognition and binding. We use a coarse-grained approach whereby amino acids are counted in the interface, and these counts are used as binding affinity predictors. We perform lasso regression, a modern regression technique aimed at interpretability, with every possible amino acid combination (over 106 unique feature sets) to select only those amino acid predictors that provide more information than noise. This approach circumvents arbitrary binning and assumptions about the binding environment that obscure other binding affinity models. Aggregated analysis of these models trained at various interfacial cutoff distances informs the roles of specific amino acids in different binding contexts. We find that a simple amino acid count model outperforms detailed intermolecular contact and binned residue type models. We identify the prevalence of serine, glycine, and tryptophan in the interface as particularly important for predicting binding affinity across a range of distance cutoffs. Although current sample size limitations prevent a robust consensus model for binding affinity prediction, our approach underscores the relevance of a residue-based description of the protein-protein interface to increase our understanding of specific interactions.