Nanoassembly of spider silk protein mediated by intrinsically disordered regions.

Spider silk is the self-assembling product of silk proteins each containing multiple repeating units. Each repeating unit is entirely intrinsically disordered or contains a small disordered domain. The role of the disordered domain/unit in conferring silk protein storage and self-assembly is not fully understood yet. Here, we used biophysical and biochemical techniques to investigate the self-assembly of a miniature version of a minor ampullate spidroin (denoted as miniMiSp). miniMiSp consists of two identical intrinsically disordered domains, one folded repetitive domain, and two folded terminal domains. Our data indicated that miniMiSp self-assembles into oligomers and further into liquid droplets. The oligomerization is attributed to the aggregation-prone property of both the disordered domains and the folded repetitive domain. Our results support the model of micellar structure for silk proteins at high protein concentrations. The disordered domain is indispensable for liquid droplet formation via liquid-liquid phase separation, and tyrosine residues located in the disordered domain make dominant contributions to stability of the liquid droplets. As the same tyrosine residues are also critical to fibrillation, the liquid droplets are likely an intermediate state between the solution state and the fiber state. Additionally, the terminal domains contribute to the pH- and salt-dependent self-assembly properties.

Effects of ligand binding on dynamics of fatty acid binding protein and interactions with membranes.

Intracellular transport of fatty acids involves binding of ligands to their carrier fatty acid binding proteins (FABPs) and interactions of ligand-free and -bound FABPs with membranes. Previous studies focused on ligand-free FABPs. Here, our amide hydrogen exchange data showed that oleic acid binding to human intestinal FABP (hIFABP) stabilizes the protein, most likely through enhancing the hydrogen-bonding network, and induces rearrangement of sidechains even far away from the ligand binding site. Using NMR relaxation techniques, we found that the ligand binding affects not only conformational exchanges between major and minor states but also the affinity of hIFABP to nanodiscs. Analyses of the relaxation and amide exchange data suggested that two minor native-like states existing in both ligand-free and -bound hIFABPs originate from global "breathing" motions, while one minor native-like state comes from local motions. The amide hydrogen exchange data also indicated that helix αII undergoes local unfolding through which ligands can exit from the binding cavity.

Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study.

BACKGROUND: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. OBJECTIVE: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. METHODS: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. CONCLUSIONS: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.