This study presents a comprehensive landscape of gut microbiota in PBC. Dysbiosis was found in the gut microbiome in PBC and partially relieved by UDCA. Our study suggests that gut microbiota is a potential therapeutic target and diagnostic biomarker for PBC.

The hairpin II of U1 snRNA can bind U1A protein with high affinity and specificity. NMR spectra suggest that the loop region of apo-RNA is largely unstructured and undergoes a transition from unstructured to well-folded upon U1Abinding. However, the mechanism that RNA folding coupled protein binding is poorly understood. To get an insight into the mechanism, we have performed explicit-solvent molecular dynamics (MD) to study the folding kinetics of bound RNA and apo-RNA. Roomtemperature MD simulations suggest that the conformation of bound RNA has significant adjustment and becomes more stable upon U1A binding. Kinetic analysis of high-temperature MD simulations shows that bound RNA and apo-RNA unfold via a twostate process, respectively. Both kinetics and free energy landscape analyses indicate that bound RNA folds in the order of RNA contracting, U1A binding, and tertiary folding. The predicted F-values suggest that A8, C10, A11, and G16 are key bases for bound RNA folding. Mutant Arg52Gln analysis shows that electrostatic interaction and hydrogen bonds between RNA and U1A (Arg52Gln) decrease. These results are in qualitative agreement with experiments. Furthermore, this method could be used in other studies about biomolecule folding upon receptor binding.

Brinker is the key target protein of the Drosophila Decapentaplegic morphogen signalling pathway. Brinker is widely expressed and can bind with DNA. NMR spectra suggest that apo-Brinker is intrinsically unstructured and undergoes a folding transition upon DNA-binding. However, the coupled mechanism of binding and folding is poorly understood. Here, we performed molecular dynamics (MD) simulations for both bound and apo-Brinker to study the mechanism. Room-temperature MD simulations suggest that Brinker becomes more rigid and stable upon DNA-binding. Kinetic analysis of high-temperature MD simulations shows that both bound and apo-Brinker unfold via a two-state process. The time scale of tertiary unfolding is significantly different between bound and apo-Brinker. The predicted Φ-values suggest that there are more residues with native-like transition state ensembles (TSEs) for bound Brinker than for apo-Brinker. The average RMSD differences between bound and apo-Brinker and Kolmogorov-Smirnov (KS) test analysis illustrate that Brinker folding upon DNA-binding might obey induced-fit mechanism based on MD simulations. These methods can be used for the research of other biomolecular folding upon ligand-binding.

BackgroundProteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.ResultsWe systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies.ConclusionsPhysical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.

Amyloid fibrils are considered to play causal roles in the pathogenesis of amyloid-related degenerative diseases such as Alzheimer's disease, type II diabetes mellitus, the transmissible spongiform encephalopathies, and prion disease. The mechanism of fibril formation is still hotly debated and remains an important open question. In this study, we utilized molecular dynamics (MD) simulation to analyze the stability of hexamer for eight class peptides. The MD results suggest that VEALYL and MVGGVV-1 are the most stable ones, then SNQNNY, followed by LYQLEN, MVGGVV-2, VQIVYK, SSTSAA, and GGVVIA. The statistics result indicates that hydrophobic residues play a key role in stabilizing the zipper interface. Single point and two linkage mutants of MVGGVV-1 confirmed that both Met1 and Val2 are key hydrophobic residues. This is consistent with the statistics analysis. The stability results of oligomer for MVGGVV-1 suggest that the intermediate state should be trimer (3-0) and tetramer (2-2). These methods can be used in stabilization study of other amyloid fibril.