Toward a single-layer two-dimensional honeycomb supramolecular organic framework in water.

The self-assembly of well-defined 2D supramolecular polymers in solution has been a challenge in supramolecular chemistry. We have designed and synthesized a rigid stacking-forbidden 1,3,5-triphenylbenzene compound that bears three 4,4'-bipyridin-1-ium (BP) units on the peripheral benzene rings. Three hydrophilic bis(2-hydroxyethyl)carbamoyl groups are introduced to the central benzene ring to suppress 1D stacking of the triangular backbone and to ensure solubility in water. Mixing the triangular preorganized molecule with cucurbit[8]uril (CB[8]) in a 2:3 molar ratio in water leads to the formation of the first solution-phase single-layer 2D supramolecular organic framework, which is stabilized by the strong complexation of CB[8] with two BP units of adjacent molecules. The periodic honeycomb 2D framework has been characterized by various (1)H NMR spectroscopy, dynamic light scattering, X-ray diffraction and scattering, scanning probe and electron microscope techniques and by comparing with the self-assembled structures of the control systems.

Deeper investigation into the utility of functional class scoring in missing protein prediction from proteomics data.

Functional Class Scoring (FCS) is a network-based approach previously demonstrated to be powerful in missing protein prediction (MPP). We update its performance evaluation using data derived from new proteomics technology (SWATH) and also checked for reproducibility using two independent datasets profiling kidney tissue proteome. We also evaluated the objectivity of the FCS p-value, and followed up on the value of MPP from predicted complexes. Our results suggest that (1) FCS p -values are non-objective, and are confounded strongly by complex size, (2) best recovery performance do not necessarily lie at standard p -value cutoffs, (3) while predicted complexes may be used for augmenting MPP, they are inferior to real complexes, and are further confounded by issues relating to network coverage and quality and (4) moderate sized complexes of size 5 to 10 still exhibit considerable instability, we find that FCS works best with big complexes. While FCS is a powerful approach, blind reliance on its non-objective p -value is ill-advised.

Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - A case study on prostate cancer.

Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Quantitative Proteomics Signature Profiling) and PFSNet (Paired Fuzzy SubNetworks), in an intra-tissue proteome data set of prostate tissue samples. Despite high basal variation, we find that traditional statistical analysis may exaggerate the extent of heterogeneity. We also report that network-based analysis outperforms protein-based feature selection with concomitantly higher cross-validation accuracy. Overall, network-based analysis provides emergent signal that boosts sensitivity while retaining good precision. It is a potential means of circumventing heterogeneity for stable biomarker discovery.