Amphiphilic peptides A(3)K, A(6)K, and A(9)K displayed an increasing propensity for nanoaggregation with increasing the size of hydrophobic alanine moiety, and the size and shape of the aggregates showed a steady transition from loose peptide stacks formed by A(3)K, long nanofibers by A(6)K, to short and narrow nanorods by A(9)K. This size and shape transition was broadly consistent with the trend predicted from interfacial packing and curvature change if these peptide surfactants were treated as conventional surfactants. The antibacterial capacity, defined by the killing of percentage of bacteria in a given time and peptide concentration, showed a strong correlation to peptide hydrophobicity, evident from both microscopic and fluorescence imaging studies. For A(9)K, the power for membrane permeation and bacterial clustering intensified with peptide concentration and incubation time. These results thus depict a positive correlation between the propensity for self-assembly of the peptides, their membrane penetration power, and bactericidal capacity. Although the exposure of A(9)K to a preformed DPPC membrane bilayer showed little structural disturbance, the same treatment to the preformed DPPG membrane bilayer led to substantial disruption of model membrane structure, a trend entirely consistent with the high selectivity observed from membrane hemolytic studies.

In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.

Poly(N-isopropylacrylamide) (PNIPAM)-based thermosensitive hydrogels demonstrate great potential in biomedical applications. However, they have inherent drawbacks such as low mechanical strength, limited drug loading capacity and low biodegradability. Formulating PNIPAM with other functional components to form composited hydrogels is an effective strategy to make up for these deficiencies, which can greatly benefit their practical applications. This review seeks to provide a comprehensive observation about the PNIPAM-based composite hydrogels for biomedical applications so as to guide related research. It covers the general principles from the materials choice to the hybridization strategies as well as the performance improvement by focusing on several application areas including drug delivery, tissue engineering and wound dressing. The most effective strategies include incorporation of functional inorganic nanoparticles or self-assembled structures to give composite hydrogels and linking PNIPAM with other polymer blocks of unique properties to produce copolymeric hydrogels, which can improve the properties of the hydrogels by enhancing the mechanical strength, giving higher biocompatibility and biodegradability, introducing multi-stimuli responsibility, enabling higher drug loading capacity as well as controlled release. These aspects will be of great help for promoting the development of PNIPAM-based composite materials for biomedical applications.

Motivation: It has long been hypothesized that incorporating models of network noise as well as edge directions and known pathway information into the representation of protein–protein interaction (PPI) networks might improve their utility for functional inference. However, a simple way to do this has not been obvious. We find that diffusion state distance (DSD), our recent diffusion-based metric for measuring dissimilarity in PPI networks, has natural extensions that incorporate confidence, directions and can even express coherent pathways by calculating DSD on an augmented graph.Results: We define three incremental versions of DSD which we term cDSD, caDSD and capDSD, where the capDSD matrix incorporates confidence, known directed edges, and pathways into the measure of how similar each pair of nodes is according to the structure of the PPI network. We test four popular function prediction methods (majority vote, weighted majority vote, multi-way cut and functional flow) using these different matrices on the Baker’s yeast PPI network in cross-validation. The best performing method is weighted majority vote using capDSD. We then test the performance of our augmented DSD methods on an integrated heterogeneous set of protein association edges from the STRING database. The superior performance of capDSD in this context confirms that treating the pathways as probabilistic units is more powerful than simply incorporating pathway edges independently into the network.Availability: All source code for calculating the confidences, for extracting pathway information from KEGG XML files, and for calculating the cDSD, caDSD and capDSD matrices are available from http://dsd.cs.tufts.edu/capdsdContact: lenore.cowen@tufts.edu or benjamin.hescott@tufts.eduSupplementary information: Supplementary data are available at Bioinformatics online.