Background: The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.

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Background: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM.

MotivationCross-species analysis of large-scale protein–protein interaction (PPI) networks has played a significant role in understanding the principles deriving evolution of cellular organizations and functions. Recently, network alignment algorithms have been proposed to predict conserved interactions and functions of proteins. These approaches are based on the notion that orthologous proteins across species are sequentially similar and that topology of PPIs between orthologs is often conserved. However, high accuracy and scalability of network alignment are still a challenge.ResultsWe propose a novel pairwise global network alignment algorithm, called PrimAlign, which is modeled as a Markov chain and iteratively transited until convergence. The proposed algorithm also incorporates the principles of PageRank. This approach is evaluated on tasks with human, yeast and fruit fly PPI networks. The experimental results demonstrate that PrimAlign outperforms several prevalent methods with statistically significant differences in multiple evaluation measures. PrimAlign, which is multi-platform, achieves superior performance in runtime with its linear asymptotic time complexity. Further evaluation is done with synthetic networks and results suggest that popular topological measures do not reflect real precision of alignments.Availability and implementationThe source code is available at http://web.ecs.baylor.edu/faculty/cho/PrimAlign.Supplementary information Supplementary data are available at Bioinformatics online.

Recent high‐throughput experiments have generated protein–protein interaction data on a genomic scale, yielding the complete interactome for several organisms. Various graph clustering algorithms have been applied to protein interaction networks for identifying protein complexes and functional modules. Although the previous algorithms are scalable and robust, their accuracy is still limited because of the complex connectivity found in protein interaction networks. In this study, we propose a novel information‐theoretic definition, graph entropy, as a measure of the structural complexity of a graph. Loss of graph entropy represents an increase in modularity of the graph. Based on this concept, we present a graph clustering algorithm which searches for the local optimum in modularity. The algorithm detects each optimal cluster by growing a seed in a manner that minimizes graph entropy. In the experiments with the yeast interactome, the results show that the graph entropy approach has higher accuracy in predicting protein complexes and functional modules than the best competing method. We statistically compared output clusters to both known protein complexes and Gene Ontology annotations in the biological process and molecular function categories in order to measure f‐scores and p‐scores as clustering accuracy. Because this algorithm is also scalable, it can be applied to the larger scale human protein interaction network.

Layered double hydroxide (LDH) is an important layer-structured material for supercapacitors because of its versatile compositions, high theoretical capacitance, environmental friendliness, and low cost. However, the high resistivity of this material results in capacity fading, limiting its application in energy storage. Herein, we develop a facile approach to synthesize ultrathin petal-like NiAl layered double oxide/sulfide (LDO/LDS) composites with high electrochemical activity using hydrothermal reaction followed by sulfidation process. Scanning electron micrograph shows that the petal-like NiAl LDO/LDS composites are as thin as ~10 nm with a mean lateral dimension of ~1 µm. The NiAl LDO/LDS electrode delivers remarkably high specific capacitance of 2250.5 F g−1 at 1 A g−1 compared with that of NiAl LDH (1740.5 F g−1 at 1 A g−1) and possesses good cycling ability of 88.9% capacitance retention over 5000 cycles at 5 A g−1. Asymmetric supercapacitor (ASC) is fabricated using NiAl LDO/LDS and graphene as positive and negative electrodes, respectively. NiAl LDO/LDS//G ASC exhibits specific capacitance of 153.3 F g−1 at 1 A g−1, high energy density of 47.9 Wh kg−1 at a power density of 750 W kg−1, and reliable cycling stability of 95.68% capacitance retention after 5000 cycles. Results highlight that NiAl LDO/LDS composites are promising materials for energy storage devices with long cycling stability

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Layered double hydroxide (LDH) is a promising electrode material for supercapacitor owing to its versatility in compositions, high theoretical capacitance, environmental benignity, and low cost. However, capacity fading of LDH hinders its application in energy storage. Herein, we develop a facile approach to synthesize NiAl-LDH nanoplates possessing high electrochemical activity and desirable morphology to improve ion diffusion kinetics and reduce charge transfer resistance, leading to enhanced specific capacitance compared to pristine NiAl-LDH. Scanning electron microscopy shows that the LDH nanoplates are as thin as ∼30 nm with a mean lateral dimension of ∼150 nm. The NiAl-LDH nanoplates electrode delivers remarkably high specific capacitance of 1713.2 F g−1 at 1 A g−1 and good cycling ability of 88% capacitance retention over 5000 cycles compared to only 757.1 F g−1 at 1 A g−1 and 76.4% of the pristine NiAl-LDH. An asymmetric supercapacitor (ASC) is assembled using NiAl-LDH nanoplates and graphene as positive and negative electrodes, respectively. The ASC operating at 1.4 V delivers a high specific capacitance of 125 F g−1 at 1 A g−1 with a high energy density of 34.1 Wh kg−1 at a power density of 700 W kg−1 and outstanding cyclic stability (91.8% capacitance retention after 5000 cycles)