Computational drug repositioning with attention walking.

Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.

GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.

Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immune checkpoint inhibition by estimating their binding to major histocompatibility complex (MHC) proteins. Although deep neural networks have been primarily used for these prediction models, it is difficult to interpret the models reported thus far as accurately representing the interactions between biomolecules. In this study, we propose the GraphMHC model, which utilizes a graph neural network model applied to molecular structure to simulate the binding between MHC proteins and peptide sequences. Amino acid sequences sourced from the immune epitope database (IEDB) undergo conversion into molecular structures. Subsequently, atomic intrinsic informations and inter-atomic connections are extracted and structured as a graph representation. Stacked graph attention and convolution layers comprise the GraphMHC network which classifies bindings. The prediction results from the test set using the GraphMHC model showed a high performance with an area under the receiver operating characteristic curve of 92.2% (91.9-92.5%), surpassing a baseline model. Moreover, by applying the GraphMHC model to melanoma patient data from The Cancer Genome Atlas project, we found a borderline difference (0.061) in overall survival and a significant difference in stromal score between the high and low neoantigen load groups. This distinction was not present in the baseline model. This study presents the first feature-intrinsic method based on biochemical molecular structure for modeling the binding between MHC protein sequences and neoantigen candidate peptide sequences. This model can provide highly accurate responsibility information that can predict the prognosis of immune checkpoint inhibitors to cancer patients who want to apply it.

π-Conjugated Polymer with Pendant Side Chains as a Dopant-Free Hole Transport Material for High-Performance Perovskite Solar Cells.

Dopant-free polymeric hole transport materials (HTMs) have attracted considerable attention in perovskite solar cells (PSCs) due to their high carrier mobilities and excellent hydrophobicity. They are considered promising candidates for HTMs to replace commercial Spiro-OMeTAD to achieve long-term stability and high efficiency in PSCs. In this study, we developed BDT-TA-BTASi, a conjugated donor-π-acceptor polymeric HTM. The donor benzo[1,2-b:4,5-b']dithiophene (BDT) and acceptor benzotriazole (BTA) incorporated pendant siloxane, and alkyl side chains led to high hole mobility and solubility. In addition, BDT-TA-BTASi can effectively passivate the perovskite layer and markedly decrease the trap density. Based on these advantages, dopant-free BDT-TA-BTASi-based PSCs achieved an efficiency of over 21.5%. Furthermore, dopant-free BDT-TA-BTASi-based devices not only exhibited good stability in N2 (retaining 92% of the initial efficiency after 1000 h) but also showed good stability at high-temperature (60 °C) and -humidity conditions (80 ± 10%) (retaining 92 and 82% of the initial efficiency after 400 h). These results demonstrate that BDT-TA-BTASi is a promising HTM, and the study provides guidance on dopant-free polymeric HTMs to achieve high-performance PSCs.

Highly Efficient and Stable Mo-CoP3 @FeOOH Electrocatalysts for Alkaline Seawater Splitting.

The introduction of high-valence state elements and highly active species is promisingly desired to design superior electrocatalysts for water electrolysis. Exploring scalable synthetic strategies is necessary for an in-depth understanding of the mechanism of improving electrocatalytic performance. But it remains challenging. Herein, several electrocatalysts through element doping are prepared. The obtained Mo-CoP3 -2@FeOOH samples show the overpotentials (OER) of 232 mV (alkaline seawater) and 262 mV (KOH electrolyte). As HER catalyst, it also presents an excellent electrocatalytic performance. The above electrocatalysts are utilized as anode/cathode to assemble devices for alkaline seawater/water electrolysis, which delivers a cell voltage of 1.58 V and durability of 350 h. Density functional theory calculations reveal that Mo ion doping and FeOOH significantly enhance the density states of the Fermi level and tune the position of the d-band center. It expedites the charge transfer and decreases the adsorption energy of intermediates. It demonstrates that transition-metal phosphides coated with highly active FeOOH offer an effective route to fabricate high-performance and durable catalysts for seawater/water electrolysis.

Conductive N, S doped Copolymers as Stable Metal-Free Electrocatalysts for Water Splitting.

Noble metals (Pt) and metal oxides (IrC and RuO2) are heavily utilized as benchmark electrocatalysts for alkaline water splitting; however, these materials possess several drawbacks including high cost, poor selectivity and stability, and high environmental impact. To address these issues, we synthesized a novel metal-free conducting polypyrrole-polythiophene (Ppy-Ptp) copolymer and a separate Ppy electrode material for water-splitting applications. The Ppy-Ptp and Ppy electrocatalysts exhibited remarkable activity in the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively. The optimal Ppy-Ptp (1:3) formulation, when deposited on a conductive nickel foam (NF) substrate, exhibited an exceptional OER performance with a low overpotential of approximately 250 mV at 20 mAcm-2, thereby outperforming the benchmark IrC/NF electrocatalyst (290 mV, 20 mAcm-2). Additionally, a similarly prepared Ppy/NF electrocatalyst exhibited an extraordinary HER performance with an overpotential of approximately 72 mV at 10 mA cm-2. Furthermore, an alkaline anion-exchange membrane (AEM) electrolyzer incorporating Ppy-Ptp (1:3) and Ppy as the anode and cathode materials, respectively, displayed operating potentials of 1.55, 1.70, and 1.78 V at 10, 50, and 100 mA cm-2, which are lower than those observed in previously reported electrolyzers. This electrolyzer also exhibited considerable operational endurance over 50 h at 50 mA cm-2, over which a negligible decay of 0.02 V was observed. The novel polymer-based metal-free catalysts presented herein therefore exhibit considerable potential as alternative electrocatalytic materials for sustainable industrial-scale H2 synthesis.

Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning.

Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug-gene-disease tensor by integrating drug-disease, drug-gene, and disease-gene associations and predict drug-gene-disease triple associations through tensor decomposition. The proposed method, which ensembles generalized tensor decomposition (GTD) and multi-layer perceptron (MLP), models drug-gene-disease associations through GTD and learns the features of drugs, genes, and diseases through MLP, providing more flexibility and non-linearity than conventional tensor decomposition. We experimented with drug-gene-disease association prediction using two distinct networks created by chemical structures and ATC codes as drug features. Moreover, we leveraged drug, gene, and disease latent vectors obtained from the predicted triple associations to predict drug-disease, drug-gene, and disease-gene pairwise associations. Our experimental results revealed that the proposed ensemble method was superior for triple association prediction. The ensemble model achieved an AUC of 0.96 in predicting triple associations for new drugs, resulting in an approximately 7% improvement over the performance of existing models. It also showed competitive accuracy for pairwise association prediction compared with previous methods. This study demonstrated that incorporating genetic information leads to notable advancements in drug repositioning.

Application of poly (vinyl alcohol)-cryogels to immobilizing nitrifiers: Enhanced tolerance to shear stress-induced destruction and viability control.

The hardness of poly (vinyl alcohol)-cryogels (PVA-CGs) was improved under three parameter conditions: 7.5 %-12.5 % PVA, 1-5 freezing-thawing cycles (FTCs), and the addition of 0 %-10 % glycerol as a cryoprotectant. This study investigated the effects of shear stress-induced destruction (SSID) on mechanical strength by inducing rapid erosion with a high frictional force. Tolerance to SSID (Tol-SSID) exhibited different sensitivities and trends depending on the above three fabrication parameters. The measured Tol-SSID exhibited consistent and inconsistent trends with tensile strength and swelling, respectively. Tol-SSID evaluation provides new insights into the practically meaningful mechanical strength of PVA-CGs against strong friction, which simulates extreme shear stress in a bioreactor. A PVA-CG with a PVA concentration of 10 % and in two FTCs resulted in Tol-SSID and tensile strength of 88.3 % and 0.59 kPa, respectively. Here, 5 % glycerol was added to maintain the bacterial respiration activity of immobilized nitrifiers of 0.097 mg-O2/g-VSS·min and survival of 88.6 %. The continuous mode of nitrification using the optimized PVA-CG for 10 days resulted in an ammonia removal rate of 0.2173 kg-N/m3·d, which is an improvement over cases without glycerol addition: 0.1426 and 0.1472 kg-N/m3·d for PVA-CGs in two and three FTCs, respectively.

Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug-disease associations. This review reveals existing network-based approaches for predicting drug-disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug-drug similarities based on chemical structures and ATC codes, ontology-based disease-disease similarities, and drug-disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug-drug similarity measurement are crucial for improving disease-side prediction.

High performance aqueous zinc battery enabled by potassium ion stabilization.

Aqueous zinc ion batteries (AZIBs) are highly competitive in the energy storage systems due to their feature with operation safety and environmental friendliness. However, the sluggish diffusion kinetics of Zn2+ and inferior cathode circulation hinder their widespread application. Herein, we assemble a highly durable zinc ion battery by intercalating K+ into V2O5 nanolayers. The K+ pre-intercalation can buffer the lattice expansion of the electrode materials and reduce the internal stress. In addition, the stable K+ acts as a "pillar" to protect the layered structure of V2O5 materials from collapse during operation cycling. It delivers a reversible capacity of 479.8 mAh g-1 at 0.2 A g-1 and achieves excellent cyclic stability with a retention rate of 91.3% (10 A g-1) after 3000 cycles. The cell still maintains excellent specific capacity at high work temperature.

Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks.

Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.

Enhanced Electrochemical Performance of Zn/VOx Batteries by a Carbon-Encapsulation Strategy.

Aqueous zinc ion batteries show tremendous potential in emerging energy storage devices. However, it is challenging to explore the desired cathode materials that match well with the Zn anode. In this work, we report two kinds of carbon-encapsulated VOx microspheres grown by controlling the calcination temperature. The assembled Zn/VO2@C-0.5 batteries deliver a high specific capacity and reversible rate performance. They can still maintain 260 mA h g-1 at 5 A g-1 after 1000 cycles. In addition, the cells possess an energy density of 280 W h kg-1 at a power density of 140 W kg-1. The soft pack devices also show favorable mechanical stability and durable cycle ability. The excellent zinc ion storage capacity can be attributed to the large tunnel structure of VO2 materials and the high conductivity of amorphous carbon.

Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.

Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs have been proposed over the past decade. Our interest is which methods or techniques are the most advantageous for increasing prediction accuracy. This article provides a comprehensive overview of network-based, machine learning, and integrated DTI prediction methods. The network-based methods handle a DTI network along with drug and target similarities in a matrix form and apply graph-theoretic algorithms to identify new DTIs. Machine learning methods use known DTIs and the features of drugs and target proteins as training data to build a predictive model. Integrated methods combine these two techniques. We assessed the prediction performance of the selected state-of-the-art methods using two different benchmark datasets. Our experimental results demonstrate that the integrated methods outperform the others in general. Some previous methods showed low accuracy on predicting interactions of unknown drugs which do not exist in the training dataset. Combining similarity matrices from multiple features by data fusion was not beneficial in increasing prediction accuracy. Finally, we analyzed future directions for further improvements in DTI predictions.

Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins.

Functional modules can be predicted using genome-wide protein-protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

Integrative analysis of breast cancer profiles in TCGA by TNBC subgrouping reveals novel microRNA-specific clusters, including miR-17-92a, distinguishing basal-like 1 and basal-like 2 TNBC subtypes.

BACKGROUND: The term triple-negative breast cancer (TNBC) is used to describe breast cancers without expression of estrogen receptor, progesterone receptor or HER2 amplification. To advance targeted treatment options for TNBC, it is critical that the subtypes within this classification be described in regard to their characteristic biology and gene expression. The Cancer Genome Atlas (TCGA) dataset provides not only clinical and mRNA expression data but also expression data for microRNAs. RESULTS: In this study, we applied the Lehmann classifier to TCGA-derived TNBC cases which also contained microRNA expression data and derived subtype-specific microRNA expression patterns. Subsequent analyses integrated known and predicted microRNA-mRNA regulatory nodes as well as patient survival data to identify key networks. Notably, basal-like 1 (BL1) TNBCs were distinguished from basal-like 2 TNBCs through up-regulation of members of the miR-17-92 cluster of microRNAs and suppression of several known miR-17-92 targets including inositol polyphosphate 4-phosphatase type II, INPP4B. CONCLUSIONS: These data demonstrate TNBC subtype-specific microRNA and target mRNA expression which may be applied to future biomarker and therapeutic development studies.

Correction: One-step synthesis and electrochemical performance of a PbMoO4/CdMoO4 composite as an electrode material for high-performance supercapacitor applications.

Correction for 'One-step synthesis and electrochemical performance of a PbMoO4/CdMoO4 composite as an electrode material for high-performance supercapacitor applications' by Tarugu Anitha et al., Dalton Trans., 2019, 48, 10652-10660.

LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules.

BACKGROUND: Cross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging. RESULTS: We present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments. CONCLUSION: The proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

Pattern formation of metal-oxide hybrid nanostructures via the self-assembly of di-block copolymer blends.

The templated self-assembly of block copolymers (BCPs) with a high Flory-Huggins interaction parameter (χ) can effectively create ultrafine, well-ordered nanostructures in the range of 5-30 nm. However, the self-assembled BCP patterns remain limited to possible morphological geometries and materials. Here, we introduce a novel and useful self-assembly method of di-BCP blends capable of generating diverse hybrid nanostructures consisting of oxide and metal materials through the rapid microphase separation of A-B/B-C BCP blends. We successfully obtained various hybridized BCP morphologies which cannot be acquired from a single di-BCP, such as hexagonally arranged hybrid dot and dot-in-hole patterns by controlling the mixing ratios of the solvents with a binary solvent annealing process. Furthermore, we demonstrate how the binary solvent vapor annealing process can provide a wide range of pattern geometries to di-BCP blends, showing a well-defined spontaneous one-to-one accommodation in dot-in-hole nanostructures. Specifically, we show clearly how the self-assembled BCPs can be functionalized via selective reduction and/or an oxidation process, resulting in the excellent positioning of confined silica nanodots into each nanospace of a Pt mesh. These results suggest a new method to achieve the pattern formation of more diverse and complex hybrid nanostructures using various blended BCPs.

One-step synthesis and electrochemical performance of a PbMoO4/CdMoO4 composite as an electrode material for high-performance supercapacitor applications.

Homogeneously ultrathin nanocubes of PbMoO4/CdMoO4 nanocomposites, which are useful for energy storage applications, were prepared on nickel foam using a one-step chemical bath deposition method. The capacitive performance of the synthesized PbMoO4/CdMoO4 electrode material was examined by cyclic voltammetry, galvanostatic charge/discharge, and electrochemical impedance spectroscopy in a three-electrode configuration. This unique structure can provide more electroactive sites and a larger surface area, which can enhance the electrochemical performance. The PbMoO4/CdMoO4 redox-active material achieved a high specific capacitance of 1840.32 F g-1 at a current density of 1 A g-1 in a 3 M KOH solution. This electrode exhibited excellent long cycle life stability with ∼81.4% specific capacitance retention after 5000 cycles at a current density of 4 A g-1, which is superior to that of individual PbMoO4 and CdMoO4 nanosheets. The prepared PbMoO4/CdMoO4 composite electrode displayed excellent electrocapacitive properties, which can be attributed to the synergetic effects of PbMoO4 and CdMoO4. These results suggest that the PbMoO4/CdMoO4 nanocube arrays have the potential to meet the requirements of practical electrochemical energy storage applications.