scholarly journals Ensembling graph attention networks for human microbe–drug association prediction

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i779-i786
Author(s):  
Yahui Long ◽  
Min Wu ◽  
Yong Liu ◽  
Chee Keong Kwoh ◽  
Jiawei Luo ◽  
...  
Keyword(s):  
Drug Targets ◽  
New Drugs ◽  
Supplementary Information ◽  
Biomedical Data ◽  
Convolutional Network ◽  
Great Opportunity ◽  
Attention Networks ◽  
Source Codes ◽  
Multiple Networks ◽  
Multiple Input

Abstract Motivation Human microbes get closely involved in an extensive variety of complex human diseases and become new drug targets. In silico methods for identifying potential microbe–drug associations provide an effective complement to conventional experimental methods, which can not only benefit screening candidate compounds for drug development but also facilitate novel knowledge discovery for understanding microbe–drug interaction mechanisms. On the other hand, the recent increased availability of accumulated biomedical data for microbes and drugs provides a great opportunity for a machine learning approach to predict microbe–drug associations. We are thus highly motivated to integrate these data sources to improve prediction accuracy. In addition, it is extremely challenging to predict interactions for new drugs or new microbes, which have no existing microbe–drug associations. Results In this work, we leverage various sources of biomedical information and construct multiple networks (graphs) for microbes and drugs. Then, we develop a novel ensemble framework of graph attention networks with a hierarchical attention mechanism for microbe–drug association prediction from the constructed multiple microbe–drug graphs, denoted as EGATMDA. In particular, for each input graph, we design a graph convolutional network with node-level attention to learn embeddings for nodes (i.e. microbes and drugs). To effectively aggregate node embeddings from multiple input graphs, we implement graph-level attention to learn the importance of different input graphs. Experimental results under different cross-validation settings (e.g. the setting for predicting associations for new drugs) showed that our proposed method outperformed seven state-of-the-art methods. Case studies on predicted microbe–drug associations further demonstrated the effectiveness of our proposed EGATMDA method. Availability Source codes and supplementary materials are available at: https://github.com/longyahui/EGATMDA/ Supplementary information Supplementary data are available at Bioinformatics online.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

2020 ◽  
Vol 36 (16) ◽  
pp. 4490-4497
Author(s):  
Siqi Liang ◽  
Haiyuan Yu
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Drug Targets ◽  
Class Imbalance ◽  
Target Prediction ◽  
Drug Repurposing ◽  
New Drugs ◽  
Supplementary Information ◽  
Training Scheme ◽  
Drug Target Prediction

Abstract Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary information Supplementary data are available at Bioinformatics online.

Adaptive One-Class gaussian processes allow accurate prioritization of oncology drug targets

2020 ◽  
Author(s):  
Antonio de Falco ◽  
Zoltan Dezso ◽  
Francesco Ceccarelli ◽  
Luigi Cerulo ◽  
Angelo Ciaramella ◽  
...  
Keyword(s):  
Gaussian Processes ◽  
Drug Targets ◽  
Process Model ◽  
New Drugs ◽  
Supplementary Information ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Hyperparameter Selection ◽  
The Cost ◽  
Selection Of

Abstract Motivation The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples. Results Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates. Availability Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP. Supplementary information Supplementary data are available at Bioinformatics online.

PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method

2021 ◽  
Author(s):  
Seungyoon Nam ◽  
Sungyoung Lee ◽  
Sungjin Park ◽  
Jinhyuk Lee ◽  
Aron Park ◽  
...  
Keyword(s):  
Web Service ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Disease Status ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Multiple Targets ◽  
Source Codes ◽  
Approved Drugs ◽  
The Web

Abstract Motivation Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. Results Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. Availability and implementation The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. Supplementary information Supplementary data are available at Bioinformatics online.

Mycobacterial DNA Replication as a Target for Antituberculosis Drug Discovery

2017 ◽  
Vol 17 (19) ◽  
pp. 2129-2142 ◽  
Author(s):  
Renata Płocinska ◽  
Malgorzata Korycka-Machala ◽  
Przemyslaw Plocinski ◽  
Jaroslaw Dziadek
Keyword(s):  
Drug Resistance ◽  
Dna Replication ◽  
Drug Targets ◽  
Rapid Development ◽  
New Drugs ◽  
Drug Resistant ◽  
Antituberculosis Drug ◽  
Nucleotide Synthesis ◽  
Antituberculosis Therapy ◽  
Optimal Drug

Background: Mycobacterium tuberculosis (M. tuberculosis), the causative agent of tuberculosis, is a leading infectious disease organism, causing millions of deaths each year. This serious pathogen has been greatly spread worldwide and recent years have observed an increase in the number of multi-drug resistant and totally drug resistant M. tuberculosis strains (WHO report, 2014). The danger of tuberculosis becoming an incurable disease has emphasized the need for the discovery of a new generation of antimicrobial agents. The development of novel alternative medical strategies, new drugs and the search for optimal drug targets are top priority areas of tuberculosis research. Factors: Key characteristics of mycobacteria include: slow growth, the ability to transform into a metabolically silent - latent state, intrinsic drug resistance and the relatively rapid development of acquired drug resistance. These factors make finding an ideal antituberculosis drug enormously challenging, even if it is designed to treat drug sensitive tuberculosis strains. A vast majority of canonical antibiotics including antituberculosis agents target bacterial cell wall biosynthesis or DNA/RNA processing. Novel therapeutic approaches are being tested to target mycobacterial cell division, twocomponent regulatory factors, lipid synthesis and the transition between the latent and actively growing states. Discussion and Conclusion: This review discusses the choice of cellular targets for an antituberculosis therapy, describes putative drug targets evaluated in the recent literature and summarizes potential candidates under clinical and pre-clinical development. We focus on the key cellular process of DNA replication, as a prominent target for future antituberculosis therapy. We describe two main pathways: the biosynthesis of nucleic acids precursors – the nucleotides, and the synthesis of DNA molecules. We summarize data regarding replication associated proteins that are critical for nucleotide synthesis, initiation, unwinding and elongation of the DNA during the replication process. They are pivotal processes required for successful multiplication of the bacterial cells and hence they are extensively investigated for the development of antituberculosis drugs. Finally, we summarize the most potent inhibitors of DNA synthesis and provide an up to date report on their status in the clinical trials.

scholarly journals A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

2020 ◽  
Vol 8 ◽  
Author(s):  
Ushashi Banerjee ◽  
Santhosh Sankar ◽  
Amit Singh ◽  
Nagasuma Chandra
Keyword(s):  
Drug Targets ◽  
Metabolic Flux ◽  
Latent Tuberculosis ◽  
Interaction Network ◽  
New Drugs ◽  
Prolonged Treatment ◽  
Flux Analysis ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Global Population

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

scholarly journals Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

2020 ◽  
Author(s):  
Lei Deng ◽  
Yideng Cai ◽  
Wenhao Zhang ◽  
Wenyi Yang ◽  
Bo Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Cancer Cells ◽  
Drug Targets ◽  
Deep Neural Network ◽  
Drug Sensitivity ◽  
Predictive Ability ◽  
Biological Knowledge ◽  
Data Sets ◽  
Source Codes ◽  
Sensitivity Data

AbstractMotivationTo efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in-silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of mechanism of drug action or limited performance in modeling drug sensitivity.ResultsIn this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To make advantage of both the excellent predictive ability of deep neural network and the biological knowledge of pathways, we reshape the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets, and demonstrate that pathDNN significantly outperformed canonical DNN model and seven other classical regression models. Most importantly, we observed remarkable activity decreases of disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments show that pathDNN achieves pharmacological interpretability and predictive ability in modeling drug sensitivity to cancer cells.AvailabilityThe web server, as well as the processed data sets and source codes for reproducing our work, is available at http://pathdnn.denglab.org

scholarly journals Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2534
Author(s):  
Cagatay Catal ◽  
Hakan Gunduz ◽  
Alper Ozcan
Keyword(s):  
Intelligent Transportation Systems ◽  
Malware Detection ◽  
Application Programming Interface ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Convolutional Network ◽  
Attention Networks ◽  
Security Issues ◽  
Call Graphs ◽  
Network Metrics

Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that damage the infrastructure of the entire ITS. Attackers can inject malware code that triggers dangerous actions such as information theft and unwanted system moves. The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks. To detect malware attacks addressing ITS, a Graph Attention Network (GAN)-based framework is proposed in this study. The inputs to this framework are the Application Programming Interface (API)-call graphs obtained from malware and benign Android apk files. During the graph creation, network metrics and the Node2Vec model are utilized to generate the node features. A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN). Experimental results demonstrated that the integration of the GAN and Node2Vec models provides the best performance in terms of F-measure and accuracy parameters and, also, the use of an attention mechanism in GAN improves the performance. Furthermore, node features generated with Node2Vec resulted in a 3% increase in classification accuracy compared to the features generated with network metrics.

scholarly journals Global Proteomic Analysis of Listeria monocytogenes’ Response to Linalool

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2449
Author(s):  
Zhipeng Gao ◽  
Weiming Zhong ◽  
Ting Liu ◽  
Tianyu Zhao ◽  
Jiajing Guo
Keyword(s):  
Listeria Monocytogenes ◽  
Foodborne Pathogens ◽  
Proteomic Analysis ◽  
Drug Targets ◽  
Enrichment Analysis ◽  
Good Evidence ◽  
New Drugs ◽  
Organic System ◽  
Flow Cytometry Data ◽  
Theoretical Support

Listeria monocytogenes (LM) is one of the most serious foodborne pathogens. Listeriosis, the disease caused by LM infection, has drawn attention worldwide because of its high hospitalization and mortality rates. Linalool is a vital constituent found in many essential oils; our previous studies have proved that linalool exhibits strong anti-Listeria activity. In this study, iTRAQ-based quantitative proteomics analysis was performed to explore the response of LM exposed to linalool, and to unravel the mode of action and drug targets of linalool against LM. A total of 445 differentially expressed proteins (DEPs) were screened out, including 211 up-regulated and 234 down-regulated proteins which participated in different biological functions and pathways. Thirty-one significantly enriched gene ontology (GO) functional categories were obtained, including 12 categories in “Biological Process”, 10 categories in “Cell Component”, and 9 categories in “Molecular Function”. Sixty significantly enriched biological pathways were classified, including 6 pathways in “Cell Process”, 6 pathways in “Environmental Information Processing”, 3 pathways in “Human Disease”, 40 pathways in “Metabolism”, and 2 pathways in “Organic System”. GO and Kyoto Encyclopedia of Genes (KEGG) enrichment analysis together with flow cytometry data implied that cell membranes, cell walls, nucleoids, and ribosomes might be the targets of linalool against LM. Our study provides good evidence for the proteomic analysis of bacteria, especially LM, exposed to antibacterial agents. Further, those drug targets discovered by proteomic analysis can provide theoretical support for the development of new drugs against LM.

Antiherpesvirus drugs: a promising spectrum of new drugs and drug targets

2003 ◽  
Vol 2 (4) ◽  
pp. 278-288 ◽  
Author(s):  
Donald M. Coen ◽  
Priscilla A. Schaffer
Keyword(s):  
Drug Targets ◽  
New Drugs
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