DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

BMC Med Genomics. 2016;9 Suppl 2:48

Authors: Liang Z, Huang JX, Zeng X, Zhang G

Abstract
BACKGROUND: Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions.
METHODS: A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model.
RESULTS: There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80 %. In the comparison of reliability, the deep learning model shows the best stability among the three models.
CONCLUSIONS: Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.

PMID: 27510822 [PubMed - in process]

Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).

Pharm Res. 2015 Dec;32(12):3785-802

Authors: Varma MV, Steyn SJ, Allerton C, El-Kattan AF

Abstract
Early prediction of clearance mechanisms allows for the rapid progression of drug discovery and development programs, and facilitates risk assessment of the pharmacokinetic variability associated with drug interactions and pharmacogenomics. Here we propose a scientific framework--Extended Clearance Classification System (ECCS)--which can be used to predict the predominant clearance mechanism (rate-determining process) based on physicochemical properties and passive membrane permeability. Compounds are classified as: Class 1A--metabolism as primary systemic clearance mechanism (high permeability acids/zwitterions with molecular weight (MW) ≤400 Da), Class 1B--transporter-mediated hepatic uptake as primary systemic clearance mechanism (high permeability acids/zwitterions with MW >400 Da), Class 2--metabolism as primary clearance mechanism (high permeability bases/neutrals), Class 3A--renal clearance (low permeability acids/zwitterions with MW ≤400 Da), Class 3B--transporter mediated hepatic uptake or renal clearance (low permeability acids/zwitterions with MW >400 Da), and Class 4--renal clearance (low permeability bases/neutrals). The performance of the ECCS framework was validated using 307 compounds with single clearance mechanism contributing to ≥70% of systemic clearance. The apparent permeability across clonal cell line of Madin - Darby canine kidney cells, selected for low endogenous efflux transporter expression, with a cut-off of 5 × 10(-6) cm/s was used for permeability classification, and the ionization (at pH7) was assigned based on calculated pKa. The proposed scheme correctly predicted the rate-determining clearance mechanism to be either metabolism, hepatic uptake or renal for ~92% of total compounds. We discuss the general characteristics of each ECCS class, as well as compare and contrast the framework with the biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS). Collectively, the ECCS framework is valuable in early prediction of clearance mechanism and can aid in choosing the right preclinical tool kit and strategy for optimizing drug exposure and evaluating clinical risk of pharmacokinetic variability caused by drug interactions and pharmacogenomics.

PMID: 26155985 [PubMed - indexed for MEDLINE]

Systems biology insights into the meaning of the platelet's dual-receptor thrombin signaling.

J Thromb Haemost. 2016 Aug 11;

Authors: Sveshnikova AN, Balatskiy AV, Demianova AS, Shepelyuk TO, Shakhidzhanov SS, Balatskaya MN, Pichugin AV, Ataullakhanov FI, Panteleev MA

Abstract
BACKGROUND: Activation of human platelets with thrombin proceeds via two protease-activated receptors (PARs), PAR1 and PAR4, that have identical main intracellular signaling responses. Although there is evidence that they have different cleavage/inactivation kinetics (and some secondary variations in signaling), the reason for such redundancy is not clear.
METHODS: We developed a multicompartmental stochastic computational systems biology model of dual-receptor thrombin signaling in platelets to gain insight into the mechanisms and roles of PAR1 and PAR4 functioning. Experiments employing continuous flow cytometry of washed human platelets were used to validate the model and test its predictions. Activity of PAR receptors in donors was evaluated by mRNA measurement and by polymorphism sequencing.
RESULTS: While PAR1 activation produced rapid and short-lived response, signaling via PAR4 developed slowly and propagated in time. Response of the dual-receptor system was both rapid and prolonged in time. Inclusion of PAR1/PAR4 heterodimer formation promoted PAR4 signaling in the medium range of thrombin concentration (about 10 nM), with little contribution at high and low thrombin. Different dynamics and dose-dependence of procoagulant platelet formation in healthy donors was associated with individual variations in PAR1 and PAR4 activities and particularly by the Ala120Thr polymorphism in the F2RL3 gene encoding PAR4.
CONCLUSIONS: The dual-receptor combination is critical to produce a response combining three critical advantages: sensitivity to thrombin concentration, rapid onset and steady propagation; specific features of the protease-activated receptors do not allow combination of all three in a single receptor. This article is protected by copyright. All rights reserved.

PMID: 27513817 [PubMed - as supplied by publisher]

Systems metabolic engineering of Escherichia coli for the heterologous production of high value molecules-a veteran at new shores.

Curr Opin Biotechnol. 2016 Aug 8;42:178-188

Authors: Becker J, Wittmann C

Abstract
For more than fifty years, Escherichia coli has represented a remarkable success story in industrial biotechnology. Traditionally known as a producer of l-amino acids, E. coli has also entered the precious market of high-value molecules and is becoming a flexible, efficient production platform for various therapeutics, pre-biotics, nutraceuticals and pigments. This tremendous progress is enabled by systems metabolic engineering concepts that integrate systems biology and synthetic biology into the design and engineering of powerful E. coli cell factories.

PMID: 27513555 [PubMed - as supplied by publisher]

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.

BMC Med Genomics. 2016;9 Suppl 2:50

Authors: Kim D, Kang M, Biswas A, Liu C, Gao J

Abstract
BACKGROUND: Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator.
RESULTS: We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above.
CONCLUSIONS: In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

PMID: 27510319 [PubMed - in process]

Integrative analysis of human omics data using biomolecular networks.

Mol Biosyst. 2016 Aug 11;

Authors: Robinson JL, Nielsen J

Abstract
High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

PMID: 27510223 [PubMed - as supplied by publisher]

An integrated global chemomics and system biology approach to analyze the mechanisms of the traditional Chinese medicinal preparation Eriobotrya japonica - Fritillaria usuriensis dropping pills for pulmonary diseases.

BMC Complement Altern Med. 2016;16:4

Authors: Tao J, Hou Y, Ma X, Liu D, Tong Y, Zhou H, Gao J, Bai G

Abstract
BACKGROUND: Traditional Chinese medicine (TCM) herbal formulae provide valuable therapeutic strategies. However, the active ingredients and mechanisms of action remain unclear for most of these formulae. Therefore, the identification of complex mechanisms is a major challenge in TCM research.
METHODS: This study used a network pharmacology approach to clarify the anti-inflammatory and cough suppressing mechanisms of the Chinese medicinal preparation Eriobotrya japonica - Fritillaria usuriensis dropping pills (ChuanbeiPipa dropping pills, CBPP). The chemical constituents of CBPP were identified by high-quality ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS), and anti-inflammatory ingredients were selected and analyzed using the PharmMapper and Kyoto Encyclopedia of Genes and Genomes (KEGG) bioinformatics websites to predict the target proteins and related pathways, respectively. Then, an RNA-sequencing (RNA-Seq) analysis was carried out to investigate the different expression of genes in the lung tissue of rats with chronic bronchitis.
RESULTS: Six main constituents affected 19 predicted pathways, including ursolic acid and oleanolic acid from Eriobotrya japonica (Thunb.) Lindl. (Eri), peiminine from Fritillaria usuriensis Maxim. (Fri), platycodigenin and polygalacic acid from Platycodon grandiflorum (Jacq.) A. DC. (Pla) and guanosine from Pinellia ternata (Thunb.) Makino. (Pin). Expression of 34 genes was significantly decreased after CBPP treatment, affecting four therapeutic functions: immunoregulation, anti-inflammation, collagen formation and muscle contraction.
CONCLUSION: The active components acted on the mitogen activated protein kinase (MAPK) pathway, transforming growth factor (TGF)-beta pathway, focal adhesion, tight junctions and the action cytoskeleton to exert anti-inflammatory effects, resolve phlegm, and relieve cough. This novel approach of global chemomics-integrated systems biology represents an effective and accurate strategy for the study of TCM with multiple components and multiple target mechanisms.

PMID: 26742634 [PubMed - indexed for MEDLINE]

Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis.

J Chem Inf Model. 2016 Aug 10;

Authors: Liu L, Tsompana M, Wang Y, Wu D, Zhu L, Zhu R

Abstract
Drug discovery and development is a costly and time-consuming process with a high risk for failure resulting primarily from a drug's associated clinical safety and efficacy potential. Identifying and eliminating inapt candidate drugs as early as possible is an effective way for reducing unnecessary costs, but limited analytical tools are currently available for this purpose. Recent growth in the area of toxicogenomics and pharmacogenomics has provided with a vast amount of drug expression microarray data. Web servers such as CMap and LTMap have used this information to evaluate drug toxicity and mechanisms of action independently, however their wider applicability has been limited by the lack of a combinatorial drug-safety type of analysis. Using available genome-wide drug transcriptional expression profiles, we developed the first web server for combinatorial evaluation of toxicity and efficacy of candidate drugs named "Connection Map for Compounds" (CMC). Using CMC, researchers can initially compare their query drug gene signatures with prebuilt gene profiles generated from two large-scale toxicogenomics databases, and subsequently perform a drug efficacy analysis for identification of known mechanisms of drug action or generation of new predictions. CMC provides a novel approach for drug repositioning and early evaluation in drug discovery with its unique combination of toxicity and efficacy analyses, expansibility of data and algorithms, and customization of reference gene profiles. CMC can be freely accessed at http://cadd.tongji.edu.cn/webserver/CMCbp.jsp.

PMID: 27508329 [PubMed - as supplied by publisher]

Clinical Interpretation of Genomic Variations.

Turk J Haematol. 2016 Aug 8;

Authors: Sayitoğlu M

Abstract
Novel high-throughput sequencing technologies generate large-scale genomic data and use extensively for disease mapping of monogenic and/or complex disorders, personalized treatment and pharmacogenomics. It is rapidly becoming a routine tool for diagnosis and molecular monitorization of the patients to evaluate the therapeutic efficiency. The next generation sequencing platforms generate huge amounts of genetic variation data and it remains a challenge to interpret the variations that identified. NGS data interpretation needs a close collaboration of bioinformaticians, clinicians and geneticist. There are several problems needs to draw attention such as generation of new algorithms for mapping and annotation, harmonization of the terminology, correct us of nomenclature, reference genome for different populations, rare disease variant databases and clinical reports.

PMID: 27507302 [PubMed - as supplied by publisher]

An Official American Thoracic Society Workshop Report 2015. Stem Cells and Cell Therapies in Lung Biology and Diseases.

Ann Am Thorac Soc. 2016 Aug;13(8):S259-S278

Authors: Wagner DE, Cardoso WV, Gilpin SE, Majka S, Ott H, Randell SH, Thébaud B, Waddell T, Weiss DJ, ATS Subcommittee on Stem Cells and Cell Therapies

Abstract
The University of Vermont College of Medicine, in collaboration with the NHLBI, Alpha-1 Foundation, American Thoracic Society, Cystic Fibrosis Foundation, European Respiratory Society, International Society for Cellular Therapy, and the Pulmonary Fibrosis Foundation, convened a workshop, "Stem Cells and Cell Therapies in Lung Biology and Lung Diseases," held July 27 to 30, 2015, at the University of Vermont. The conference objectives were to review the current understanding of the role of stem and progenitor cells in lung repair after injury and to review the current status of cell therapy and ex vivo bioengineering approaches for lung diseases. These are all rapidly expanding areas of study that both provide further insight into and challenge traditional views of mechanisms of lung repair after injury and pathogenesis of several lung diseases. The goals of the conference were to summarize the current state of the field, discuss and debate current controversies, and identify future research directions and opportunities for both basic and translational research in cell-based therapies for lung diseases. This 10th anniversary conference was a follow up to five previous biennial conferences held at the University of Vermont in 2005, 2007, 2009, 2011, and 2013. Each of those conferences, also sponsored by the National Institutes of Health, American Thoracic Society, and respiratory disease foundations, has been important in helping guide research and funding priorities. The major conference recommendations are summarized at the end of the report and highlight both the significant progress and major challenges in these rapidly progressing fields.

PMID: 27509163 [PubMed - as supplied by publisher]

Antibiotic therapy for stable non-CF bronchiectasis in adults - A systematic review.

Chron Respir Dis. 2016 Aug 9;

Authors: Fjaellegaard K, Sin MD, Browatzki A, Ulrik CS

Abstract
To provide an update on efficacy and safety of antibiotic treatments for stable non-cystic fibrosis (CF) bronchiectasis (BE). Systematic review based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines was done. Twenty-six studies (1.898 patients) fulfilled the inclusion criteria. Studies of inhaled tobramycin have revealed conflicting results regarding quality of life (QoL), exacerbations and admissions, but may result in sputum cultures negative for Pseudomonas aeruginosa, whereas studies investigating the effect of inhaled gentamycin have shown positive effects on sputum bacterial density, decrease in sputum cultures positive for P. aeruginosa, QoL and exacerbation rate, but no improvement in forced expiratory volume in first second (FEV1). Oral azithromycin can reduce exacerbations, together with minor improvements in QoL and FEV1 Furthermore, oral erythromycin reduces exacerbations, but has no effect on lung function, symptoms or QoL. Inhaled ciprofloxacin may reduce P. aeruginosa in sputum cultures, but without changes in lung function, exacerbations or QoL. Although with limited evidence, inhaled colistin may have effects on P. aeruginosa density, exacerbations and QoL, whereas studies on aztreonam revealed no significant clinical improvements in the outcomes of interest, including exacerbation rate. Adverse events, including bronchospasm, have been reported in association with tobramycin and aztreonam. Several antibiotic treatment regimens have been shown to improve QoL and exacerbation rate, whereas findings regarding sputum production, lung function and admissions have been conflicting. Evidence-based treatment algorithms for antibiotic treatment of stable non-CF BE will have to await large-scale, long-term controlled studies.

PMID: 27507832 [PubMed - as supplied by publisher]

50 Years Ago in TheJournal ofPediatrics: The Sweat Test in Cystic Fibrosis: A Comparison of Overnight Sweat Collection versus the Pilocarpine Iontophoresis Method.

J Pediatr. 2016 Aug;175:73

Authors: Toltzis P

PMID: 27507315 [PubMed - in process]

Is genotyping of single isolates sufficient for population structure analysis of Pseudomonas aeruginosa in cystic fibrosis airways?

BMC Genomics. 2016;17:589

Authors: Sommer LM, Marvig RL, Luján A, Koza A, Pressler T, Molin S, Johansen HK

Abstract
BACKGROUND: The primary cause of morbidity and mortality in cystic fibrosis (CF) patients is lung infection by Pseudomonas aeruginosa. Therefore much work has been done to understand the adaptation and evolution of P. aeruginosa in the CF lung. However, many of these studies have focused on longitudinally collected single isolates, and only few have included cross-sectional analyses of entire P. aeruginosa populations in sputum samples. To date only few studies have used the approach of metagenomic analysis for the purpose of investigating P. aeruginosa populations in CF airways.
RESULTS: We analysed five metagenomes together with longitudinally collected single isolates from four recently chronically infected CF patients. With this approach we were able to link the clone type and the majority of SNP profiles of the single isolates to that of the metagenome(s) for each individual patient.
CONCLUSION: Based on our analysis we find that when having access to comprehensive collections of longitudinal single isolates it is possible to rediscover the genotypes of the single isolates in the metagenomic samples. This suggests that information gained from genome sequencing of comprehensive collections of single isolates is satisfactory for many investigations of adaptation and evolution of P. aeruginosa to the CF airways.

PMID: 27506816 [PubMed - in process]

Differentiation of pulmonary bacterial pathogens in cystic fibrosis by volatile metabolites emitted by their in vitro cultures: Pseudomonas aeruginosa, Staphylococcus aureus, Stenotrophomonas maltophilia and the Burkholderia cepacia complex.

J Breath Res. 2016;10(3):037102

Authors: Dryahina K, Sovová K, Nemec A, Španěl P

Abstract
As a contribution to the continuing search for breath biomarkers of lung and airways infection in patients with cystic fibrosis, CF, we have analysed the volatile metabolites released in vitro by Pseudomonas aeruginosa and other bacteria involved in respiratory infections in these patients, i.e. those belonging to the Burkholderia cepacia complex, Staphylococcus aureus or Stenotrophomonas maltophilia. These opportunistic pathogens are generally harmless to healthy people but they may cause serious infections in patients with severe underlying disease or impaired immunity such as CF patients. Volatile organic compounds emitted from the cultures of strains belonging to the above-mentioned four taxa were analysed by selected ion flow tube mass spectrometry. In order to minimize the effect of differences in media composition all strains were cultured in three different liquid media. Multivariate statistical analysis reveals that the four taxa can be well discriminated by the differences in the headspace VOC concentration profiles. The compounds that should be targeted in breath as potential biomarkers of airway infection were identified for each of these taxa of CF pathogens.

PMID: 27506232 [PubMed - in process]

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data.

Front Mol Biosci. 2016;3:35

Authors: Bujak R, Daghir-Wojtkowiak E, Kaliszan R, Markuszewski MJ

Abstract
Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study). The orthogonal projections to latent structures-discriminant analysis (OPLS-DA) without and with multiple testing correction as well as the least absolute shrinkage and selection operator (LASSO) with bootstrapping, were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction selected 46 and 218 variables based on the VIP criteria using Pareto and UV scaling, respectively. For the PH study, 217 and 320 variables were selected based on the VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built after correcting for multiple testing, selected 4 and 19 variables as in terms of Pareto and UV scaling, respectively. For the PH study, 14 and 18 variables were selected based on the VIP criteria in terms of Pareto and UV scaling, respectively. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3 and 100%, respectively. In the light of PLS-based models, the larger the search space the higher the probability of developing models that fit the training data well with simultaneous poor predictive performance on the validation set. The LASSO offers potential improvements over standard linear regression due to the presence of the constrain, which promotes sparse solutions. This paper is the first one to date utilizing the LASSO penalized logistic regression in untargeted metabolomics studies.

PMID: 27508208 [PubMed]

Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods.

Artif Intell Med. 2016 Jul;71:62-9

Authors: Dimitrakopoulos C, Theofilatos K, Pegkas A, Likothanassis S, Mavroudi S

Abstract
OBJECTIVE: Proteins are vital biological molecules driving many fundamental cellular processes. They rarely act alone, but form interacting groups called protein complexes. The study of protein complexes is a key goal in systems biology. Recently, large protein-protein interaction (PPI) datasets have been published and a plethora of computational methods that provide new ideas for the prediction of protein complexes have been implemented. However, most of the methods suffer from two major limitations: First, they do not account for proteins participating in multiple functions and second, they are unable to handle weighted PPI graphs. Moreover, the problem remains open as existing algorithms and tools are insufficient in terms of predictive metrics.
METHOD: In the present paper, we propose gradually expanding neighborhoods with adjustment (GENA), a new algorithm that gradually expands neighborhoods in a graph starting from highly informative "seed" nodes. GENA considers proteins as multifunctional molecules allowing them to participate in more than one protein complex. In addition, GENA accepts weighted PPI graphs by using a weighted evaluation function for each cluster.
RESULTS: In experiments with datasets from Saccharomyces cerevisiae and human, GENA outperformed Markov clustering, restricted neighborhood search and clustering with overlapping neighborhood expansion, three state-of-the-art methods for computationally predicting protein complexes. Seven PPI networks and seven evaluation datasets were used in total. GENA outperformed existing methods in 16 out of 18 experiments achieving an average improvement of 5.5% when the maximum matching ratio metric was used. Our method was able to discover functionally homogeneous protein clusters and uncover important network modules in a Parkinson expression dataset. When used on the human networks, around 47% of the detected clusters were enriched in gene ontology (GO) terms with depth higher than five in the GO hierarchy.
CONCLUSIONS: In the present manuscript, we introduce a new method for the computational prediction of protein complexes by making the realistic assumption that proteins participate in multiple protein complexes and cellular functions. Our method can detect accurate and functionally homogeneous clusters.

PMID: 27506132 [PubMed - in process]

Targeting glycolysis in the malaria parasite Plasmodium falciparum.

FEBS J. 2016 Feb;283(4):634-46

Authors: van Niekerk DD, Penkler GP, du Toit F, Snoep JL

Abstract
UNLABELLED: Glycolysis is the main pathway for ATP production in the malaria parasite Plasmodium falciparum and essential for its survival. Following a sensitivity analysis of a detailed kinetic model for glycolysis in the parasite, the glucose transport reaction was identified as the step whose activity needed to be inhibited to the least extent to result in a 50% reduction in glycolytic flux. In a subsequent inhibitor titration with cytochalasin B, we confirmed the model analysis experimentally and measured a flux control coefficient of 0.3 for the glucose transporter. In addition to the glucose transporter, the glucokinase and phosphofructokinase had high flux control coefficients, while for the ATPase a small negative flux control coefficient was predicted. In a broader comparative analysis of glycolytic models, we identified a weakness in the P. falciparum pathway design with respect to stability towards perturbations in the ATP demand.
DATABASE: The mathematical model described here has been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.bio.vu.nl/database/vanniekerk1. The SEEK-study including the experimental data set is available at DOI 10.15490/seek.1.
INVESTIGATION: 56 (http://dx.doi.org/10.15490/seek.1.
INVESTIGATION: 56).

PMID: 26648082 [PubMed - indexed for MEDLINE]

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Repositioning organohalogen drugs: a case study for identification of potent B-Raf V600E inhibitors via docking and bioassay.

Sci Rep. 2016;6:31074

Authors: Li Y, Guo B, Xu Z, Li B, Cai T, Zhang X, Yu Y, Wang H, Shi J, Zhu W

Abstract
Drug repositioning has been attracting increasingly attention for its advantages of reducing costs and risks. Statistics showed that around one quarter of the marketed drugs are organohalogens. However, no study has been reported, to the best of our knowledge, to aim at efficiently repositioning organohalogen drugs, which may be attributed to the lack of accurate halogen bonding scoring function. Here, we present a study to show that two organohalogen drugs were successfully repositioned as potent B-Raf V600E inhibitors via molecular docking with halogen bonding scoring function, namely D(3)DOCKxb developed in our lab, and bioassay. After virtual screening by D(3)DOCKxb against the database CMC (Comprehensive Medicinal Chemistry), 3 organohalogen drugs that were predicted to form strong halogen bonding with B-Raf V600E were purchased and tested with ELISA-based assay. In the end, 2 of them, rafoxanide and closantel, were identified as potent inhibitors with IC50 values of 0.07 μM and 1.90 μM, respectively, which are comparable to that of vemurafenib (IC50: 0.17 μM), a marketed drug targeting B-Raf V600E. Single point mutagenesis experiments confirmed the conformations predicted by D(3)DOCKxb. And comparison experiment revealed that halogen bonding scoring function is essential for repositioning those drugs with heavy halogen atoms in their molecular structures.

PMID: 27501852 [PubMed - in process]