Modified linear predictive coding approach for moving target tracking by Doppler radar

NASA Astrophysics Data System (ADS)

Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

2016-07-01

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

PubMed

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Drug-Target Interactions: Prediction Methods and Applications.

PubMed

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Motor cortex guides selection of predictable movement targets

PubMed Central

Woodgate, Philip J.W.; Strauss, Soeren; Sami, Saber A.; Heinke, Dietmar

2016-01-01

The present paper asks whether the motor cortex contributes to prediction-based guidance of target selection. This question was inspired by recent evidence that suggests (i) recurrent connections from the motor system into the attentional system may extract movement-relevant perceptual information and (ii) that the motor cortex cannot only generate predictions of the sensory consequences of movements but may also operate as predictor of perceptual events in general. To test this idea we employed a choice reaching task requiring participants to rapidly reach and touch a predictable or unpredictable colour target. Motor cortex activity was modulated via transcranial direct current stimulation (tDCS). In Experiment 1 target colour repetitions were predictable. Under such conditions anodal tDCS facilitated selection versus sham and cathodal tDCS. This improvement was apparent for trajectory curvature but not movement initiation. Conversely, where no predictability of colour was embedded reach performance was unaffected by tDCS. Finally, the results of a key-press experiment suggested that motor cortex involvement is restricted to tasks where the predictable target colour is movement-relevant. The outcomes are interpreted as evidence that the motor system contributes to the top-down guidance of selective attention to movement targets. PMID:25835319

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Target identification by image analysis.

PubMed

Fetz, V; Prochnow, H; Brönstrup, M; Sasse, F

2016-05-04

Covering: 1997 to the end of 2015Each biologically active compound induces phenotypic changes in target cells that are characteristic for its mode of action. These phenotypic alterations can be directly observed under the microscope or made visible by labelling structural elements or selected proteins of the cells with dyes. A comparison of the cellular phenotype induced by a compound of interest with the phenotypes of reference compounds with known cellular targets allows predicting its mode of action. While this approach has been successfully applied to the characterization of natural products based on a visual inspection of images, recent studies used automated microscopy and analysis software to increase speed and to reduce subjective interpretation. In this review, we give a general outline of the workflow for manual and automated image analysis, and we highlight natural products whose bacterial and eucaryotic targets could be identified through such approaches.

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Identification of HMX1 target genes: A predictive promoter model approach

PubMed Central

Boulling, Arnaud; Wicht, Linda

2013-01-01

Purpose A homozygous mutation in the H6 family homeobox 1 (HMX1) gene is responsible for a new oculoauricular defect leading to eye and auricular developmental abnormalities as well as early retinal degeneration (MIM 612109). However, the HMX1 pathway remains poorly understood, and in the first approach to better understand the pathway’s function, we sought to identify the target genes. Methods We developed a predictive promoter model (PPM) approach using a comparative transcriptomic analysis in the retina at P15 of a mouse model lacking functional Hmx1 (dmbo mouse) and its respective wild-type. This PPM was based on the hypothesis that HMX1 binding site (HMX1-BS) clusters should be more represented in promoters of HMX1 target genes. The most differentially expressed genes in the microarray experiment that contained HMX1-BS clusters were used to generate the PPM, which was then statistically validated. Finally, we developed two genome-wide target prediction methods: one that focused on conserving PPM features in human and mouse and one that was based on the co-occurrence of HMX1-BS pairs fitting the PPM, in human or in mouse, independently. Results The PPM construction revealed that sarcoglycan, gamma (35kDa dystrophin-associated glycoprotein) (Sgcg), teashirt zinc finger homeobox 2 (Tshz2), and solute carrier family 6 (neurotransmitter transporter, glycine) (Slc6a9) genes represented Hmx1 targets in the mouse retina at P15. Moreover, the genome-wide target prediction revealed that mouse genes belonging to the retinal axon guidance pathway were targeted by Hmx1. Expression of these three genes was experimentally validated using a quantitative reverse transcription PCR approach. The inhibitory activity of Hmx1 on Sgcg, as well as protein tyrosine phosphatase, receptor type, O (Ptpro) and Sema3f, two targets identified by the PPM, were validated with luciferase assay. Conclusions Gene expression analysis between wild-type and dmbo mice allowed us to develop a PPM

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

PubMed

Kobayashi, Hiroki; Harada, Hiroko; Nakamura, Masaomi; Futamura, Yushi; Ito, Akihiro; Yoshida, Minoru; Iemura, Shun-Ichiro; Shin-Ya, Kazuo; Doi, Takayuki; Takahashi, Takashi; Natsume, Tohru; Imoto, Masaya; Sakakibara, Yasubumi

2012-04-05

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

Macromolecular target prediction by self-organizing feature maps.

PubMed

Schneider, Gisbert; Schneider, Petra

2017-03-01

Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

Computational exploration of microRNAs from expressed sequence tags of Humulus lupulus, target predictions and expression analysis.

PubMed

Mishra, Ajay Kumar; Duraisamy, Ganesh Selvaraj; Týcová, Anna; Matoušek, Jaroslav

2015-12-01

Among computationally predicted and experimentally validated plant miRNAs, several are conserved across species boundaries in the plant kingdom. In this study, a combined experimental-in silico computational based approach was adopted for the identification and characterization of miRNAs in Humulus lupulus (hop), which is widely cultivated for use by the brewing industry and apart from, used as a medicinal herb. A total of 22 miRNAs belonging to 17 miRNA families were identified in hop following comparative computational approach and EST-based homology search according to a series of filtering criteria. Selected miRNAs were validated by end-point PCR and quantitative reverse transcription-polymerase chain reaction (qRT-PCR), confirmed the existence of conserved miRNAs in hop. Based on the characteristic that miRNAs exhibit perfect or nearly perfect complementarity with their targeted mRNA sequences, a total of 47 potential miRNA targets were identified in hop. Strikingly, the majority of predicted targets were belong to transcriptional factors which could regulate hop growth and development, including leaf, root and even cone development. Moreover, the identified miRNAs may also be involved in other cellular and metabolic processes, such as stress response, signal transduction, and other physiological processes. The cis-regulatory elements relevant to biotic and abiotic stress, plant hormone response, flavonoid biosynthesis were identified in the promoter regions of those miRNA genes. Overall, findings from this study will accelerate the way for further researches of miRNAs, their functions in hop and shows a path for the prediction and analysis of miRNAs to those species whose genomes are not available. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

Drug-target interaction prediction: A Bayesian ranking approach.

PubMed

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

PubMed

Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

2017-12-04

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

Drug-target interaction prediction from PSSM based evolutionary information.

PubMed

Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-01-01

The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

PubMed

Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

2015-07-15

Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

Prediction of target genes for miR-140-5p in pulmonary arterial hypertension using bioinformatics methods.

PubMed

Li, Fangwei; Shi, Wenhua; Wan, Yixin; Wang, Qingting; Feng, Wei; Yan, Xin; Wang, Jian; Chai, Limin; Zhang, Qianqian; Li, Manxiang

2017-12-01

The expression of microRNA (miR)-140-5p is known to be reduced in both pulmonary arterial hypertension (PAH) patients and monocrotaline-induced PAH models in rat. Identification of target genes for miR-140-5p with bioinformatics analysis may reveal new pathways and connections in PAH. This study aimed to explore downstream target genes and relevant signaling pathways regulated by miR-140-5p to provide theoretical evidences for further researches on role of miR-140-5p in PAH. Multiple downstream target genes and upstream transcription factors (TFs) of miR-140-5p were predicted in the analysis. Gene ontology (GO) enrichment analysis indicated that downstream target genes of miR-140-5p were enriched in many biological processes, such as biological regulation, signal transduction, response to chemical stimulus, stem cell proliferation, cell surface receptor signaling pathways. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis found that downstream target genes were mainly located in Notch, TGF-beta, PI3K/Akt, and Hippo signaling pathway. According to TF-miRNA-mRNA network, the important downstream target genes of miR-140-5p were PPI, TGF-betaR1, smad4, JAG1, ADAM10, FGF9, PDGFRA, VEGFA, LAMC1, TLR4, and CREB. After thoroughly reviewing published literature, we found that 23 target genes and seven signaling pathways were truly inhibited by miR-140-5p in various tissues or cells; most of these verified targets were in accordance with our present prediction. Other predicted targets still need further verification in vivo and in vitro .

Docking analysis targeted to the whole enzyme: an application to the prediction of inhibition of PTP1B by thiomorpholine and thiazolyl derivatives.

PubMed

Ganou, C A; Eleftheriou, P Th; Theodosis-Nobelos, P; Fesatidou, M; Geronikaki, A A; Lialiaris, T; Rekka, E A

2018-02-01

PTP1b is a protein tyrosine phosphatase involved in the inactivation of insulin receptor. Since inhibition of PTP1b may prolong the action of the receptor, PTP1b has become a drug target for the treatment of type II diabetes. In the present study, prediction of inhibition using docking analysis targeted specifically to the active or allosteric site was performed on 87 compounds structurally belonging to 10 different groups. Two groups, consisting of 15 thiomorpholine and 10 thiazolyl derivatives exhibiting the best prediction results, were selected for in vitro evaluation. All thiomorpholines showed inhibitory action (with IC 50 = 4-45 μΜ, Ki = 2-23 μM), while only three thiazolyl derivatives showed low inhibition (best IC 50 = 18 μΜ, Ki = 9 μΜ). However, free binding energy (E) was in accordance with the IC 50 values only for some compounds. Docking analysis targeted to the whole enzyme revealed that the compounds exhibiting IC 50 values higher than expected could bind to other peripheral sites with lower free energy, E o , than when bound to the active/allosteric site. A prediction factor, E- (Σ Eo × 0.16), which takes into account lower energy binding to peripheral sites, was proposed and was found to correlate well with the IC 50 values following an asymmetrical sigmoidal equation with r 2 = 0.9692.

TAPIR, a web server for the prediction of plant microRNA targets, including target mimics.

PubMed

Bonnet, Eric; He, Ying; Billiau, Kenny; Van de Peer, Yves

2010-06-15

We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir. Supplementary data are available at Bioinformatics online.

SeedVicious: Analysis of microRNA target and near-target sites.

PubMed

Marco, Antonio

2018-01-01

Here I describe seedVicious, a versatile microRNA target site prediction software that can be easily fitted into annotation pipelines and run over custom datasets. SeedVicious finds microRNA canonical sites plus other, less efficient, target sites. Among other novel features, seedVicious can compute evolutionary gains/losses of target sites using maximum parsimony, and also detect near-target sites, which have one nucleotide different from a canonical site. Near-target sites are important to study population variation in microRNA regulation. Some analyses suggest that near-target sites may also be functional sites, although there is no conclusive evidence for that, and they may actually be target alleles segregating in a population. SeedVicious does not aim to outperform but to complement existing microRNA prediction tools. For instance, the precision of TargetScan is almost doubled (from 11% to ~20%) when we filter predictions by the distance between target sites using this program. Interestingly, two adjacent canonical target sites are more likely to be present in bona fide target transcripts than pairs of target sites at slightly longer distances. The software is written in Perl and runs on 64-bit Unix computers (Linux and MacOS X). Users with no computing experience can also run the program in a dedicated web-server by uploading custom data, or browse pre-computed predictions. SeedVicious and its associated web-server and database (SeedBank) are distributed under the GPL/GNU license.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.

PubMed

Artemov, Artem; Aliper, Alexander; Korzinkin, Michael; Lezhnina, Ksenia; Jellen, Leslie; Zhukov, Nikolay; Roumiantsev, Sergey; Gaifullin, Nurshat; Zhavoronkov, Alex; Borisov, Nicolas; Buzdin, Anton

2015-10-06

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

DIANA-microT web server: elucidating microRNA functions through target prediction.

PubMed

Maragkakis, M; Reczko, M; Simossis, V A; Alexiou, P; Papadopoulos, G L; Dalamagas, T; Giannopoulos, G; Goumas, G; Koukis, E; Kourtis, K; Vergoulis, T; Koziris, N; Sellis, T; Tsanakas, P; Hatzigeorgiou, A G

2009-07-01

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

Ensemble Methods for MiRNA Target Prediction from Expression Data.

PubMed

Le, Thuc Duy; Zhang, Junpeng; Liu, Lin; Li, Jiuyong

2015-01-01

microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory. In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth

Ensemble Methods for MiRNA Target Prediction from Expression Data

PubMed Central

Le, Thuc Duy; Zhang, Junpeng; Liu, Lin; Li, Jiuyong

2015-01-01

Background microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory. Results In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and

Identification, Expression Analysis, and Target Prediction of Flax Genotroph MicroRNAs Under Normal and Nutrient Stress Conditions

PubMed Central

Melnikova, Nataliya V.; Dmitriev, Alexey A.; Belenikin, Maxim S.; Koroban, Nadezhda V.; Speranskaya, Anna S.; Krinitsina, Anastasia A.; Krasnov, George S.; Lakunina, Valentina A.; Snezhkina, Anastasiya V.; Sadritdinova, Asiya F.; Kishlyan, Natalya V.; Rozhmina, Tatiana A.; Klimina, Kseniya M.; Amosova, Alexandra V.; Zelenin, Alexander V.; Muravenko, Olga V.; Bolsheva, Nadezhda L.; Kudryavtseva, Anna V.

2016-01-01

Cultivated flax (Linum usitatissimum L.) is an important plant valuable for industry. Some flax lines can undergo heritable phenotypic and genotypic changes (LIS-1 insertion being the most common) in response to nutrient stress and are called plastic lines. Offspring of plastic lines, which stably inherit the changes, are called genotrophs. MicroRNAs (miRNAs) are involved in a crucial regulatory mechanism of gene expression. They have previously been assumed to take part in nutrient stress response and can, therefore, participate in genotroph formation. In the present study, we performed high-throughput sequencing of small RNAs (sRNAs) extracted from flax plants grown under normal, phosphate deficient and nutrient excess conditions to identify miRNAs and evaluate their expression. Our analysis revealed expression of 96 conserved miRNAs from 21 families in flax. Moreover, 475 novel potential miRNAs were identified for the first time, and their targets were predicted. However, none of the identified miRNAs were transcribed from LIS-1. Expression of seven miRNAs (miR168, miR169, miR395, miR398, miR399, miR408, and lus-miR-N1) with up- or down-regulation under nutrient stress (on the basis of high-throughput sequencing data) was evaluated on extended sampling using qPCR. Reference gene search identified ETIF3H and ETIF3E genes as most suitable for this purpose. Down-regulation of novel potential lus-miR-N1 and up-regulation of conserved miR399 were revealed under the phosphate deficient conditions. In addition, the negative correlation of expression of lus-miR-N1 and its predicted target, ubiquitin-activating enzyme E1 gene, as well as, miR399 and its predicted target, ubiquitin-conjugating enzyme E2 gene, was observed. Thus, in our study, miRNAs expressed in flax plastic lines and genotrophs were identified and their expression and expression of their targets was evaluated using high-throughput sequencing and qPCR for the first time. These data provide new insights

Tertiary structure-based analysis of microRNA–target interactions

PubMed Central

Gan, Hin Hark; Gunsalus, Kristin C.

2013-01-01

Current computational analysis of microRNA interactions is based largely on primary and secondary structure analysis. Computationally efficient tertiary structure-based methods are needed to enable more realistic modeling of the molecular interactions underlying miRNA-mediated translational repression. We incorporate algorithms for predicting duplex RNA structures, ionic strength effects, duplex entropy and free energy, and docking of duplex–Argonaute protein complexes into a pipeline to model and predict miRNA–target duplex binding energies. To ensure modeling accuracy and computational efficiency, we use an all-atom description of RNA and a continuum description of ionic interactions using the Poisson–Boltzmann equation. Our method predicts the conformations of two constructs of Caenorhabditis elegans let-7 miRNA–target duplexes to an accuracy of ∼3.8 Å root mean square distance of their NMR structures. We also show that the computed duplex formation enthalpies, entropies, and free energies for eight miRNA–target duplexes agree with titration calorimetry data. Analysis of duplex–Argonaute docking shows that structural distortions arising from single-base-pair mismatches in the seed region influence the activity of the complex by destabilizing both duplex hybridization and its association with Argonaute. Collectively, these results demonstrate that tertiary structure-based modeling of miRNA interactions can reveal structural mechanisms not accessible with current secondary structure-based methods. PMID:23417009

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

PubMed

Xianfang, Wang; Junmei, Wang; Xiaolei, Wang; Yue, Zhang

2017-01-01

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

PubMed Central

Xiaolei, Wang

2017-01-01

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server. PMID:28497044

Predictive model of outcome of targeted nodal assessment in colorectal cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton; Eberhardt, John; Peoples, George E; Stojadinovic, Alexander

2010-02-01

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Contextual remapping in visual search after predictable target-location changes.

PubMed

Conci, Markus; Sun, Luning; Müller, Hermann J

2011-07-01

Invariant spatial context can facilitate visual search. For instance, detection of a target is faster if it is presented within a repeatedly encountered, as compared to a novel, layout of nontargets, demonstrating a role of contextual learning for attentional guidance ('contextual cueing'). Here, we investigated how context-based learning adapts to target location (and identity) changes. Three experiments were performed in which, in an initial learning phase, observers learned to associate a given context with a given target location. A subsequent test phase then introduced identity and/or location changes to the target. The results showed that contextual cueing could not compensate for target changes that were not 'predictable' (i.e. learnable). However, for predictable changes, contextual cueing remained effective even immediately after the change. These findings demonstrate that contextual cueing is adaptive to predictable target location changes. Under these conditions, learned contextual associations can be effectively 'remapped' to accommodate new task requirements.

Identification of the feedforward component in manual control with predictable target signals.

PubMed

Drop, Frank M; Pool, Daan M; Damveld, Herman J; van Paassen, Marinus M; Mulder, Max

2013-12-01

In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.

Benchmark data sets for structure-based computational target prediction.

PubMed

Schomburg, Karen T; Rarey, Matthias

2014-08-25

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

A quick reality check for microRNA target prediction.

PubMed

Kast, Juergen

2011-04-01

The regulation of protein abundance by microRNA (miRNA)-mediated repression of mRNA translation is a rapidly growing area of interest in biochemical research. In animal cells, the miRNA seed sequence does not perfectly match that of the mRNA it targets, resulting in a large number of possible miRNA targets and varied extents of repression. Several software tools are available for the prediction of miRNA targets, yet the overlap between them is limited. Jovanovic et al. have developed and applied a targeted, quantitative approach to validate predicted miRNA target proteins. Using a proteome database, they have set up and tested selected reaction monitoring assays for approximately 20% of more than 800 predicted let-7 targets, as well as control genes in Caenorhabditis elegans. Their results demonstrate that such assays can be developed quickly and with relative ease, and applied in a high-throughput setup to verify known and identify novel miRNA targets. They also show, however, that the choice of the biological system and material has a noticeable influence on the frequency, extent and direction of the observed changes. Nonetheless, selected reaction monitoring assays, such as those developed by Jovanovic et al., represent an attractive new tool in the study of miRNA function at the organism level.

Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets.

PubMed

Prakash, Pravin; Rajakani, Raja; Gupta, Vikrant

2016-04-01

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 19-24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be -0.815 kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. Copyright © 2015 Elsevier Ltd. All rights reserved.

Differential Expression of MicroRNA and Predicted Targets in Pulmonary Sarcoidosis

PubMed Central

Crouser, Elliott D.; Julian, Mark W.; Crawford, Melissa; Shao, Guohong; Yu, Lianbo; Planck, Stephen R.; Rosenbaum, James T.; Nana-Sinkam, S. Patrick

2014-01-01

Background Recent studies show that various inflammatory diseases are regulated at the level of RNA translation by small non-coding RNAs, termed microRNAs (miRNAs). We sought to determine whether sarcoidosis tissues harbor a distinct pattern of miRNA expression and then considered their potential molecular targets. Methods and Results Genome-wide microarray analysis of miRNA expression in lung tissue and peripheral blood mononuclear cells (PBMCs) was performed and differentially expressed (DE)-miRNAs were then validated by real-time PCR. A distinct pattern of DE-miRNA expression was identified in both lung tissue and PBMCs of sarcoidosis patients. A subgroup of DE-miRNAs common to lung and lymph node tissues were predicted to target transforming growth factor (TGFβ)-regulated pathways. Likewise, the DE-miRNAs identified in PBMCs of sarcoidosis patients were predicted to target the TGFβ-regulated “wingless and integrase-1” (WNT) pathway. Conclusions This study is the first to profile miRNAs in sarcoidosis tissues and to consider their possible roles in disease pathogenesis. Our results suggest that miRNA regulate TGFβ and related WNT pathways in sarcoidosis tissues, pathways previously incriminated in the pathogenesis of sarcoidosis. PMID:22209793

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

PubMed

Bender, Andreas; Scheiber, Josef; Glick, Meir; Davies, John W; Azzaoui, Kamal; Hamon, Jacques; Urban, Laszlo; Whitebread, Steven; Jenkins, Jeremy L

2007-06-01

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application

PubMed Central

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-01-01

Background microRNAs (miRNAs) are single-stranded RNA molecules of about 20–23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. Results GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. Conclusion GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA. PMID:19534746

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application.

PubMed

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-06-16

microRNAs (miRNAs) are single-stranded RNA molecules of about 20-23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA.

In silico prediction of novel therapeutic targets using gene-disease association data.

PubMed

Ferrero, Enrico; Dunham, Ian; Sanseau, Philippe

2017-08-29

Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target

Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology

PubMed Central

2013-01-01

Herpes simplex virus (HSV) types 1 and 2 (HSV-1 and HSV-2) are the most common infectious agents of humans. No safe and effective HSV vaccines have been licensed. Reverse vaccinology is an emerging and revolutionary vaccine development strategy that starts with the prediction of vaccine targets by informatics analysis of genome sequences. Vaxign (http://www.violinet.org/vaxign) is the first web-based vaccine design program based on reverse vaccinology. In this study, we used Vaxign to analyze 52 herpesvirus genomes, including 3 HSV-1 genomes, one HSV-2 genome, 8 other human herpesvirus genomes, and 40 non-human herpesvirus genomes. The HSV-1 strain 17 genome that contains 77 proteins was used as the seed genome. These 77 proteins are conserved in two other HSV-1 strains (strain F and strain H129). Two envelope glycoproteins gJ and gG do not have orthologs in HSV-2 or 8 other human herpesviruses. Seven HSV-1 proteins (including gJ and gG) do not have orthologs in all 40 non-human herpesviruses. Nineteen proteins are conserved in all human herpesviruses, including capsid scaffold protein UL26.5 (NP_044628.1). As the only HSV-1 protein predicted to be an adhesin, UL26.5 is a promising vaccine target. The MHC Class I and II epitopes were predicted by the Vaxign Vaxitop prediction program and IEDB prediction programs recently installed and incorporated in Vaxign. Our comparative analysis found that the two programs identified largely the same top epitopes but also some positive results predicted from one program might not be positive from another program. Overall, our Vaxign computational prediction provides many promising candidates for rational HSV vaccine development. The method is generic and can also be used to predict other viral vaccine targets. PMID:23514126

Predicting ground contact events for a continuum of gait types: An application of targeted machine learning using principal component analysis.

PubMed

Osis, Sean T; Hettinga, Blayne A; Ferber, Reed

2016-05-01

An ongoing challenge in the application of gait analysis to clinical settings is the standardized detection of temporal events, with unobtrusive and cost-effective equipment, for a wide range of gait types. The purpose of the current study was to investigate a targeted machine learning approach for the prediction of timing for foot strike (or initial contact) and toe-off, using only kinematics for walking, forefoot running, and heel-toe running. Data were categorized by gait type and split into a training set (∼30%) and a validation set (∼70%). A principal component analysis was performed, and separate linear models were trained and validated for foot strike and toe-off, using ground reaction force data as a gold-standard for event timing. Results indicate the model predicted both foot strike and toe-off timing to within 20ms of the gold-standard for more than 95% of cases in walking and running gaits. The machine learning approach continues to provide robust timing predictions for clinical use, and may offer a flexible methodology to handle new events and gait types. Copyright © 2016 Elsevier B.V. All rights reserved.

Computational Predictions Provide Insights into the Biology of TAL Effector Target Sites

PubMed Central

Grau, Jan; Wolf, Annett; Reschke, Maik; Bonas, Ulla; Posch, Stefan; Boch, Jens

2013-01-01

Transcription activator-like (TAL) effectors are injected into host plant cells by Xanthomonas bacteria to function as transcriptional activators for the benefit of the pathogen. The DNA binding domain of TAL effectors is composed of conserved amino acid repeat structures containing repeat-variable diresidues (RVDs) that determine DNA binding specificity. In this paper, we present TALgetter, a new approach for predicting TAL effector target sites based on a statistical model. In contrast to previous approaches, the parameters of TALgetter are estimated from training data computationally. We demonstrate that TALgetter successfully predicts known TAL effector target sites and often yields a greater number of predictions that are consistent with up-regulation in gene expression microarrays than an existing approach, Target Finder of the TALE-NT suite. We study the binding specificities estimated by TALgetter and approve that different RVDs are differently important for transcriptional activation. In subsequent studies, the predictions of TALgetter indicate a previously unreported positional preference of TAL effector target sites relative to the transcription start site. In addition, several TAL effectors are predicted to bind to the TATA-box, which might constitute one general mode of transcriptional activation by TAL effectors. Scrutinizing the predicted target sites of TALgetter, we propose several novel TAL effector virulence targets in rice and sweet orange. TAL-mediated induction of the candidates is supported by gene expression microarrays. Validity of these targets is also supported by functional analogy to known TAL effector targets, by an over-representation of TAL effector targets with similar function, or by a biological function related to pathogen infection. Hence, these predicted TAL effector virulence targets are promising candidates for studying the virulence function of TAL effectors. TALgetter is implemented as part of the open-source Java library

Predicting selective drug targets in cancer through metabolic networks

PubMed Central

Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

2011-01-01

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

Penetration analysis of projectile with inclined concrete target

NASA Astrophysics Data System (ADS)

Kim, S. B.; Kim, H. W.; Yoo, Y. H.

2015-09-01

This paper presents numerical analysis result of projectile penetration with concrete target. We applied dynamic material properties of 4340 steels, aluminium and explosive for projectile body. Dynamic material properties were measured with static tensile testing machine and Hopkinson pressure bar tests. Moreover, we used three concrete damage models included in LS-DYNA 3D, such as SOIL_CONCRETE, CSCM (cap model with smooth interaction) and CONCRETE_DAMAGE (K&C concrete) models. Strain rate effect for concrete material is important to predict the fracture deformation and shape of concrete, and penetration depth for projectiles. CONCRETE_DAMAGE model with strain rate effect also applied to penetration analysis. Analysis result with CSCM model shows good agreement with penetration experimental data. The projectile trace and fracture shapes of concrete target were compared with experimental data.

TarPmiR: a new approach for microRNA target site prediction.

PubMed

Ding, Jun; Li, Xiaoman; Hu, Haiyan

2016-09-15

The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/ CONTACTS: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Drug Target Mining and Analysis of the Chinese Tree Shrew for Pharmacological Testing

PubMed Central

Liu, Jie; Lee, Wen-hui; Zhang, Yun

2014-01-01

The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research. PMID:25105297

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

PubMed

Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

2017-11-27

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Texture metric that predicts target detection performance

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.

2015-12-01

Two texture metrics based on gray level co-occurrence error (GLCE) are used to predict probability of detection and mean search time. The two texture metrics are local clutter metrics and are based on the statistics of GLCE probability distributions. The degree of correlation between various clutter metrics and the target detection performance of the nine military vehicles in complex natural scenes found in the Search_2 dataset are presented. Comparison is also made between four other common clutter metrics found in the literature: root sum of squares, Doyle, statistical variance, and target structure similarity. The experimental results show that the GLCE energy metric is a better predictor of target detection performance when searching for targets in natural scenes than the other clutter metrics studied.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.

PubMed

Ahadi, Alireza; Sablok, Gaurav; Hutvagner, Gyorgy

2017-04-07

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A systems-pharmacology analysis of herbal medicines used in health improvement treatment: predicting potential new drugs and targets.

PubMed

Liu, Jianling; Pei, Mengjie; Zheng, Chunli; Li, Yan; Wang, Yonghua; Lu, Aiping; Yang, Ling

2013-01-01

For thousands of years, tonic herbs have been successfully used all around the world to improve health, energy, and vitality. However, their underlying mechanisms of action in molecular/systems levels are still a mystery. In this work, two sets of tonic herbs, so called Qi-enriching herbs (QEH) and Blood-tonifying herbs (BTH) in TCM, were selected to elucidate why they can restore proper balance and harmony inside body, organ and energy system. Firstly, a pattern recognition model based on artificial neural network and discriminant analysis for assessing the molecular difference between QEH and BTH was developed. It is indicated that QEH compounds have high lipophilicity while BTH compounds possess high chemical reactivity. Secondly, a systematic investigation integrating ADME (absorption, distribution, metabolism, and excretion) prediction, target fishing and network analysis was performed and validated on these herbs to obtain the compound-target associations for reconstructing the biologically-meaningful networks. The results suggest QEH enhance physical strength, immune system and normal well-being, acting as adjuvant therapy for chronic disorders while BTH stimulate hematopoiesis function in body. As an emerging approach, the systems pharmacology model might facilitate to understand the mechanisms of action of the tonic herbs, which brings about new development for complementary and alternative medicine.

On Why Targets Evoke P3 Components in Prediction Tasks: Drawing an Analogy between Prediction and Matching Tasks

PubMed Central

Verleger, Rolf; Cäsar, Stephanie; Siller, Bastian; Śmigasiewicz, Kamila

2017-01-01

P3 is the most conspicuous component in recordings of stimulus-evoked EEG potentials from the human scalp, occurring whenever some task has to be performed with the stimuli. The process underlying P3 has been assumed to be the updating of expectancies. More recently, P3 has been related to decision processing and to activation of established stimulus-response associations (S/R-link hypothesis). However, so far this latter approach has not provided a conception about how to explain the occurrence of P3 with predicted stimuli, although P3 was originally discovered in a prediction task. The present article proposes such a conception. We assume that the internal responses right or wrong both become associatively linked to each predicted target and that one of these two response alternatives gets activated as a function of match or mismatch of the target to the preceding prediction. This seems similar to comparison tasks where responses depend on the matching of the target stimulus with a preceding first stimulus (S1). Based on this idea, this study compared the effects of frequencies of first events (predictions or S1) on target-evoked P3s in prediction and comparison tasks. Indeed, frequencies not only of targets but also of first events had similar effects across tasks on target-evoked P3s. These results support the notion that P3 evoked by predicted stimuli reflects activation of appropriate internal “match” or “mismatch” responses, which is compatible with S/R-link hypothesis. PMID:29066965

Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy.

PubMed

Jung, Jinmyung; Kwon, Mijin; Bae, Sunghwa; Yim, Soorin; Lee, Doheon

2018-03-05

Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation of the status of phosphorylation, with a reasonable assumption that the recovery of abnormally phosphorylated proteins can be a treatment for muscle atrophy. The Petri net model was employed to simulate phosphorylation status in three states, i.e. reference, atrophic and each gene-inhibited state based on the myocyte-specific phosphorylation network. Here, we newly devised a phosphorylation specific Petri net that involves two types of transitions (phosphorylation or de-phosphorylation) and two types of places (activation with or without phosphorylation). Before predicting therapeutic targets, the simulation results in reference and atrophic states were validated by Western blotting experiments detecting five marker proteins, i.e. RELA, SMAD2, SMAD3, FOXO1 and FOXO3. Finally, we determined 37 potential therapeutic targets whose inhibition recovers the phosphorylation status from an atrophic state as indicated by the five validated marker proteins. In the evaluation, we confirmed that the 37 potential targets were enriched for muscle atrophy-related terms such as actin and muscle contraction processes, and they were also significantly overlapping with the genes associated with muscle atrophy reported in the Comparative Toxicogenomics Database (p-value < 0.05). Furthermore, we noticed that they included several proteins that could not be characterized by the shortest path analysis. The three potential targets, i.e. BMPR1B, ROCK, and LEPR, were manually validated with the literature. In this study, we suggest a new approach to predict potential therapeutic targets of muscle atrophy with an analysis of phosphorylation status simulated by Petri net. We generated a list of the potential

TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples.

PubMed

Bandyopadhyay, Sanghamitra; Mitra, Ramkrishna

2009-10-15

Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training. In this article, we have identified approximately 300 tissue-specific negative examples using a novel approach that involves expression profiling of both miRNAs and mRNAs, miRNA-mRNA structural interactions and seed-site conservation. The newly generated negative examples are validated with pSILAC dataset, which elucidate the fact that the identified non-targets are indeed non-targets.These high-throughput tissue-specific negative examples and a set of experimentally verified positive examples are then used to build a system called TargetMiner, a support vector machine (SVM)-based classifier. In addition to assessing the prediction accuracy on cross-validation experiments, TargetMiner has been validated with a completely independent experimental test dataset. Our method outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. We achieve a significantly higher sensitivity and specificity of 69% and 67.8% based on a pool of 90 feature set and 76.5% and 66.1% using a set of 30 selected feature set on the completely independent test dataset. In order to establish the effectiveness of the systematically generated negative examples, the SVM is trained using a different set of negative data generated using the method in Yousef et al. A significantly higher false positive rate (70.6%) is observed when tested on the independent set, while all other factors are kept the

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

PubMed Central

Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

2013-01-01

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

Drug target identification using network analysis: Taking active components in Sini decoction as an example

NASA Astrophysics Data System (ADS)

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-04-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

Drug target identification using network analysis: Taking active components in Sini decoction as an example

PubMed Central

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-01-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound. PMID:27095146

psRNATarget: a plant small RNA target analysis server

PubMed Central

Dai, Xinbin; Zhao, Patrick Xuechun

2011-01-01

Plant endogenous non-coding short small RNAs (20–24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s). We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to ‘open’ secondary structure around small RNA’s target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript. The psRNATarget server is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded small RNAs and transcript sequences; and outputs a comprehensive list of small RNA/target pairs along with the online tools for batch downloading, key word searching and results sorting. The psRNATarget server is freely available at http://plantgrn.noble.org/psRNATarget/. PMID:21622958

Predicting the size-dependent tissue accumulation of agents released from vascular targeted nanoconstructs

NASA Astrophysics Data System (ADS)

de Tullio, Marco D.; Singh, Jaykrishna; Pascazio, Giuseppe; Decuzzi, Paolo

2014-03-01

Vascular targeted nanoparticles have been developed for the delivery of therapeutic and imaging agents in cancer and cardiovascular diseases. However, at authors' knowledge, a comprehensive systematic analysis on their delivery efficiency is still missing. Here, a computational model is developed to predict the vessel wall accumulation of agents released from vascular targeted nanoconstructs. The transport problem for the released agent is solved using a finite volume scheme in terms of three governing parameters: the local wall shear rate , ranging from to ; the wall filtration velocity , varying from to ; and the agent diffusion coefficient , ranging from to . It is shown that the percentage of released agent adsorbing on the vessel walls in the vicinity of the vascular targeted nanoconstructs reduces with an increase in shear rate , and with a decrease in filtration velocity and agent diffusivity . In particular, in tumor microvessels, characterized by lower shear rates () and higher filtration velocities (), an agent with a diffusivity (i.e. a 50 nm particle) is predicted to deposit on the vessel wall up to of the total released dose. Differently, drug molecules, exhibiting a smaller size and much higher diffusion coefficient (), are predicted to accumulate up to . In healthy vessels, characterized by higher and lower , the largest majority of the released agent is redistributed directly in the circulation. These data suggest that drug molecules and small nanoparticles only can be efficiently released from vascular targeted nanoconstructs towards the diseased vessel walls and tissue.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

PubMed

Lu, Yao; Deng, Jingyuan; Rhodes, Judith C; Lu, Hui; Lu, Long Jason

2014-06-01

Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation". In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to

Predicting new molecular targets for known drugs

PubMed Central

Keiser, Michael J.; Setola, Vincent; Irwin, John J.; Laggner, Christian; Abbas, Atheir; Hufeisen, Sandra J.; Jensen, Niels H.; Kuijer, Michael B.; Matos, Roberto C.; Tran, Thuy B.; Whaley, Ryan; Glennon, Richard A.; Hert, Jérôme; Thomas, Kelan L.H.; Edwards, Douglas D.; Shoichet, Brian K.; Roth, Bryan L.

2009-01-01

Whereas drugs are intended to be selective, at least some bind to several physiologic targets, explaining both side effects and efficacy. As many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here, we compared 3,665 FDA-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-HT transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (< 100 nM). The physiological relevance of one such, the drug DMT on serotonergic receptors, was confirmed in a knock-out mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs. PMID:19881490

Integrating Transcriptomics with Metabolic Modeling Predicts Biomarkers and Drug Targets for Alzheimer's Disease

PubMed Central

Stempler, Shiri; Yizhak, Keren; Ruppin, Eytan

2014-01-01

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment. PMID:25127241

Role of retinal slip in the prediction of target motion during smooth and saccadic pursuit.

PubMed

de Brouwer, S; Missal, M; Lefèvre, P

2001-08-01

Visual tracking of moving targets requires the combination of smooth pursuit eye movements with catch-up saccades. In primates, catch-up saccades usually take place only during pursuit initiation because pursuit gain is close to unity. This contrasts with the lower and more variable gain of smooth pursuit in cats, where smooth eye movements are intermingled with catch-up saccades during steady-state pursuit. In this paper, we studied in detail the role of retinal slip in the prediction of target motion during smooth and saccadic pursuit in the cat. We found that the typical pattern of pursuit in the cat was a combination of smooth eye movements with saccades. During smooth pursuit initiation, there was a correlation between peak eye acceleration and target velocity. During pursuit maintenance, eye velocity oscillated at approximately 3 Hz around a steady-state value. The average gain of smooth pursuit was approximately 0.5. Trained cats were able to continue pursuing in the absence of a visible target, suggesting a role of the prediction of future target motion in this species. The analysis of catch-up saccades showed that the smooth-pursuit motor command is added to the saccadic command during catch-up saccades and that both position error and retinal slip are taken into account in their programming. The influence of retinal slip on catch-up saccades showed that prediction about future target motion is used in the programming of catch-up saccades. Altogether, these results suggest that pursuit systems in primates and cats are qualitatively similar, with a lower average gain in the cat and that prediction affects both saccades and smooth eye movements during pursuit.

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

PubMed

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

Imbalanced target prediction with pattern discovery on clinical data repositories.

PubMed

Chan, Tak-Ming; Li, Yuxi; Chiau, Choo-Chiap; Zhu, Jane; Jiang, Jie; Huo, Yong

2017-04-20

Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin

PubMed Central

Dai, Shao-Xing; Li, Wen-Xing

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view. PMID:26989626

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin.

PubMed

Dai, Shao-Xing; Li, Wen-Xing; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view.

In Silico Approach for SAR Analysis of the Predicted Model of DEPDC1B: A Novel Target for Oral Cancer.

PubMed

Ahuja, Palak; Singh, Kailash

2016-01-01

With the incidence rate of oral carcinogenesis increasing in the Southeast-Asian countries, due to increase in the consumption of tobacco and betel quid as well as infection from human papillomavirus, specifically type 16, it becomes crucial to predict the transition of premalignant lesion to cancerous tissue at an initial stage in order to control the process of oncogenesis. DEPDC1B, downregulated in the presence of E2 protein, was recently found to be overexpressed in oral cancer, which can possibly be explained by the disruption of the E2 open reading frame upon the integration of viral genome into the host genome. DEPDC1B mediates its effect by directly interacting with Rac1 protein, which is known to regulate important cell signaling pathways. Therefore, DEPDC1B can be a potential biomarker as well as a therapeutic target for diagnosing and curing the disease. However, the lack of 3D model of the structure makes the utilization of DEPDC1B as a therapeutic target difficult. The present study focuses on the prediction of a suitable 3D model of the protein as well as the analysis of protein-protein interaction between DEPDC1B and Rac1 protein using PatchDock web server along with the identification of allosteric or regulatory sites of DEPDC1B.

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

PubMed

Betel, Doron; Koppal, Anjali; Agius, Phaedra; Sander, Chris; Leslie, Christina

2010-01-01

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Research of maneuvering target prediction and tracking technology based on IMM algorithm

NASA Astrophysics Data System (ADS)

Cao, Zheng; Mao, Yao; Deng, Chao; Liu, Qiong; Chen, Jing

2016-09-01

Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision

Time Critical Targeting: Predictive Vs Reactionary Methods An Analysis For The Future

DTIC Science & Technology

2002-06-01

critical targets. To conduct the analysis, a four-step process is used. First, research is conducted to determine which future aircraft, spacecraft , and...the most promising aircraft, spacecraft , and weapons are determined , they are categorized for use in either the reactive or preemptive method. For...no significant delays, 292; Alan Vick et al., 17. 33 Ibid. 12 sensors are Electro-optical (EO) sensors, thermal imagers , and signal intelligence

Gambling score in earthquake prediction analysis

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.

2011-03-01

The number of successes and the space-time alarm rate are commonly used to characterize the strength of an earthquake prediction method and the significance of prediction results. It has been recently suggested to use a new characteristic to evaluate the forecaster's skill, the gambling score (GS), which incorporates the difficulty of guessing each target event by using different weights for different alarms. We expand parametrization of the GS and use the M8 prediction algorithm to illustrate difficulties of the new approach in the analysis of the prediction significance. We show that the level of significance strongly depends (1) on the choice of alarm weights, (2) on the partitioning of the entire alarm volume into component parts and (3) on the accuracy of the spatial rate measure of target events. These tools are at the disposal of the researcher and can affect the significance estimate. Formally, all reasonable GSs discussed here corroborate that the M8 method is non-trivial in the prediction of 8.0 ≤M < 8.5 events because the point estimates of the significance are in the range 0.5-5 per cent. However, the conservative estimate 3.7 per cent based on the number of successes seems preferable owing to two circumstances: (1) it is based on relative values of the spatial rate and hence is more stable and (2) the statistic of successes enables us to construct analytically an upper estimate of the significance taking into account the uncertainty of the spatial rate measure.

Earthquake prediction analysis based on empirical seismic rate: the M8 algorithm

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.

2010-12-01

The quality of space-time earthquake prediction is usually characterized by a 2-D error diagram (n, τ), where n is the fraction of failures-to-predict and τ is the local rate of alarm averaged in space. The most reasonable averaging measure for analysis of a prediction strategy is the normalized rate of target events λ(dg) in a subarea dg. In that case the quantity H = 1 - (n + τ) determines the prediction capability of the strategy. The uncertainty of λ(dg) causes difficulties in estimating H and the statistical significance, α, of prediction results. We investigate this problem theoretically and show how the uncertainty of the measure can be taken into account in two situations, viz., the estimation of α and the construction of a confidence zone for the (n, τ)-parameters of the random strategies. We use our approach to analyse the results from prediction of M >= 8.0 events by the M8 method for the period 1985-2009 (the M8.0+ test). The model of λ(dg) based on the events Mw >= 5.5, 1977-2004, and the magnitude range of target events 8.0 <= M < 8.5 are considered as basic to this M8 analysis. We find the point and upper estimates of α and show that they are still unstable because the number of target events in the experiment is small. However, our results argue in favour of non-triviality of the M8 prediction algorithm.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

Target-motion prediction for robotic search and rescue in wilderness environments.

PubMed

Macwan, Ashish; Nejat, Goldie; Benhabib, Beno

2011-10-01

This paper presents a novel modular methodology for predicting a lost person's (motion) behavior for autonomous coordinated multirobot wilderness search and rescue. The new concept of isoprobability curves is introduced and developed, which represents a unique mechanism for identifying the target's probable location at any given time within the search area while accounting for influences such as terrain topology, target physiology and psychology, clues found, etc. The isoprobability curves are propagated over time and space. The significant tangible benefit of the proposed target-motion prediction methodology is demonstrated through a comparison to a nonprobabilistic approach, as well as through a simulated realistic wilderness search scenario.

Predicting Drug-Target Interactions Based on Small Positive Samples.

PubMed

Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

2018-01-01

A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

Murine Hyperglycemic Vasculopathy and Cardiomyopathy: Whole-Genome Gene Expression Analysis Predicts Cellular Targets and Regulatory Networks Influenced by Mannose Binding Lectin

PubMed Central

Zou, Chenhui; La Bonte, Laura R.; Pavlov, Vasile I.; Stahl, Gregory L.

2012-01-01

Hyperglycemia, in the absence of type 1 or 2 diabetes, is an independent risk factor for cardiovascular disease. We have previously demonstrated a central role for mannose binding lectin (MBL)-mediated cardiac dysfunction in acute hyperglycemic mice. In this study, we applied whole-genome microarray data analysis to investigate MBL’s role in systematic gene expression changes. The data predict possible intracellular events taking place in multiple cellular compartments such as enhanced insulin signaling pathway sensitivity, promoted mitochondrial respiratory function, improved cellular energy expenditure and protein quality control, improved cytoskeleton structure, and facilitated intracellular trafficking, all of which may contribute to the organismal health of MBL null mice against acute hyperglycemia. Our data show a tight association between gene expression profile and tissue function which might be a very useful tool in predicting cellular targets and regulatory networks connected with in vivo observations, providing clues for further mechanistic studies. PMID:22375142

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

PubMed Central

Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

2012-01-01

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

Predicting the Noise of High Power Fluid Targets Using Computational Fluid Dynamics

NASA Astrophysics Data System (ADS)

Moore, Michael; Covrig Dusa, Silviu

The 2.5 kW liquid hydrogen (LH2) target used in the Qweak parity violation experiment is the highest power LH2 target in the world and the first to be designed with Computational Fluid Dynamics (CFD) at Jefferson Lab. The Qweak experiment determined the weak charge of the proton by measuring the parity-violating elastic scattering asymmetry of longitudinally polarized electrons from unpolarized liquid hydrogen at small momentum transfer (Q2 = 0 . 025 GeV2). This target satisfied the design goals of < 1 % luminosity reduction and < 5 % contribution to the total asymmetry width (the Qweak target achieved 2 % or 55ppm). State of the art time dependent CFD simulations are being developed to improve the predictions of target noise on the time scale of the electron beam helicity period. These predictions will be bench-marked with the Qweak target data. This work is an essential component in future designs of very high power low noise targets like MOLLER (5 kW, target noise asymmetry contribution < 25 ppm) and MESA (4.5 kW).

Brainstorming: weighted voting prediction of inhibitors for protein targets.

PubMed

Plewczynski, Dariusz

2011-09-01

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.

Hot-spot analysis for drug discovery targeting protein-protein interactions.

PubMed

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine

PubMed Central

Meng, Jun; Shi, Lin; Luan, Yushi

2014-01-01

Background Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions. Results Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species. Conclusions The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided. PMID:25051153

Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Targeted Analysis of Whole Genome Sequence Data to Diagnose Genetic Cardiomyopathy

DOE PAGES

Golbus, Jessica R.; Puckelwartz, Megan J.; Dellefave-Castillo, Lisa; ...

2014-09-01

Background—Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of more than 50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated $1000 genome, it is expected that genetic testing will shift towards comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. Methods and Results—Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused onmore » 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1-14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and/or segregation analysis. Three of three previously identified primary mutations were detected by this analysis. In six subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and/or had additional pathological correlation to provide evidence for causality. For two subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. We conclude that these pilot data demonstrate that ~30-40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.« less

Changes in predictive cuing modulate the hemispheric distribution of the P1 inhibitory response to attentional targets.

PubMed

Lasaponara, Stefano; D' Onofrio, Marianna; Dragone, Alessio; Pinto, Mario; Caratelli, Ludovica; Doricchi, Fabrizio

2017-05-01

Brain activity related to orienting of attention with spatial cues and brain responses to attentional targets are influenced the probabilistic contingency between cues and targets. Compared to predictive cues, cues predicting at chance the location of targets reduce the filtering out of uncued locations and the costs in reorienting attention to targets presented at these locations. Slagter et al. (2016) have recently suggested that the larger target related P1 component that is found in the hemisphere ipsilateral to validly cued targets reflects stimulus-driven inhibition in the processing of the unstimulated side of space contralateral to the same hemisphere. Here we verified whether the strength of this inhibition and the amplitude of the corresponding P1 wave are modulated by the probabilistic link between cues and targets. Healthy participants performed a task of endogenous orienting once with predictive and once with non-predictive directional cues. In the non-predictive condition we observed a drop in the amplitude of the P1 ipsilateral to the target and in the costs of reorienting. No change in the inter-hemispheric latencies of the P1 was found between the two predictive conditions. The N1 facilitatory component was unaffected by predictive cuing. These results show that the predictive context modulates the strength of the inhibitory P1 response and that this modulation is not matched with changes in the inter-hemispheric interaction between the P1 generators of the two hemispheres. Copyright © 2017. Published by Elsevier Ltd.

Evolution of egg target size: an analysis of selection on correlated characters.

PubMed

Podolsky, R D

2001-12-01

In broadcast-spawning marine organisms, chronic sperm limitation should select for traits that improve chances of sperm-egg contact. One mechanism may involve increasing the size of the physical or chemical target for sperm. However, models of fertilization kinetics predict that increasing egg size can reduce net zygote production due to an associated decline in fecundity. An alternate method for increasing physical target size is through addition of energetically inexpensive external structures, such as the jelly coats typical of eggs in species from several phyla. In selection experiments on eggs of the echinoid Dendraster excentricus, in which sperm was used as the agent of selection, eggs with larger overall targets were favored in fertilization. Actual shifts in target size following selection matched quantitative predictions of a model that assumed fertilization was proportional to target size. Jelly volume and ovum volume, two characters that contribute to target size, were correlated both within and among females. A cross-sectional analysis of selection partitioned the independent effects of these characters on fertilization success and showed that they experience similar direct selection pressures. Coupled with data on relative organic costs of the two materials, these results suggest that, under conditions where fertilization is limited by egg target size, selection should favor investment in low-cost accessory structures and may have a relatively weak effect on the evolution of ovum size.

Uncovering leaf rust responsive miRNAs in wheat (Triticum aestivum L.) using high-throughput sequencing and prediction of their targets through degradome analysis.

PubMed

Kumar, Dhananjay; Dutta, Summi; Singh, Dharmendra; Prabhu, Kumble Vinod; Kumar, Manish; Mukhopadhyay, Kunal

2017-01-01

Deep sequencing identified 497 conserved and 559 novel miRNAs in wheat, while degradome analysis revealed 701 targets genes. QRT-PCR demonstrated differential expression of miRNAs during stages of leaf rust progression. Bread wheat (Triticum aestivum L.) is an important cereal food crop feeding 30 % of the world population. Major threat to wheat production is the rust epidemics. This study was targeted towards identification and functional characterizations of micro(mi)RNAs and their target genes in wheat in response to leaf rust ingression. High-throughput sequencing was used for transcriptome-wide identification of miRNAs and their expression profiling in retort to leaf rust using mock and pathogen-inoculated resistant and susceptible near-isogenic wheat plants. A total of 1056 mature miRNAs were identified, of which 497 miRNAs were conserved and 559 miRNAs were novel. The pathogen-inoculated resistant plants manifested more miRNAs compared with the pathogen infected susceptible plants. The miRNA counts increased in susceptible isoline due to leaf rust, conversely, the counts decreased in the resistant isoline in response to pathogenesis illustrating precise spatial tuning of miRNAs during compatible and incompatible interaction. Stem-loop quantitative real-time PCR was used to profile 10 highly differentially expressed miRNAs obtained from high-throughput sequencing data. The spatio-temporal profiling validated the differential expression of miRNAs between the isolines as well as in retort to pathogen infection. Degradome analysis provided 701 predicted target genes associated with defense response, signal transduction, development, metabolism, and transcriptional regulation. The obtained results indicate that wheat isolines employ diverse arrays of miRNAs that modulate their target genes during compatible and incompatible interaction. Our findings contribute to increase knowledge on roles of microRNA in wheat-leaf rust interactions and could help in rust

Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

PubMed

El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

2015-04-01

This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

Effects of Target Fragmentation on Evaluation of LET Spectra From Space Radiation in Low-Earth Orbit (LEO) Environment: Impact on SEU Predictions

NASA Technical Reports Server (NTRS)

Shinn, J. L.; Cucinotta, F. A.; Badhwar, G. D.; ONeill, P. M.; Badavi, F. F.

1995-01-01

Recent improvements in the radiation transport code HZETRN/BRYNTRN and galactic cosmic ray environmental model have provided an opportunity to investigate the effects of target fragmentation on estimates of single event upset (SEU) rates for spacecraft memory devices. Since target fragments are mostly of very low energy, an SEU prediction model has been derived in terms of particle energy rather than linear energy transfer (LET) to account for nonlinear relationship between range and energy. Predictions are made for SEU rates observed on two Shuttle flights, each at low and high inclination orbit. Corrections due to track structure effects are made for both high energy ions with track structure larger than device sensitive volume and for low energy ions with dense track where charge recombination is important. Results indicate contributions from target fragments are relatively important at large shield depths (or any thick structure material) and at low inclination orbit. Consequently, a more consistent set of predictions for upset rates observed in these two flights is reached when compared to an earlier analysis with CREME model. It is also observed that the errors produced by assuming linear relationship in range and energy in the earlier analysis have fortuitously canceled out the errors for not considering target fragmentation and track structure effects.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

PubMed

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Molecular Composition Analysis of Distant Targets

NASA Technical Reports Server (NTRS)

Hughes, Gary B.; Lubin, Philip

2017-01-01

This document is the Final Report for NASA Innovative Advanced Concepts (NIAC) Phase I Grant 15-NIAC16A-0145, titled Molecular Composition Analysis of Distant Targets. The research was focused on developing a system concept for probing the molecular composition of cold solar system targets, such as Asteroids, Comets, Planets and Moons from a distant vantage, for example from a spacecraft that is orbiting the target (Hughes et al., 2015). The orbiting spacecraft is equipped with a high-power laser, which is run by electricity from photovoltaic panels. The laser is directed at a spot on the target. Materials on the surface of the target are heated by the laser beam, and begin to melt and then evaporate, forming a plume of asteroid molecules in front of the heated spot. The heated spot glows, producing blackbody illumination that is visible from the spacecraft, via a path through the evaporated plume. As the blackbody radiation from the heated spot passes through the plume of evaporated material, molecules in the plume absorb radiation in a manner that is specific to the rotational and vibrational characteristics of the specific molecules. A spectrometer aboard the spacecraft is used to observe absorption lines in the blackbody signal. The pattern of absorption can be used to estimate the molecular composition of materials in the plume, which originated on the target. Focusing on a single spot produces a borehole, and shallow subsurface profiling of the targets bulk composition is possible. At the beginning of the Phase I research, the estimated Technology Readiness Level (TRL) of the system was TRL-1. During the Phase I research, an end-to-end theoretical model of the sensor system was developed from first principles. The model includes laser energy and optical propagation, target heating, melting and evaporation of target material, plume density, thermal radiation from the heated spot, molecular cross section of likely asteroid materials, and estimation of the

Patent Analysis for Supporting Merger and Acquisition (M&A) Prediction: A Data Mining Approach

NASA Astrophysics Data System (ADS)

Wei, Chih-Ping; Jiang, Yu-Syun; Yang, Chin-Sheng

M&A plays an increasingly important role in the contemporary business environment. Companies usually conduct M&A to pursue complementarity from other companies for preserving and/or extending their competitive advantages. For the given bidder company, a critical first step to the success of M&A activities is the appropriate selection of target companies. However, existing studies on M&A prediction incur several limitations, such as the exclusion of technological variables in M&A prediction models and the omission of the profile of the respective bidder company and its compatibility with candidate target companies. In response to these limitations, we propose an M&A prediction technique which not only encompasses technological variables derived from patent analysis as prediction indictors but also takes into account the profiles of both bidder and candidate target companies when building an M&A prediction model. We collect a set of real-world M&A cases to evaluate the proposed technique. The evaluation results are encouraging and will serve as a basis for future studies.

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PubMed

Keum, Jongsoo; Nam, Hojung

2017-01-01

Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.

Statistical analysis of target acquisition sensor modeling experiments

NASA Astrophysics Data System (ADS)

Deaver, Dawne M.; Moyer, Steve

2015-05-01

The U.S. Army RDECOM CERDEC NVESD Modeling and Simulation Division is charged with the development and advancement of military target acquisition models to estimate expected soldier performance when using all types of imaging sensors. Two elements of sensor modeling are (1) laboratory-based psychophysical experiments used to measure task performance and calibrate the various models and (2) field-based experiments used to verify the model estimates for specific sensors. In both types of experiments, it is common practice to control or measure environmental, sensor, and target physical parameters in order to minimize uncertainty of the physics based modeling. Predicting the minimum number of test subjects required to calibrate or validate the model should be, but is not always, done during test planning. The objective of this analysis is to develop guidelines for test planners which recommend the number and types of test samples required to yield a statistically significant result.

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.

PubMed

Hao, Ming; Wang, Yanli; Bryant, Stephen H

2016-02-25

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. Published by Elsevier B.V.

Use of thermodynamic coupling between antibody-antigen binding and phospholipid acyl chain phase transition energetics to predict immunoliposome targeting affinity.

PubMed

Klegerman, Melvin E; Zou, Yuejiao; Golunski, Eva; Peng, Tao; Huang, Shao-Ling; McPherson, David D

2014-09-01

Thermodynamic analysis of ligand-target binding has been a useful tool for dissecting the nature of the binding mechanism and, therefore, potentially can provide valuable information regarding the utility of targeted formulations. Based on a consistent coupling of antibody-antigen binding and gel-liquid crystal transition energetics observed for antibody-phosphatidylethanolamine (Ab-PE) conjugates, we hypothesized that the thermodynamic parameters and the affinity for antigen of the Ab-PE conjugates could be effectively predicted once the corresponding information for the unconjugated antibody is determined. This hypothesis has now been tested in nine different antibody-targeted echogenic liposome (ELIP) preparations, where antibody is conjugated to dipalmitoylphosphatidylethanolamine (DPPE) head groups through a thioether linkage. Predictions were satisfactory (affinity not significantly different from the population of values found) in five cases (55.6%), but the affinity of the unconjugated antibody was not significantly different from the population of values found in six cases (66.7%), indicating that the affinities of the conjugated antibody tended not to deviate appreciably from those of the free antibody. While knowledge of the affinities of free antibodies may be sufficient to judge their suitability as targeting agents, thermodynamic analysis may still provide valuable information regarding their usefulness for specific applications.

Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: forensic fire debris analysis.

PubMed

Williams, Mary R; Sigman, Michael E; Lewis, Jennifer; Pitan, Kelly McHugh

2012-10-10

A bayesian soft classification method combined with target factor analysis (TFA) is described and tested for the analysis of fire debris data. The method relies on analysis of the average mass spectrum across the chromatographic profile (i.e., the total ion spectrum, TIS) from multiple samples taken from a single fire scene. A library of TIS from reference ignitable liquids with assigned ASTM classification is used as the target factors in TFA. The class-conditional distributions of correlations between the target and predicted factors for each ASTM class are represented by kernel functions and analyzed by bayesian decision theory. The soft classification approach assists in assessing the probability that ignitable liquid residue from a specific ASTM E1618 class, is present in a set of samples from a single fire scene, even in the presence of unspecified background contributions from pyrolysis products. The method is demonstrated with sample data sets and then tested on laboratory-scale burn data and large-scale field test burns. The overall performance achieved in laboratory and field test of the method is approximately 80% correct classification of fire debris samples. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Drug-therapy networks and the prediction of novel drug targets

PubMed Central

Spiro, Zoltan; Kovacs, Istvan A; Csermely, Peter

2008-01-01

A recent study in BMC Pharmacology presents a network of drugs and the therapies in which they are used. Network approaches open new ways of predicting novel drug targets and overcoming the cellular robustness that can prevent drugs from working. PMID:18710588

Target Highlights in CASP9: Experimental Target Structures for the Critical Assessment of Techniques for Protein Structure Prediction

PubMed Central

Kryshtafovych, Andriy; Moult, John; Bartual, Sergio G.; Bazan, J. Fernando; Berman, Helen; Casteel, Darren E.; Christodoulou, Evangelos; Everett, John K.; Hausmann, Jens; Heidebrecht, Tatjana; Hills, Tanya; Hui, Raymond; Hunt, John F.; Jayaraman, Seetharaman; Joachimiak, Andrzej; Kennedy, Michael A.; Kim, Choel; Lingel, Andreas; Michalska, Karolina; Montelione, Gaetano T.; Otero, José M.; Perrakis, Anastassis; Pizarro, Juan C.; van Raaij, Mark J.; Ramelot, Theresa A.; Rousseau, Francois; Tong, Liang; Wernimont, Amy K.; Young, Jasmine; Schwede, Torsten

2011-01-01

One goal of the CASP Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction is to identify the current state of the art in protein structure prediction and modeling. A fundamental principle of CASP is blind prediction on a set of relevant protein targets, i.e. the participating computational methods are tested on a common set of experimental target proteins, for which the experimental structures are not known at the time of modeling. Therefore, the CASP experiment would not have been possible without broad support of the experimental protein structural biology community. In this manuscript, several experimental groups discuss the structures of the proteins which they provided as prediction targets for CASP9, highlighting structural and functional peculiarities of these structures: the long tail fibre protein gp37 from bacteriophage T4, the cyclic GMP-dependent protein kinase Iβ (PKGIβ) dimerization/docking domain, the ectodomain of the JTB (Jumping Translocation Breakpoint) transmembrane receptor, Autotaxin (ATX) in complex with an inhibitor, the DNA-Binding J-Binding Protein 1 (JBP1) domain essential for biosynthesis and maintenance of DNA base-J (β-D-glucosyl-hydroxymethyluracil) in Trypanosoma and Leishmania, an so far uncharacterized 73 residue domain from Ruminococcus gnavus with a fold typical for PDZ-like domains, a domain from the Phycobilisome (PBS) core-membrane linker (LCM) phycobiliprotein ApcE from Synechocystis, the Heat shock protein 90 (Hsp90) activators PFC0360w and PFC0270w from Plasmodium falciparum, and 2-oxo-3-deoxygalactonate kinase from Klebsiella pneumoniae. PMID:22020785

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

PubMed Central

Bustos-Korts, Daniela; Malosetti, Marcos; Chapman, Scott; Biddulph, Ben; van Eeuwijk, Fred

2016-01-01

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

PubMed Central

Shi, Xiao-He; Hu, Le-Le; Kong, Xiangyin; Cai, Yu-Dong; Chou, Kuo-Chen

2010-01-01

Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging. PMID:20300175

Identification of novel microRNAs in Hevea brasiliensis and computational prediction of their targets

PubMed Central

2012-01-01

Background Plants respond to external stimuli through fine regulation of gene expression partially ensured by small RNAs. Of these, microRNAs (miRNAs) play a crucial role. They negatively regulate gene expression by targeting the cleavage or translational inhibition of target messenger RNAs (mRNAs). In Hevea brasiliensis, environmental and harvesting stresses are known to affect natural rubber production. This study set out to identify abiotic stress-related miRNAs in Hevea using next-generation sequencing and bioinformatic analysis. Results Deep sequencing of small RNAs was carried out on plantlets subjected to severe abiotic stress using the Solexa technique. By combining the LeARN pipeline, data from the Plant microRNA database (PMRD) and Hevea EST sequences, we identified 48 conserved miRNA families already characterized in other plant species, and 10 putatively novel miRNA families. The results showed the most abundant size for miRNAs to be 24 nucleotides, except for seven families. Several MIR genes produced both 20-22 nucleotides and 23-27 nucleotides. The two miRNA class sizes were detected for both conserved and putative novel miRNA families, suggesting their functional duality. The EST databases were scanned with conserved and novel miRNA sequences. MiRNA targets were computationally predicted and analysed. The predicted targets involved in "responses to stimuli" and to "antioxidant" and "transcription activities" are presented. Conclusions Deep sequencing of small RNAs combined with transcriptomic data is a powerful tool for identifying conserved and novel miRNAs when the complete genome is not yet available. Our study provided additional information for evolutionary studies and revealed potentially specific regulation of the control of redox status in Hevea. PMID:22330773

Hydrocode predictions of collisional outcomes: Effects of target size

NASA Technical Reports Server (NTRS)

Ryan, Eileen V.; Asphaug, Erik; Melosh, H. J.

1991-01-01

Traditionally, laboratory impact experiments, designed to simulate asteroid collisions, attempted to establish a predictive capability for collisional outcomes given a particular set of initial conditions. Unfortunately, laboratory experiments are restricted to using targets considerably smaller than the modelled objects. It is therefore necessary to develop some methodology for extrapolating the extensive experimental results to the size regime of interest. Results are reported obtained through the use of two dimensional hydrocode based on 2-D SALE and modified to include strength effects and the fragmentation equations. The hydrocode was tested by comparing its predictions for post-impact fragment size distributions to those observed in laboratory impact experiments.

Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets.

PubMed

Xu, Weijun; Lucke, Andrew J; Fairlie, David P

2015-04-01

Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile.

PubMed

van Laarhoven, Twan; Marchiori, Elena

2013-01-01

In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/.

Analysis of Free Modeling Predictions by RBO Aleph in CASP11

PubMed Central

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2015-01-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue–residue contact prediction by EPC-map and contact–guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. PMID:26492194

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less

Smooth Pursuit Eye Movement Deficits in Patients With Whiplash and Neck Pain are Modulated by Target Predictability.

PubMed

Janssen, Malou; Ischebeck, Britta K; de Vries, Jurryt; Kleinrensink, Gert-Jan; Frens, Maarten A; van der Geest, Jos N

2015-10-01

This is a cross-sectional study. The purpose of this study is to support and extend previous observations on oculomotor disturbances in patients with neck pain and whiplash-associated disorders (WADs) by systematically investigating the effect of static neck torsion on smooth pursuit in response to both predictably and unpredictably moving targets using video-oculography. Previous studies showed that in patients with neck complaints, for instance due to WAD, extreme static neck torsion deteriorates smooth pursuit eye movements in response to predictably moving targets compared with healthy controls. Eye movements in response to a smoothly moving target were recorded with video-oculography in a heterogeneous group of 55 patients with neck pain (including 11 patients with WAD) and 20 healthy controls. Smooth pursuit performance was determined while the trunk was fixed in 7 static rotations relative to the head (from 45° to the left to 45° to right), using both predictably and unpredictably moving stimuli. Patients had reduced smooth pursuit gains and smooth pursuit gain decreased due to neck torsion. Healthy controls showed higher gains for predictably moving targets compared with unpredictably moving targets, whereas patients with neck pain had similar gains in response to both types of target movements. In 11 patients with WAD, increased neck torsion decreased smooth pursuit performance, but only for predictably moving targets. Smooth pursuit of patients with neck pain is affected. The previously reported WAD-specific decline in smooth pursuit due to increased neck torsion seems to be modulated by the predictability of the movement of the target. The observed oculomotor disturbances in patients with WAD are therefore unlikely to be induced by impaired neck proprioception alone. 3.

Multiplex primer prediction software for divergent targets

PubMed Central

Gardner, Shea N.; Hiddessen, Amy L.; Williams, Peter L.; Hara, Christine; Wagner, Mark C.; Colston, Bill W.

2009-01-01

We describe a Multiplex Primer Prediction (MPP) algorithm to build multiplex compatible primer sets to amplify all members of large, diverse and unalignable sets of target sequences. The MPP algorithm is scalable to larger target sets than other available software, and it does not require a multiple sequence alignment. We applied it to questions in viral detection, and demonstrated that there are no universally conserved priming sequences among viruses and that it could require an unfeasibly large number of primers (∼3700 18-mers or ∼2000 10-mers) to generate amplicons from all sequenced viruses. We then designed primer sets separately for each viral family, and for several diverse species such as foot-and-mouth disease virus (FMDV), hemagglutinin (HA) and neuraminidase (NA) segments of influenza A virus, Norwalk virus, and HIV-1. We empirically demonstrated the application of the software with a multiplex set of 16 short (10 nt) primers designed to amplify the Poxviridae family to produce a specific amplicon from vaccinia virus. PMID:19759213

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes Multi

Predicting targets of compounds against neurological diseases using cheminformatic methodology

NASA Astrophysics Data System (ADS)

Nikolic, Katarina; Mavridis, Lazaros; Bautista-Aguilera, Oscar M.; Marco-Contelles, José; Stark, Holger; do Carmo Carreiras, Maria; Rossi, Ilaria; Massarelli, Paola; Agbaba, Danica; Ramsay, Rona R.; Mitchell, John B. O.

2015-02-01

Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand ( 71/MBA-VEG8).

RNAstructure: software for RNA secondary structure prediction and analysis.

PubMed

Reuter, Jessica S; Mathews, David H

2010-03-15

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing

2018-01-01

Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

PubMed

Chiddarwar, Rucha K; Rohrer, Sebastian G; Wolf, Antje; Tresch, Stefan; Wollenhaupt, Sabrina; Bender, Andreas

2017-01-01

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of

Can working memory predict target-to-target interval effects in the P300?

PubMed

Steiner, Genevieve Z; Barry, Robert J; Gonsalvez, Craig J

2013-09-01

It has been suggested that the P300 component of the ERP is an electrophysiological index of memory-updating processes associated with task-relevant stimuli. Component magnitude varies with the time separating target stimuli (target-to-target interval: TTI), with longer TTIs eliciting larger P300 amplitudes. According to the template-update perspective, TTI effects observable in the P300 reflect the updating of stimulus-templates in working memory (WM). The current study explored whether young adults' memory-task ability could predict TTI effects in P300. EEG activity was recorded from 50 university students (aged 18-25 years) while they completed an auditory equiprobable Go/NoGo task with manipulations of TTIs. Participants also completed a CogState® battery and were sorted according to their WM score. ERPs were analysed using a temporal PCA. Two P300 components, P3b and the Slow Wave, were found to linearly increase in amplitude to longer TTIs. This TTI effect differed between groups only for the P3b component: The high WM group showed a steeper increase in P3b amplitude with TTI than the low WM group. These results suggest that TTI effects in P300 are directly related to WM processes. © 2013.

Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

PubMed

Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

2009-02-01

Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.

Predictive saccade in the absence of smooth pursuit: interception of moving targets in the archer fish.

PubMed

Ben-Simon, Avi; Ben-Shahar, Ohad; Vasserman, Genadiy; Segev, Ronen

2012-12-15

Interception of fast-moving targets is a demanding task many animals solve. To handle it successfully, mammals employ both saccadic and smooth pursuit eye movements in order to confine the target to their area centralis. But how can non-mammalian vertebrates, which lack smooth pursuit, intercept moving targets? We studied this question by exploring eye movement strategies employed by archer fish, an animal that possesses an area centralis, lacks smooth pursuit eye movements, but can intercept moving targets by shooting jets of water at them. We tracked the gaze direction of fish during interception of moving targets and found that they employ saccadic eye movements based on prediction of target position when it is hit. The fish fixates on the target's initial position for ∼0.2 s from the onset of its motion, a time period used to predict whether a shot can be made before the projection of the target exits the area centralis. If the prediction indicates otherwise, the fish performs a saccade that overshoots the center of gaze beyond the present target projection on the retina, such that after the saccade the moving target remains inside the area centralis long enough to prepare and perform a shot. These results add to the growing body of knowledge on biological target tracking and may shed light on the mechanism underlying this behavior in other animals with no neural system for the generation of smooth pursuit eye movements.

Parallel analysis of RNA ends enhances global investigation of microRNAs and target RNAs of Brachypodium distachyon

PubMed Central

2013-01-01

Background The wild grass Brachypodium distachyon has emerged as a model system for temperate grasses and biofuel plants. However, the global analysis of miRNAs, molecules known to be key for eukaryotic gene regulation, has been limited in B. distachyon to studies examining a few samples or that rely on computational predictions. Similarly an in-depth global analysis of miRNA-mediated target cleavage using parallel analysis of RNA ends (PARE) data is lacking in B. distachyon. Results B. distachyon small RNAs were cloned and deeply sequenced from 17 libraries that represent different tissues and stresses. Using a computational pipeline, we identified 116 miRNAs including not only conserved miRNAs that have not been reported in B. distachyon, but also non-conserved miRNAs that were not found in other plants. To investigate miRNA-mediated cleavage function, four PARE libraries were constructed from key tissues and sequenced to a total depth of approximately 70 million sequences. The roughly 5 million distinct genome-matched sequences that resulted represent an extensive dataset for analyzing small RNA-guided cleavage events. Analysis of the PARE and miRNA data provided experimental evidence for miRNA-mediated cleavage of 264 sites in predicted miRNA targets. In addition, PARE analysis revealed that differentially expressed miRNAs in the same family guide specific target RNA cleavage in a correspondingly tissue-preferential manner. Conclusions B. distachyon miRNAs and target RNAs were experimentally identified and analyzed. Knowledge gained from this study should provide insights into the roles of miRNAs and the regulation of their targets in B. distachyon and related plants. PMID:24367943

Qweak Data Analysis for Target Modeling Using Computational Fluid Dynamics

NASA Astrophysics Data System (ADS)

Moore, Michael; Covrig, Silviu

2015-04-01

The 2.5 kW liquid hydrogen (LH2) target used in the Qweak parity violation experiment is the highest power LH2 target in the world and the first to be designed with Computational Fluid Dynamics (CFD) at Jefferson Lab. The Qweak experiment determined the weak charge of the proton by measuring the parity-violating elastic scattering asymmetry of longitudinally polarized electrons from unpolarized liquid hydrogen at small momentum transfer (Q2 = 0 . 025 GeV2). This target met the design goals of < 1 % luminosity reduction and < 5 % contribution to the total asymmetry width (the Qweak target achieved 2 % or 55 ppm). State of the art time dependent CFD simulations are being developed to improve the predictions of target noise on the time scale of the electron beam helicity period. These predictions will be bench-marked with the Qweak target data. This work is an essential ingredient in future designs of very high power low noise targets like MOLLER (5 kW, target noise asymmetry contribution < 25 ppm) and MESA (4.5 kW).

Computational Prediction of Neutralization Epitopes Targeted by Human Anti-V3 HIV Monoclonal Antibodies

PubMed Central

Shmelkov, Evgeny; Krachmarov, Chavdar; Grigoryan, Arsen V.; Pinter, Abraham; Statnikov, Alexander; Cardozo, Timothy

2014-01-01

The extreme diversity of HIV-1 strains presents a formidable challenge for HIV-1 vaccine design. Although antibodies (Abs) can neutralize HIV-1 and potentially protect against infection, antibodies that target the immunogenic viral surface protein gp120 have widely variable and poorly predictable cross-strain reactivity. Here, we developed a novel computational approach, the Method of Dynamic Epitopes, for identification of neutralization epitopes targeted by anti-HIV-1 monoclonal antibodies (mAbs). Our data demonstrate that this approach, based purely on calculated energetics and 3D structural information, accurately predicts the presence of neutralization epitopes targeted by V3-specific mAbs 2219 and 447-52D in any HIV-1 strain. The method was used to calculate the range of conservation of these specific epitopes across all circulating HIV-1 viruses. Accurately identifying an Ab-targeted neutralization epitope in a virus by computational means enables easy prediction of the breadth of reactivity of specific mAbs across the diversity of thousands of different circulating HIV-1 variants and facilitates rational design and selection of immunogens mimicking specific mAb-targeted epitopes in a multivalent HIV-1 vaccine. The defined epitopes can also be used for the purpose of epitope-specific analyses of breakthrough sequences recorded in vaccine clinical trials. Thus, our study is a prototype for a valuable tool for rational HIV-1 vaccine design. PMID:24587168

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

PubMed

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. http://datamining-iip.fudan.edu.cn/service/DrugE-Rank zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline

PubMed Central

Zhang, Jie; Li, Qingyang; Caselli, Richard J.; Thompson, Paul M.; Ye, Jieping; Wang, Yalin

2017-01-01

Alzheimer’s Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms. PMID:28943731

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline.

PubMed

Zhang, Jie; Li, Qingyang; Caselli, Richard J; Thompson, Paul M; Ye, Jieping; Wang, Yalin

2017-06-01

Alzheimer's Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms.

Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

DOE PAGES

Cucinotta, Francis A.; Cacao, Eliedonna

2017-05-12

Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cucinotta, Francis A.; Cacao, Eliedonna

Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

Analysis of free modeling predictions by RBO aleph in CASP11.

PubMed

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2016-09-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue-residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. Proteins 2016; 84(Suppl 1):87-104. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Cognitive versus Software-Assisted Registration: Development of a New Nomogram Predicting Prostate Cancer at MRI-Targeted Biopsies.

PubMed

Kaufmann, Sascha; Russo, Giorgio I; Thaiss, Wolfgang; Notohamiprodjo, Mike; Bamberg, Fabian; Bedke, Jens; Morgia, Giuseppe; Nikolaou, Konstantin; Stenzl, Arnulf; Kruck, Stephan

2018-04-03

Multiparametric magnetic resonance imaging (mpMRI) is gaining acceptance to guide targeted biopsy (TB) in prostate cancer (PC) diagnosis. We aimed to compare the detection rate of software-assisted fusion TB (SA-TB) versus cognitive fusion TB (COG-TB) for PC and to evaluate potential clinical features in detecting PC and clinically significant PC (csPC) at TB. This was a retrospective cohort study of patients with rising and/or persistently elevated prostate-specific antigen (PSA) undergoing mpMRI followed by either transperineal SA-TB or transrectal COG-TB. The analysis showed a matched-paired analysis between SA-TB versus COG-TB without differences in clinical or radiological characteristics. Differences among detection of PC/csPC among groups were analyzed. A multivariable logistic regression model predicting PC at TB was fitted. The model was evaluated using the receiver operating characteristic-derived area under the curve, goodness of fit test, and decision-curve analyses. One hundred ninety-one and 87 patients underwent SA-TB or COG-TB, respectively. The multivariate logistic analysis showed that SA-TB was associated with overall PC (odds ratio [OR], 5.70; P < .01) and PC at TB (OR, 3.00; P < .01) but not with overall csPC (P = .40) and csPC at TB (P = .40). A nomogram predicting PC at TB was constructed using the Prostate Imaging Reporting and Data System version 2.0, age, PSA density and biopsy technique, showing improved clinical risk prediction against a threshold probability of 10% with a c-index of 0.83. In patients with suspected PC, software-assisted biopsy detects most cancers and outperforms the cognitive approach in targeting magnetic resonance imaging-visible lesions. Furthermore, we introduced a prebiopsy nomogram for the probability of PC in TB. Copyright © 2018 Elsevier Inc. All rights reserved.

Predictive distractor context facilitates attentional selection of high, but not intermediate and low, salience targets.

PubMed

Töllner, Thomas; Conci, Markus; Müller, Hermann J

2015-03-01

It is well established that we can focally attend to a specific region in visual space without shifting our eyes, so as to extract action-relevant sensory information from covertly attended locations. The underlying mechanisms that determine how fast we engage our attentional spotlight in visual-search scenarios, however, remain controversial. One dominant view advocated by perceptual decision-making models holds that the times taken for focal-attentional selection are mediated by an internal template that biases perceptual coding and selection decisions exclusively through target-defining feature coding. This notion directly predicts that search times remain unaffected whether or not participants can anticipate the upcoming distractor context. Here we tested this hypothesis by employing an illusory-figure localization task that required participants to search for an invariant target amongst a variable distractor context, which gradually changed--either randomly or predictably--as a function of distractor-target similarity. We observed a graded decrease in internal focal-attentional selection times--correlated with external behavioral latencies--for distractor contexts of higher relative to lower similarity to the target. Critically, for low but not intermediate and high distractor-target similarity, these context-driven effects were cortically and behaviorally amplified when participants could reliably predict the type of distractors. This interactive pattern demonstrates that search guidance signals can integrate information about distractor, in addition to target, identities to optimize distractor-target competition for focal-attentional selection. © 2014 Wiley Periodicals, Inc.

A novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

A new approach to human microRNA target prediction using ensemble pruning and rotation forest.

PubMed

Mousavi, Reza; Eftekhari, Mahdi; Haghighi, Mehdi Ghezelbash

2015-12-01

MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the selected classifiers are trained based on the RTF method and then are combined using weighted majority voting. In addition to seeking a better subset of classifiers, the parameter of RTF is also optimized by GA. Findings of the present study confirm that the newly developed EP-RTF outperforms (in terms of classification accuracy, sensitivity, and specificity) the previously applied methods over four datasets in the field of human miRNA target. Diversity-error diagrams reveal that the proposed ensemble approach constructs individual classifiers which are more accurate and usually diverse than the other ensemble approaches. Given these experimental results, we highly recommend EP-RTF for improving the performance of miRNA target prediction.

Hyperspectral target detection analysis of a cluttered scene from a virtual airborne sensor platform using MuSES

NASA Astrophysics Data System (ADS)

Packard, Corey D.; Viola, Timothy S.; Klein, Mark D.

2017-10-01

The ability to predict spectral electro-optical (EO) signatures for various targets against realistic, cluttered backgrounds is paramount for rigorous signature evaluation. Knowledge of background and target signatures, including plumes, is essential for a variety of scientific and defense-related applications including contrast analysis, camouflage development, automatic target recognition (ATR) algorithm development and scene material classification. The capability to simulate any desired mission scenario with forecast or historical weather is a tremendous asset for defense agencies, serving as a complement to (or substitute for) target and background signature measurement campaigns. In this paper, a systematic process for the physical temperature and visible-through-infrared radiance prediction of several diverse targets in a cluttered natural environment scene is presented. The ability of a virtual airborne sensor platform to detect and differentiate targets from a cluttered background, from a variety of sensor perspectives and across numerous wavelengths in differing atmospheric conditions, is considered. The process described utilizes the thermal and radiance simulation software MuSES and provides a repeatable, accurate approach for analyzing wavelength-dependent background and target (including plume) signatures in multiple band-integrated wavebands (multispectral) or hyperspectrally. The engineering workflow required to combine 3D geometric descriptions, thermal material properties, natural weather boundary conditions, all modes of heat transfer and spectral surface properties is summarized. This procedure includes geometric scene creation, material and optical property attribution, and transient physical temperature prediction. Radiance renderings, based on ray-tracing and the Sandford-Robertson BRDF model, are coupled with MODTRAN for the inclusion of atmospheric effects. This virtual hyperspectral/multispectral radiance prediction methodology has been

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

PubMed

Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

2017-10-01

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrated analysis of drug-induced gene expression profiles predicts novel hERG inhibitors.

PubMed

Babcock, Joseph J; Du, Fang; Xu, Kaiping; Wheelan, Sarah J; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors

PubMed Central

Babcock, Joseph J.; Du, Fang; Xu, Kaiping; Wheelan, Sarah J.; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays. PMID:23936032

Getting NuSTAR on target: predicting mast motion

NASA Astrophysics Data System (ADS)

Forster, Karl; Madsen, Kristin K.; Miyasaka, Hiromasa; Craig, William W.; Harrison, Fiona A.; Rana, Vikram R.; Markwardt, Craig B.; Grefenstette, Brian W.

2016-07-01

The Nuclear Spectroscopic Telescope Array (NuSTAR) is the first focusing high energy (3-79 keV) X-ray observatory operating for four years from low Earth orbit. The X-ray detector arrays are located on the spacecraft bus with the optics modules mounted on a flexible mast of 10.14m length. The motion of the telescope optical axis on the detectors during each observation is measured by a laser metrology system and matches the pre-launch predictions of the thermal flexing of the mast as the spacecraft enters and exits the Earths shadow each orbit. However, an additional motion of the telescope field of view was discovered during observatory commissioning that is associated with the spacecraft attitude control system and an additional flexing of the mast correlated with the Solar aspect angle for the observation. We present the methodology developed to predict where any particular target coordinate will fall on the NuSTAR detectors based on the Solar aspect angle at the scheduled time of an observation. This may be applicable to future observatories that employ optics deployed on extendable masts. The automation of the prediction system has greatly improved observatory operations efficiency and the reliability of observation planning.

Getting NuSTAR on Target: Predicting Mast Motion

NASA Technical Reports Server (NTRS)

Forster, Karl; Madsen, Kristin K.; Miyasaka, Hiroshima; Craig, William W.; Harrison, Fiona A.; Rana, Vikram R.; Markwardt, Craig B.; Grenfenstette, Brian W.

2017-01-01

The Nuclear Spectroscopic Telescope Array (NuSTAR) is the first focusing high energy (3-79 keV) X-ray observatory operating for four years from low Earth orbit. The X-ray detector arrays are located on the spacecraft bus with the optics modules mounted on a flexible mast of 10.14m length. The motion of the telescope optical axis on the detectors during each observation is measured by a laser metrology system and matches the pre-launch predictions of the thermal flexing of the mast as the spacecraft enters and exits the Earths shadow each orbit. However, an additional motion of the telescope field of view was discovered during observatory commissioning that is associated with the spacecraft attitude control system and an additional flexing of the mast correlated with the Solar aspect angle for the observation. We present the methodology developed to predict where any particular target coordinate will fall on the NuSTAR detectors based on the Solar aspect angle at the scheduled time of an observation. This may be applicable to future observatories that employ optics deployed on extendable masts. The automation of the prediction system has greatly improved observatory operations efficiency and the reliability of observation planning.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

PubMed

Hao, Ming; Bryant, Stephen H; Wang, Yanli

2018-02-06

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

Integrating molecular dynamics and co-evolutionary analysis for reliable target prediction and deregulation of the allosteric inhibition of aspartokinase for amino acid production.

PubMed

Chen, Zhen; Rappert, Sugima; Sun, Jibin; Zeng, An-Ping

2011-07-20

Deregulation of allosteric inhibition of enzymes is a challenge for strain engineering and has been achieved so far primarily by random mutation and trial-and-error. In this work, we used aspartokinase, an important allosteric enzyme for industrial amino acids production, to demonstrate a predictive approach that combines protein dynamics and evolution for a rational reengineering of enzyme allostery. Molecular dynamic simulation of aspartokinase III (AK3) from Escherichia coli and statistical coupling analysis of protein sequences of the aspartokinase family allowed to identify a cluster of residues which are correlated during protein motion and coupled during the evolution. This cluster of residues forms an interconnected network mediating the allosteric regulation, including most of the previously reported positions mutated in feedback insensitive AK3 mutants. Beyond these mutation positions, we have successfully constructed another twelve targeted mutations of AK3 desensitized toward lysine inhibition. Six threonine-insensitive mutants of aspartokinase I-homoserine dehydrogenase I (AK1-HD1) were also created based on the predictions. The proposed approach can be widely applied for the deregulation of other allosteric enzymes. Copyright © 2011 Elsevier B.V. All rights reserved.

High-throughput chinmedomics-based prediction of effective components and targets from herbal medicine AS1350

PubMed Central

Liu, Qi; Zhang, Aihua; Wang, Liang; Yan, Guangli; Zhao, Hongwei; Sun, Hui; Zou, Shiyu; Han, Jinwei; Ma, Chung Wah; Kong, Ling; Zhou, Xiaohang; Nan, Yang; Wang, Xijun

2016-01-01

This work was designed to explore the effective components and targets of herbal medicine AS1350 and its effect on “Kidney-Yang Deficiency Syndrome” (KYDS) based on a chinmedomics strategy which is capable of directly discovering and predicting the effective components, and potential targets, of herbal medicine. Serum samples were analysed by UPLC-MS combined with pattern recognition analysis to identify the biomarkers related to the therapeutic effects. Interestingly, the effectiveness of AS1350 against KYDS was proved by the chinmedomics method and regulated the biomarkers and targeting of metabolic disorders. Some 48 marker metabolites associated with alpha-linolenic acid metabolism, fatty acid metabolism, sphingolipids metabolism, phospholipid metabolism, steroid hormone biosynthesis, and amino acid metabolism were identified. The correlation coefficient between the constituents in vivo and the changes of marker metabolites were calculated by PCMS software and the potential effective constituents of AS1350 were also confirmed. By using chinmedomics technology, the components in AS1350 protecting against KYDS by re-balancing metabolic disorders of fatty acid metabolism, lipid metabolism, steroid hormone biosynthesis, etc. were deduced. These data indicated that the phenotypic characterisations of AS1350 altering the metabolic signatures of KYDS were multi-component, multi-pathway, multi-target, and overall regulation in nature. PMID:27910928

Visuo-vestibular interaction: predicting the position of a visual target during passive body rotation.

PubMed

Mackrous, I; Simoneau, M

2011-11-10

Following body rotation, optimal updating of the position of a memorized target is attained when retinal error is perceived and corrective saccade is performed. Thus, it appears that these processes may enable the calibration of the vestibular system by facilitating the sharing of information between both reference frames. Here, it is assessed whether having sensory information regarding body rotation in the target reference frame could enhance an individual's learning rate to predict the position of an earth-fixed target. During rotation, participants had to respond when they felt their body midline had crossed the position of the target and received knowledge of result. During practice blocks, for two groups, visual cues were displayed in the same reference frame of the target, whereas a third group relied on vestibular information (vestibular-only group) to predict the location of the target. Participants, unaware of the role of the visual cues (visual cues group), learned to predict the location of the target and spatial error decreased from 16.2 to 2.0°, reflecting a learning rate of 34.08 trials (determined from fitting a falling exponential model). In contrast, the group aware of the role of the visual cues (explicit visual cues group) showed a faster learning rate (i.e. 2.66 trials) but similar final spatial error 2.9°. For the vestibular-only group, similar accuracy was achieved (final spatial error of 2.3°), but their learning rate was much slower (i.e. 43.29 trials). Transferring to the Post-test (no visual cues and no knowledge of result) increased the spatial error of the explicit visual cues group (9.5°), but it did not change the performance of the vestibular group (1.2°). Overall, these results imply that cognition assists the brain in processing the sensory information within the target reference frame. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree.

PubMed

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen-host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree

PubMed Central

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules. PMID:26649272

3D flexible alignment using 2D maximum common substructure: dependence of prediction accuracy on target-reference chemical similarity.

PubMed

Kawabata, Takeshi; Nakamura, Haruki

2014-07-28

A protein-bound conformation of a target molecule can be predicted by aligning the target molecule on the reference molecule obtained from the 3D structure of the compound-protein complex. This strategy is called "similarity-based docking". For this purpose, we develop the flexible alignment program fkcombu, which aligns the target molecule based on atomic correspondences with the reference molecule. The correspondences are obtained by the maximum common substructure (MCS) of 2D chemical structures, using our program kcombu. The prediction performance was evaluated using many target-reference pairs of superimposed ligand 3D structures on the same protein in the PDB, with different ranges of chemical similarity. The details of atomic correspondence largely affected the prediction success. We found that topologically constrained disconnected MCS (TD-MCS) with the simple element-based atomic classification provides the best prediction. The crashing potential energy with the receptor protein improved the performance. We also found that the RMSD between the predicted and correct target conformations significantly correlates with the chemical similarities between target-reference molecules. Generally speaking, if the reference and target compounds have more than 70% chemical similarity, then the average RMSD of 3D conformations is <2.0 Å. We compared the performance with a rigid-body molecular alignment program based on volume-overlap scores (ShaEP). Our MCS-based flexible alignment program performed better than the rigid-body alignment program, especially when the target and reference molecules were sufficiently similar.

In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

PubMed

Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

2017-07-01

Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prediction of intracellular exposure bridges the gap between target- and cell-based drug discovery

PubMed Central

Gordon, Laurie J.; Wayne, Gareth J.; Almqvist, Helena; Axelsson, Hanna; Seashore-Ludlow, Brinton; Treyer, Andrea; Lundbäck, Thomas; West, Andy; Hann, Michael M.; Artursson, Per

2017-01-01

Inadequate target exposure is a major cause of high attrition in drug discovery. Here, we show that a label-free method for quantifying the intracellular bioavailability (Fic) of drug molecules predicts drug access to intracellular targets and hence, pharmacological effect. We determined Fic in multiple cellular assays and cell types representing different targets from a number of therapeutic areas, including cancer, inflammation, and dementia. Both cytosolic targets and targets localized in subcellular compartments were investigated. Fic gives insights on membrane-permeable compounds in terms of cellular potency and intracellular target engagement, compared with biochemical potency measurements alone. Knowledge of the amount of drug that is locally available to bind intracellular targets provides a powerful tool for compound selection in early drug discovery. PMID:28701380

Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions.

PubMed

Kumar, Amit; Thotakura, Pragna Lakshmi; Tiwary, Basant Kumar; Krishna, Ramadas

2016-05-12

Fusobacterium nucleatum, a well studied bacterium in periodontal diseases, appendicitis, gingivitis, osteomyelitis and pregnancy complications has recently gained attention due to its association with colorectal cancer (CRC) progression. Treatment with berberine was shown to reverse F. nucleatum-induced CRC progression in mice by balancing the growth of opportunistic pathogens in tumor microenvironment. Intestinal microbiota imbalance and the infections caused by F. nucleatum might be regulated by therapeutic intervention. Hence, we aimed to predict drug target proteins in F. nucleatum, through subtractive genomics approach and host-pathogen protein-protein interactions (HP-PPIs). We also carried out enrichment analysis of host interacting partners to hypothesize the possible mechanisms involved in CRC progression due to F. nucleatum. In subtractive genomics approach, the essential, virulence and resistance related proteins were retrieved from RefSeq proteome of F. nucleatum by searching against Database of Essential Genes (DEG), Virulence Factor Database (VFDB) and Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT) tool respectively. A subsequent hierarchical screening to identify non-human homologous, metabolic pathway-independent/pathway-specific and druggable proteins resulted in eight pathway-independent and 27 pathway-specific druggable targets. Co-aggregation of F. nucleatum with host induces proinflammatory gene expression thereby potentiates tumorigenesis. Hence, proteins from IBDsite, a database for inflammatory bowel disease (IBD) research and those involved in colorectal adenocarcinoma as interpreted from The Cancer Genome Atlas (TCGA) were retrieved to predict drug targets based on HP-PPIs with F. nucleatum proteome. Prediction of HP-PPIs exhibited 186 interactions contributed by 103 host and 76 bacterial proteins. Bacterial interacting partners were accounted as putative targets. And enrichment analysis of host interacting partners showed statistically

Prediction of effective RNA interference targets and pathway-related genes in lepidopteran insects by RNA sequencing analysis.

PubMed

Guan, Ruo-Bing; Li, Hai-Chao; Miao, Xue-Xia

2018-06-01

When using RNA interference (RNAi) to study gene functions in Lepidoptera insects, we discovered that some genes could not be suppressed; instead, their expression levels could be up-regulated by double-stranded RNA (dsRNA). To predict which genes could be easily silenced, we treated the Asian corn borer (Ostrinia furnacalis) with dsGFP (green fluorescent protein) and dsMLP (muscle lim protein). A transcriptome sequence analysis was conducted using the cDNAs 6 h after treatment with dsRNA. The results indicated that 160 genes were up-regulated and 44 genes were down-regulated by the two dsRNAs. Then, 50 co-up-regulated, 25 co-down-regulated and 43 unaffected genes were selected to determine their RNAi responses. All the 25 down-regulated genes were knocked down by their corresponding dsRNA. However, several of the up-regulated and unaffected genes were up-regulated when treated with their corresponding dsRNAs instead of being knocked down. The genes up-regulated by the dsGFP treatment may be involved in insect immune responses or the RNAi pathway. When the immune-related genes were excluded, only seven genes were induced by dsGFP, including ago-2 and dicer-2. These results not only provide a reference for efficient RNAi target predications, but also provide some potential RNAi pathway-related genes for further study. © 2017 Institute of Zoology, Chinese Academy of Sciences.

Identification of tissue-specific targeting peptide

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Lee, Nam Kyung; Kang, Sang-Kee; Choi, Seung-Hoon; Kim, Daejin; Park, Kisoo; Choi, Kihang; Choi, Yun-Jaie; Jung, Dong Hyun

2012-11-01

Using phage display technique, we identified tissue-targeting peptide sets that recognize specific tissues (bone-marrow dendritic cell, kidney, liver, lung, spleen and visceral adipose tissue). In order to rapidly evaluate tissue-specific targeting peptides, we performed machine learning studies for predicting the tissue-specific targeting activity of peptides on the basis of peptide sequence information using four machine learning models and isolated the groups of peptides capable of mediating selective targeting to specific tissues. As a representative liver-specific targeting sequence, the peptide "DKNLQLH" was selected by the sequence similarity analysis. This peptide has a high degree of homology with protein ligands which can interact with corresponding membrane counterparts. We anticipate that our models will be applicable to the prediction of tissue-specific targeting peptides which can recognize the endothelial markers of target tissues.

Predictive encoding of moving target trajectory by neurons in the parabigeminal nucleus

PubMed Central

Ma, Rui; Cui, He; Lee, Sang-Hun; Anastasio, Thomas J.

2013-01-01

Intercepting momentarily invisible moving objects requires internally generated estimations of target trajectory. We demonstrate here that the parabigeminal nucleus (PBN) encodes such estimations, combining sensory representations of target location, extrapolated positions of briefly obscured targets, and eye position information. Cui and Malpeli (Cui H, Malpeli JG. J Neurophysiol 89: 3128–3142, 2003) reported that PBN activity for continuously visible tracked targets is determined by retinotopic target position. Here we show that when cats tracked moving, blinking targets the relationship between activity and target position was similar for ON and OFF phases (400 ms for each phase). The dynamic range of activity evoked by virtual targets was 94% of that of real targets for the first 200 ms after target offset and 64% for the next 200 ms. Activity peaked at about the same best target position for both real and virtual targets. PBN encoding of target position takes into account changes in eye position resulting from saccades, even without visual feedback. Since PBN response fields are retinotopically organized, our results suggest that activity foci associated with real and virtual targets at a given target position lie in the same physical location in the PBN, i.e., a retinotopic as well as a rate encoding of virtual-target position. We also confirm that PBN activity is specific to the intended target of a saccade and is predictive of which target will be chosen if two are offered. A Bayesian predictor-corrector model is presented that conceptually explains the differences in the dynamic ranges of PBN neuronal activity evoked during tracking of real and virtual targets. PMID:23365185

Use of eQTL Analysis for the Discovery of Target Genes Identified by GWAS

DTIC Science & Technology

2013-04-01

the biologic pathways affected by these inherited factors, and ultimately to identify targets for disease prediction, risk stratification and...quality using an Agilent chip technology. Cases having a RIN number of 7.0 or greater were considered good quality. Once completed, the optimum set of...AD_________________ Award Number: W81XWH-11-1-0261 TITLE: Use of eQTL Analysis for the Discovery of

Computational prediction of the Crc regulon identifies genus-wide and species-specific targets of catabolite repression control in Pseudomonas bacteria.

PubMed

Browne, Patrick; Barret, Matthieu; O'Gara, Fergal; Morrissey, John P

2010-11-25

Catabolite repression control (CRC) is an important global control system in Pseudomonas that fine tunes metabolism in order optimise growth and metabolism in a range of different environments. The mechanism of CRC in Pseudomonas spp. centres on the binding of a protein, Crc, to an A-rich motif on the 5' end of an mRNA resulting in translational down-regulation of target genes. Despite the identification of several Crc targets in Pseudomonas spp. the Crc regulon has remained largely unexplored. In order to predict direct targets of Crc, we used a bioinformatics approach based on detection of A-rich motifs near the initiation of translation of all protein-encoding genes in twelve fully sequenced Pseudomonas genomes. As expected, our data predict that genes related to the utilisation of less preferred nutrients, such as some carbohydrates, nitrogen sources and aromatic carbon compounds are targets of Crc. A general trend in this analysis is that the regulation of transporters is conserved across species whereas regulation of specific enzymatic steps or transcriptional activators are often conserved only within a species. Interestingly, some nucleoid associated proteins (NAPs) such as HU and IHF are predicted to be regulated by Crc. This finding indicates a possible role of Crc in indirect control over a subset of genes that depend on the DNA bending properties of NAPs for expression or repression. Finally, some virulence traits such as alginate and rhamnolipid production also appear to be regulated by Crc, which links nutritional status cues with the regulation of virulence traits. Catabolite repression control regulates a broad spectrum of genes in Pseudomonas. Some targets are genus-wide and are typically related to central metabolism, whereas other targets are species-specific, or even unique to particular strains. Further study of these novel targets will enhance our understanding of how Pseudomonas bacteria integrate nutritional status cues with the regulation

Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.

PubMed

Steimer, Andreas; Müller, Michael; Schindler, Kaspar

2017-05-01

During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

PubMed

Olayan, Rawan S; Ashoor, Haitham; Bajic, Vladimir B

2018-04-01

Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs. The data and code are provided at https://bitbucket.org/RSO24/ddr/. vladimir.bajic@kaust.edu.sa. Supplementary data are available at Bioinformatics online.

Sensor image prediction techniques

NASA Astrophysics Data System (ADS)

Stenger, A. J.; Stone, W. R.; Berry, L.; Murray, T. J.

1981-02-01

The preparation of prediction imagery is a complex, costly, and time consuming process. Image prediction systems which produce a detailed replica of the image area require the extensive Defense Mapping Agency data base. The purpose of this study was to analyze the use of image predictions in order to determine whether a reduced set of more compact image features contains enough information to produce acceptable navigator performance. A job analysis of the navigator's mission tasks was performed. It showed that the cognitive and perceptual tasks he performs during navigation are identical to those performed for the targeting mission function. In addition, the results of the analysis of his performance when using a particular sensor can be extended to the analysis of this mission tasks using any sensor. An experimental approach was used to determine the relationship between navigator performance and the type of amount of information in the prediction image. A number of subjects were given image predictions containing varying levels of scene detail and different image features, and then asked to identify the predicted targets in corresponding dynamic flight sequences over scenes of cultural, terrain, and mixed (both cultural and terrain) content.

[Anti-tumor target prediction and activity verification of Ganoderma lucidum triterpenoids].

PubMed

Du, Guo-Hua; Wang, Hong-Xu; Yan, Zheng; Liu, Li-Ying; Chen, Ruo-Yun

2017-02-01

It has reported that Ganoderma lucidum triterpenoids had anti-tumor activity. However, the anti-tumor target is still unclear. The present study was designed to investigate the anti-tumor activity of G. lucidum triterpenoids on different tumor cells, and predict their potential targets by virtual screening. In this experiment, molecular docking was used to simulate the interactions of 26 triterpenoids isolated from G. lucidum and 11 target proteins by LibDock module of Discovery Studio2016 software, then the anti-tumor targets of triterpenoids were predicted. In addition, the in vitro anti-tumor effects of triterpenoids were evaluated by MTT assay by determining the inhibition of proliferation in 5 tumor cell lines. The docking results showed that the poses were greater than five, and Libdock Scores higher than 100, which can be used to determine whether compounds were activity. Eight triterpenoids might have anti-tumor activity as a result of good docking, five of which had multiple targets. MTT experiments demonstrated that the ganoderic acid Y had a certain inhibitory activity on lung cancer cell H460, with IC₅₀ of 22.4 μmol•L ⁻¹, followed by 7-oxo-ganoderic acid Z2, with IC₅₀ of 43.1 μmol•L ⁻¹. However, the other triterpenoids had no anti-tumor activity in the detected tumor cell lines. Taking together, molecular docking approach established here can be used for preliminary screening of anti-tumor activity of G.lucidum ingredients. Through this screening method, combined with the MTT assay, we can conclude that ganoderic acid Y had antitumor activity, especially anti-lung cancer, and 7-oxo-ganoderic acid Z2 as well as ganoderon B, to a certain extent, had anti-tumor activity. These findings can provide basis for the development of anti-tumor drugs. However, the anti-tumor mechanisms need to be further studied. Copyright© by the Chinese Pharmaceutical Association.

Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma

NASA Astrophysics Data System (ADS)

Azevedo, Hátylas; Moreira-Filho, Carlos Alberto

2015-11-01

Biological networks display high robustness against random failures but are vulnerable to targeted attacks on central nodes. Thus, network topology analysis represents a powerful tool for investigating network susceptibility against targeted node removal. Here, we built protein interaction networks associated with chemoresistance to temozolomide, an alkylating agent used in glioma therapy, and analyzed their modular structure and robustness against intentional attack. These networks showed functional modules related to DNA repair, immunity, apoptosis, cell stress, proliferation and migration. Subsequently, network vulnerability was assessed by means of centrality-based attacks based on the removal of node fractions in descending orders of degree, betweenness, or the product of degree and betweenness. This analysis revealed that removing nodes with high degree and high betweenness was more effective in altering networks’ robustness parameters, suggesting that their corresponding proteins may be particularly relevant to target temozolomide resistance. In silico data was used for validation and confirmed that central nodes are more relevant for altering proliferation rates in temozolomide-resistant glioma cell lines and for predicting survival in glioma patients. Altogether, these results demonstrate how the analysis of network vulnerability to topological attack facilitates target prioritization for overcoming cancer chemoresistance.

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

PubMed

LaBute, Montiago X; Zhang, Xiaohua; Lenderman, Jason; Bennion, Brian J; Wong, Sergio E; Lightstone, Felice C

2014-01-01

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number

Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10.

PubMed

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V; Craig, Timothy K; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C; van Raaij, Mark J; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-02-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, more than 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this article, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict transmembrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin (IL)-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fiber protein gene product 17 from bacteriophage T7; the bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally, an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. Copyright © 2013 The Authors. Wiley Periodicals, Inc.

Challenging the state-of-the-art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10

PubMed Central

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J. Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V.; Craig, Timothy K.; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C.; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-01-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, over 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this paper, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict trans-membrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin IL-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fibre protein gp17 from bacteriophage T7; the Bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. PMID:24318984

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

PubMed

Durán, Claudio; Daminelli, Simone; Thomas, Josephine M; Haupt, V Joachim; Schroeder, Michael; Cannistraci, Carlo Vittorio

2017-04-26

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering. © The Author 2017. Published by Oxford University Press.

Identification of Histone Deacetylase (HDAC) as a drug target against MRSA via interolog method of protein-protein interaction prediction.

PubMed

Uddin, Reaz; Tariq, Syeda Sumayya; Azam, Syed Sikander; Wadood, Abdul; Moin, Syed Tarique

2017-08-30

Patently, Protein-Protein Interactions (PPIs) lie at the core of significant biological functions and make the foundation of host-pathogen relationships. Hence, the current study is aimed to use computational biology techniques to predict host-pathogen Protein-Protein Interactions (HP-PPIs) between MRSA and Humans as potential drug targets ultimately proposing new possible inhibitors against them. As a matter of fact this study is based on the Interolog method which implies that homologous proteins retain their ability to interact. A distant homolog approach based on Interolog method was employed to speculate MRSA protein homologs in Humans using PSI-BLAST. In addition the protein interaction partners of these homologs as listed in Database of Interacting Proteins (DIP) were predicted to interact with MRSA as well. Moreover, a direct approach using BLAST was also applied so as to attain further confidence in the strategy. Consequently, the common HP-PPIs predicted by both approaches are suggested as potential drug targets (22%) whereas, the unique HP-PPIs estimated only through distant homolog approach are presented as novel drug targets (12%). Furthermore, the most repeated entry in our results was found to be MRSA Histone Deacetylase (HDAC) which was then modeled using SWISS-MODEL. Eventually, small molecules from ZINC, selected randomly, were docked against HDAC using Auto Dock and are suggested as potential binders (inhibitors) based on their energetic profiles. Thus the current study provides basis for further in-depth analysis of such data which not only include MRSA but other deadly pathogens as well. Copyright © 2017 Elsevier B.V. All rights reserved.

Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

PubMed

Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

2016-01-01

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

Methylation of WNT target genes AXIN2 and DKK1 as robust biomarkers for recurrence prediction in stage II colon cancer

PubMed Central

Kandimalla, R; Linnekamp, J F; van Hooff, S; Castells, A; Llor, X; Andreu, M; Jover, R; Goel, A; Medema, J P

2017-01-01

Stage II colon cancer (CC) still remains a clinical challenge with patient stratification for adjuvant therapy (AT) largely relying on clinical parameters. Prognostic biomarkers are urgently needed for better stratification. Previously, we have shown that WNT target genes AXIN2, DKK1, APCDD1, ASCL2 and LGR5 are silenced by DNA methylation and could serve as prognostic markers in stage II CC patients using methylation-specific PCR. Here, we have extended our discovery cohort AMC90-AJCC-II (N=65) and methylation was analyzed by quantitative pyrosequencing. Subsequently, we validated the results in an independent EPICOLON1 CC cohort (N=79). Methylation of WNT target genes is negatively correlated to mRNA expression. A combination of AXIN2 and DKK1 methylation significantly predicted recurrences in univariate (area under the curve (AUC)=0.83, confidence interval (CI): 0.72–0.94, P<0.0001) analysis in stage II microsatellite stable (MSS) CC patients. This two marker combination showed an AUC of 0.80 (CI: 0.68–0.91, P<0.0001) in the EPICOLON1 validation cohort. Multivariate analysis in the Academic Medical Center (AMC) cohort revealed that both WNT target gene methylation and consensus molecular subtype 4 (CMS4) are significantly associated with poor recurrence-free survival (hazard ratio (HR)methylation: 3.84, 95% CI: 1.14–12.43; HRCMS4: 3.73, 95% CI: 1.22–11.48). CMS4 subtype tumors with WNT target methylation showed worse prognosis. Combining WNT target gene methylation and CMS4 subtype lead to an AUC of 0.89 (0.791–0.982, P<0.0001) for recurrence prediction. Notably, we observed that methylation of DKK1 is high in BRAF mutant and CIMP (CpG island methylator phenotype)-positive cancers, whereas AXIN2 methylation appears to be associated with CMS4. Methylation of AXIN2 and DKK1 were found to be robust markers for recurrence prediction in stage II MSS CC patients. Further validation of these findings in a randomized and prospective manner could pave a way to

Targeted therapies in cancer - challenges and chances offered by newly developed techniques for protein analysis in clinical tissues

PubMed Central

Malinowsky, K; Wolff, C; Gündisch, S; Berg, D; Becker, KF

2011-01-01

In recent years, new anticancer therapies have accompanied the classical approaches of surgery and radio- and chemotherapy. These new forms of treatment aim to inhibit specific molecular targets namely altered or deregulated proteins, which offer the possibility of individualized therapies. The specificity and efficiency of these new approaches, however, bring about a number of challenges. First of all, it is essential to specifically identify and quantify protein targets in tumor tissues for the reasonable use of such targeted therapies. Additionally, it has become even more obvious in recent years that the presence of a target protein is not always sufficient to predict the outcome of targeted therapies. The deregulation of downstream signaling molecules might also play an important role in the success of such therapeutic approaches. For these reasons, the analysis of tumor-specific protein expression profiles prior to therapy has been suggested as the most effective way to predict possible therapeutic results. To further elucidate signaling networks underlying cancer development and to identify new targets, it is necessary to implement tools that allow the rapid, precise, inexpensive and simultaneous analysis of many network components while requiring only a small amount of clinical material. Reverse phase protein microarray (RPPA) is a promising technology that meets these requirements while enabling the quantitative measurement of proteins. Together with recently developed protocols for the extraction of proteins from formalin-fixed, paraffin-embedded (FFPE) tissues, RPPA may provide the means to quantify therapeutic targets and diagnostic markers in the near future and reliably screen for new protein targets. With the possibility to quantitatively analyze DNA, RNA and protein from a single FFPE tissue sample, the methods are available for integrated patient profiling at all levels of gene expression, thus allowing optimal patient stratification for

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs.

PubMed

Le, Duc-Hau; Verbeke, Lieven; Son, Le Hoang; Chu, Dinh-Toi; Pham, Van-Huy

2017-11-14

MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable

Bioinformatic identification and expression analysis of banana microRNAs and their targets.

PubMed

Chai, Juan; Feng, Renjun; Shi, Hourui; Ren, Mengyun; Zhang, Yindong; Wang, Jingyi

2015-01-01

MicroRNAs (miRNAs) represent a class of endogenous non-coding small RNAs that play important roles in multiple biological processes by degrading targeted mRNAs or repressing mRNA translation. Thousands of miRNAs have been identified in many plant species, whereas only a limited number of miRNAs have been predicted in M. acuminata (A genome) and M. balbisiana (B genome). Here, previously known plant miRNAs were BLASTed against the Expressed Sequence Tag (EST) and Genomic Survey Sequence (GSS), a database of banana genes. A total of 32 potential miRNAs belonging to 13 miRNAs families were detected using a range of filtering criteria. 244 miRNA:target pairs were subsequently predicted, most of which encode transcription factors or enzymes that participate in the regulation of development, growth, metabolism, and other physiological processes. In order to validate the predicted miRNAs and the mutual relationship between miRNAs and their target genes, qRT-PCR was applied to detect the tissue-specific expression levels of 12 putative miRNAs and 6 target genes in roots, leaves, flowers, and fruits. This study provides some important information about banana pre-miRNAs, mature miRNAs, and miRNA target genes and these findings can be applied to future research of miRNA functions.

Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure.

PubMed

Chen, Langdong; Cao, Yan; Zhang, Hai; Lv, Diya; Zhao, Yahong; Liu, Yanjun; Ye, Guan; Chai, Yifeng

2018-01-31

Yangxinshi tablet (YXST) is an effective treatment for heart failure and myocardial infarction; it consists of 13 herbal medicines formulated according to traditional Chinese Medicine (TCM) practices. It has been used for the treatment of cardiovascular disease for many years in China. In this study, a network pharmacology-based strategy was used to elucidate the mechanism of action of YXST for the treatment of heart failure. Cardiovascular disease-related protein target and compound databases were constructed for YXST. A molecular docking platform was used to predict the protein targets of YXST. The affinity between proteins and ingredients was determined using surface plasmon resonance (SPR) assays. The action modes between targets and representative ingredients were calculated using Glide docking, and the related pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A protein target database containing 924 proteins was constructed; 179 compounds in YXST were identified, and 48 compounds with high relevance to the proteins were defined as representative ingredients. Thirty-four protein targets of the 48 representative ingredients were analyzed and classified into two categories: immune and cardiovascular systems. The SPR assay and molecular docking partly validated the interplay between protein targets and representative ingredients. Moreover, 28 pathways related to heart failure were identified, which provided directions for further research on YXST. This study demonstrated that the cardiovascular protective effect of YXST mainly involved the immune and cardiovascular systems. Through the research strategy based on network pharmacology, we analysis the complex system of YXST and found 48 representative compounds, 34 proteins and 28 related pathways of YXST, which could help us understand the underlying mechanism of YSXT's anti-heart failure effect. The network-based investigation could help researchers simplify the complex

Architecture-Based Self-Adaptation for Moving Target Defense

DTIC Science & Technology

2014-08-01

using stochastic multiplayer games to verify the the behavior of a variety of MTD scenarios, from uninformed to predictive-reactive. This work is... multiplayer games to verify the the behavior of a variety of MTD scenarios, from uninformed to predictive-reactive. This work is applied in the context...for Moving Target . . . . . . . . . . . . . . 28 5 Multiplayer Games for Moving Target Defense 31 5.1 Stochastic Game Analysis for Proactive Self

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

NASA Astrophysics Data System (ADS)

Greulich, Philip; Doležal, Jakub; Scott, Matthew; Evans, Martin R.; Allen, Rosalind J.

2017-12-01

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance—yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (‘low-affinity antibiotic’) or, in contrast, irreversible transport and/or high affinity ribosome binding (‘high-affinity antibiotic’). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance.

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

PubMed Central

Greulich, Philip; Doležal, Jakub; Scott, Matthew; Evans, Martin R; Allen, Rosalind J

2017-01-01

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance—yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (‘low-affinity antibiotic’) or, in contrast, irreversible transport and/or high affinity ribosome binding (‘high-affinity antibiotic’). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance. PMID:28714461

Genome-wide Expression Profiling, In Vivo DNA Binding Analysis, and Probabilistic Motif Prediction Reveal Novel Abf1 Target Genes during Fermentation, Respiration, and Sporulation in Yeast

PubMed Central

Schlecht, Ulrich; Erb, Ionas; Demougin, Philippe; Robine, Nicolas; Borde, Valérie; van Nimwegen, Erik; Nicolas, Alain

2008-01-01

The autonomously replicating sequence binding factor 1 (Abf1) was initially identified as an essential DNA replication factor and later shown to be a component of the regulatory network controlling mitotic and meiotic cell cycle progression in budding yeast. The protein is thought to exert its functions via specific interaction with its target site as part of distinct protein complexes, but its roles during mitotic growth and meiotic development are only partially understood. Here, we report a comprehensive approach aiming at the identification of direct Abf1-target genes expressed during fermentation, respiration, and sporulation. Computational prediction of the protein's target sites was integrated with a genome-wide DNA binding assay in growing and sporulating cells. The resulting data were combined with the output of expression profiling studies using wild-type versus temperature-sensitive alleles. This work identified 434 protein-coding loci as being transcriptionally dependent on Abf1. More than 60% of their putative promoter regions contained a computationally predicted Abf1 binding site and/or were bound by Abf1 in vivo, identifying them as direct targets. The present study revealed numerous loci previously unknown to be under Abf1 control, and it yielded evidence for the protein's variable DNA binding pattern during mitotic growth and meiotic development. PMID:18305101

Factor Analysis of Therapist-Identified Treatment Targets in Community-Based Children's Mental Health.

PubMed

Love, Allison R; Okado, Izumi; Orimoto, Trina E; Mueller, Charles W

2018-01-01

The present study used exploratory and confirmatory factor analyses to identify underlying latent factors affecting variation in community therapists' endorsement of treatment targets. As part of a statewide practice management program, therapist completed monthly reports of treatment targets (up to 10 per month) for a sample of youth (n = 790) receiving intensive in-home therapy. Nearly 75 % of youth were diagnosed with multiple co-occurring disorders. Five factors emerged: Disinhibition, Societal Rules Evasion, Social Engagement Deficits, Emotional Distress, and Management of Biodevelopmental Outcomes. Using logistic regression, primary diagnosis predicted therapist selection of Disinhibition and Emotional Distress targets. Client age predicted endorsement of Societal Rules Evasion targets. Practice-to-research implications are discussed.

RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.

PubMed

Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

2018-01-01

Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities

PubMed Central

Cramer, Richard D.

2015-01-01

The possible applicability of the new template CoMFA methodology to the prediction of unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding affinities were used as training and/or prediction sets, making these 3D-QSAR models the most structurally diverse and among the largest ever. For six of the targets, X-ray crystallographic structures provided the aligned templates required as input (BACE, cdk1, chk2, carbonic anhydrase-II, factor Xa, PTP1B). For all targets including the other six (hERG, cyp3A4 binding, endocrine receptor, COX2, D2, and GABAa), six modeling protocols applied to only three familiar ligands provided six alternate sets of aligned templates. The statistical qualities of the six or seven models thus resulting for each individual target were remarkably similar. Also, perhaps unexpectedly, the standard deviations of the errors of cross-validation predictions accompanying model derivations were indistinguishable from the standard deviations of the errors of truly prospective predictions. These standard deviations of prediction ranged from 0.70 to 1.14 log units and averaged 0.89 (8x in concentration units) over the twelve targets, representing an average reduction of almost 50% in uncertainty, compared to the null hypothesis of “predicting” an unknown affinity to be the average of known affinities. These errors of prediction are similar to those from Tanimoto coefficients of fragment occurrence frequencies, the predominant approach to side effect prediction, which template CoMFA can augment by identifying additional active structural classes, by improving Tanimoto-only predictions, by yielding quantitative predictions of potency, and by providing interpretable guidance for avoiding or enhancing any specific target response. PMID:26065424

Structural and sequencing analysis of local target DNA recognition by MLV integrase.

PubMed

Aiyer, Sriram; Rossi, Paolo; Malani, Nirav; Schneider, William M; Chandar, Ashwin; Bushman, Frederic D; Montelione, Gaetano T; Roth, Monica J

2015-06-23

Target-site selection by retroviral integrase (IN) proteins profoundly affects viral pathogenesis. We describe the solution nuclear magnetic resonance structure of the Moloney murine leukemia virus IN (M-MLV) C-terminal domain (CTD) and a structural homology model of the catalytic core domain (CCD). In solution, the isolated MLV IN CTD adopts an SH3 domain fold flanked by a C-terminal unstructured tail. We generated a concordant MLV IN CCD structural model using SWISS-MODEL, MMM-tree and I-TASSER. Using the X-ray crystal structure of the prototype foamy virus IN target capture complex together with our MLV domain structures, residues within the CCD α2 helical region and the CTD β1-β2 loop were predicted to bind target DNA. The role of these residues was analyzed in vivo through point mutants and motif interchanges. Viable viruses with substitutions at the IN CCD α2 helical region and the CTD β1-β2 loop were tested for effects on integration target site selection. Next-generation sequencing and analysis of integration target sequences indicate that the CCD α2 helical region, in particular P187, interacts with the sequences distal to the scissile bonds whereas the CTD β1-β2 loop binds to residues proximal to it. These findings validate our structural model and disclose IN-DNA interactions relevant to target site selection. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Prediction Analysis for Measles Epidemics

NASA Astrophysics Data System (ADS)

Sumi, Ayako; Ohtomo, Norio; Tanaka, Yukio; Sawamura, Sadashi; Olsen, Lars Folke; Kobayashi, Nobumichi

2003-12-01

A newly devised procedure of prediction analysis, which is a linearized version of the nonlinear least squares method combined with the maximum entropy spectral analysis method, was proposed. This method was applied to time series data of measles case notification in several communities in the UK, USA and Denmark. The dominant spectral lines observed in each power spectral density (PSD) can be safely assigned as fundamental periods. The optimum least squares fitting (LSF) curve calculated using these fundamental periods can essentially reproduce the underlying variation of the measles data. An extension of the LSF curve can be used to predict measles case notification quantitatively. Some discussions including a predictability of chaotic time series are presented.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols.

PubMed

Bohari, Mohammed H; Sastry, G Narahari

2012-09-01

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

Electro-optic analysis of the influence of target geometry on electromagnetic pulses generated by petawatt laser-matter interactions

NASA Astrophysics Data System (ADS)

Robinson, Timothy; Giltrap, Samuel; Eardley, Samuel; Consoli, Fabrizio; De Angelis, Riccardo; Ingenito, Francesco; Stuart, Nicholas; Verona, Claudio; Smith, Roland A.

2018-01-01

We present an analysis of strong laser-driven electromagnetic pulses using novel electro-optic diagnostic techniques. A range of targets were considered, including thin plastic foils (20-550 nm) and mass-limited, optically-levitated micro-targets. Results from foils indicate a dependence of EMP on target thickness, with larger peak electric fields observed with thinner targets. Spectral analysis suggests high repeatability between shots, with identified spectral features consistently detected with <1 MHz standard deviations of the peak position. This deviation is reduced for shots taken on the same day, suggesting that local conditions, such as movement of metal objects within the target chamber, are more likely to lead to minor spectral modifications, highlighting the role of the local environment in determining the details of EMP production. Levitated targets are electrically isolated from their environment, hence these targets should be unable to draw a neutralization current from the earth following ejection of hot electrons from the plasma, in contrast to predictions for pin-mounted foils in the Poyé EMP generation model. With levitated targets, no EMP was measurable above the noise threshold of any diagnostic, despite observation of protons accelerated to >30 MeV energies, suggesting the discharge current contribution to EMP is dominant.

Predictability Experiments With the Navy Operational Global Atmospheric Prediction System

NASA Astrophysics Data System (ADS)

Reynolds, C. A.; Gelaro, R.; Rosmond, T. E.

2003-12-01

There are several areas of research in numerical weather prediction and atmospheric predictability, such as targeted observations and ensemble perturbation generation, where it is desirable to combine information about the uncertainty of the initial state with information about potential rapid perturbation growth. Singular vectors (SVs) provide a framework to accomplish this task in a mathematically rigorous and computationally feasible manner. In this study, SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS). The analysis error variance information produced by the NRL Atmospheric Variational Data Assimilation System is used as the initial-time SV norm. These VAR SVs are compared to SVs for which total energy is both the initial and final time norms (TE SVs). The incorporation of analysis error variance information has a significant impact on the structure and location of the SVs. This in turn has a significant impact on targeted observing applications. The utility and implications of such experiments in assessing the analysis error variance estimates will be explored. Computing support has been provided by the Department of Defense High Performance Computing Center at the Naval Oceanographic Office Major Shared Resource Center at Stennis, Mississippi.

Ewing's Sarcoma: An Analysis of miRNA Expression Profiles and Target Genes in Paraffin-Embedded Primary Tumor Tissue.

PubMed

Parafioriti, Antonina; Bason, Caterina; Armiraglio, Elisabetta; Calciano, Lucia; Daolio, Primo Andrea; Berardocco, Martina; Di Bernardo, Andrea; Colosimo, Alessia; Luksch, Roberto; Berardi, Anna C

2016-04-30

The molecular mechanism responsible for Ewing's Sarcoma (ES) remains largely unknown. MicroRNAs (miRNAs), a class of small non-coding RNAs able to regulate gene expression, are deregulated in tumors and may serve as a tool for diagnosis and prediction. However, the status of miRNAs in ES has not yet been thoroughly investigated. This study compared global miRNAs expression in paraffin-embedded tumor tissue samples from 20 ES patients, affected by primary untreated tumors, with miRNAs expressed in normal human mesenchymal stromal cells (MSCs) by microarray analysis. A miRTarBase database was used to identify the predicted target genes for differentially expressed miRNAs. The miRNAs microarray analysis revealed distinct patterns of miRNAs expression between ES samples and normal MSCs. 58 of the 954 analyzed miRNAs were significantly differentially expressed in ES samples compared to MSCs. Moreover, the qRT-PCR analysis carried out on three selected miRNAs showed that miR-181b, miR-1915 and miR-1275 were significantly aberrantly regulated, confirming the microarray results. Bio-database analysis identified BCL-2 as a bona fide target gene of the miR-21, miR-181a, miR-181b, miR-29a, miR-29b, miR-497, miR-195, miR-let-7a, miR-34a and miR-1915. Using paraffin-embedded tissues from ES patients, this study has identified several potential target miRNAs and one gene that might be considered a novel critical biomarker for ES pathogenesis.

Bioinformatic Identification and Expression Analysis of Banana MicroRNAs and Their Targets

PubMed Central

Shi, Hourui; Ren, Mengyun; Zhang, Yindong; Wang, Jingyi

2015-01-01

MicroRNAs (miRNAs) represent a class of endogenous non-coding small RNAs that play important roles in multiple biological processes by degrading targeted mRNAs or repressing mRNA translation. Thousands of miRNAs have been identified in many plant species, whereas only a limited number of miRNAs have been predicted in M. acuminata (A genome) and M. balbisiana (B genome). Here, previously known plant miRNAs were BLASTed against the Expressed Sequence Tag (EST) and Genomic Survey Sequence (GSS), a database of banana genes. A total of 32 potential miRNAs belonging to 13 miRNAs families were detected using a range of filtering criteria. 244 miRNA:target pairs were subsequently predicted, most of which encode transcription factors or enzymes that participate in the regulation of development, growth, metabolism, and other physiological processes. In order to validate the predicted miRNAs and the mutual relationship between miRNAs and their target genes, qRT-PCR was applied to detect the tissue-specific expression levels of 12 putative miRNAs and 6 target genes in roots, leaves, flowers, and fruits. This study provides some important information about banana pre-miRNAs, mature miRNAs, and miRNA target genes and these findings can be applied to future research of miRNA functions. PMID:25856313

Euglena gracilis and Trypanosomatids possess common patterns in predicted mitochondrial targeting presequences.

PubMed

Krnáčová, Katarína; Vesteg, Matej; Hampl, Vladimír; Vlček, Čestmír; Horváth, Anton

2012-10-01

Euglena gracilis possessing chloroplasts of secondary green algal origin and parasitic trypanosomatids Trypanosoma brucei, Trypanosoma cruzi and Leishmania major belong to the protist phylum Euglenozoa. Euglenozoa might be among the earliest eukaryotic branches bearing ancestral traits reminiscent of the last eukaryotic common ancestor (LECA) or missing features present in other eukaryotes. LECA most likely possessed mitochondria of endosymbiotic α-proteobacterial origin. In this study, we searched for the presence of homologs of mitochondria-targeted proteins from other organisms in the currently available EST dataset of E. gracilis. The common motifs in predicted N-terminal presequences and corresponding homologs from T. brucei, T. cruzi and L. major (if found) were analyzed. Other trypanosomatid mitochondrial protein precursor (e.g., those involved in RNA editing) were also included in the analysis. Mitochondrial presequences of E. gracilis and these trypanosomatids seem to be highly variable in sequence length (5-118 aa), but apparently share statistically significant similarities. In most cases, the common (M/L)RR motif is present at the N-terminus and it is probably responsible for recognition via import apparatus of mitochondrial outer membrane. Interestingly, this motif is present inside the predicted presequence region in some cases. In most presequences, this motif is followed by a hydrophobic region rich in alanine, leucine, and valine. In conclusion, either RR motif or arginine-rich region within hydrophobic aa-s present at the N-terminus of a preprotein can be sufficient signals for mitochondrial import irrespective of presequence length in Euglenozoa.

Prediction of Host-Derived miRNAs with the Potential to Target PVY in Potato Plants

PubMed Central

Iqbal, Muhammad S.; Hafeez, Muhammad N.; Wattoo, Javed I.; Ali, Arfan; Sharif, Muhammad N.; Rashid, Bushra; Tabassum, Bushra; Nasir, Idrees A.

2016-01-01

Potato virus Y has emerged as a threatening problem in all potato growing areas around the globe. PVY reduces the yield and quality of potato cultivars. During the last 30 years, significant genetic changes in PVY strains have been observed with an increased incidence associated with crop damage. In the current study, computational approaches were applied to predict Potato derived miRNA targets in the PVY genome. The PVY genome is approximately 9 thousand nucleotides, which transcribes the following 6 genes:CI, NIa, NIb-Pro, HC-Pro, CP, and VPg. A total of 343 mature miRNAs were retrieved from the miRBase database and were examined for their target sequences in PVY genes using the minimum free energy (mfe), minimum folding energy, sequence complementarity and mRNA-miRNA hybridization approaches. The identified potato miRNAs against viral mRNA targets have antiviral activities, leading to translational inhibition by mRNA cleavage and/or mRNA blockage. We found 86 miRNAs targeting the PVY genome at 151 different sites. Moreover, only 36 miRNAs potentially targeted the PVY genome at 101 loci. The CI gene of the PVY genome was targeted by 32 miRNAs followed by the complementarity of 26, 19, 18, 16, and 13 miRNAs. Most importantly, we found 5 miRNAs (miR160a-5p, miR7997b, miR166c-3p, miR399h, and miR5303d) that could target the CI, NIa, NIb-Pro, HC-Pro, CP, and VPg genes of PVY. The predicted miRNAs can be used for the development of PVY-resistant potato crops in the future. PMID:27683585

Computer-aided target tracking in motion analysis studies

NASA Astrophysics Data System (ADS)

Burdick, Dominic C.; Marcuse, M. L.; Mislan, J. D.

1990-08-01

Motion analysis studies require the precise tracking of reference objects in sequential scenes. In a typical situation, events of interest are captured at high frame rates using special cameras, and selected objects or targets are tracked on a frame by frame basis to provide necessary data for motion reconstruction. Tracking is usually done using manual methods which are slow and prone to error. A computer based image analysis system has been developed that performs tracking automatically. The objective of this work was to eliminate the bottleneck due to manual methods in high volume tracking applications such as the analysis of crash test films for the automotive industry. The system has proven to be successful in tracking standard fiducial targets and other objects in crash test scenes. Over 95 percent of target positions which could be located using manual methods can be tracked by the system, with a significant improvement in throughput over manual methods. Future work will focus on the tracking of clusters of targets and on tracking deformable objects such as airbags.

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

PubMed

Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J; Frey, Juerg E; Basler, Konrad; Wollscheid, Bernd; Brunner, Erich; Ahrens, Christian H

2014-08-28

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction

Visualization and Analysis of MiRNA-Targets Interactions Networks.

PubMed

León, Luis E; Calligaris, Sebastián D

2017-01-01

MicroRNAs are a class of small, noncoding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the target mRNAs, mainly leading to down-regulation or repression of the target genes. MicroRNAs are involved in diverse regulatory pathways in normal and pathological conditions. In this context, it is highly important to identify the targets of specific microRNA in order to understand the mechanism of its regulation and consequently its involvement in disease. However, the microRNA target identification is experimentally laborious and time-consuming. The in silico prediction of microRNA targets is an extremely useful approach because you can identify potential mRNA targets, reduce the number of possibilities and then, validate a few microRNA-mRNA interactions in an in vitro experimental model. In this chapter, we describe, in a simple way, bioinformatics guidelines to use miRWalk database and Cytoscape software for analyzing microRNA-mRNA interactions through their visualization as a network.

Multi-target QSPR modeling for simultaneous prediction of multiple gas-phase kinetic rate constants of diverse chemicals

NASA Astrophysics Data System (ADS)

Basant, Nikita; Gupta, Shikha

2018-03-01

The reactions of molecular ozone (O3), hydroxyl (•OH) and nitrate (NO3) radicals are among the major pathways of removal of volatile organic compounds (VOCs) in the atmospheric environment. The gas-phase kinetic rate constants (kO3, kOH, kNO3) are thus, important in assessing the ultimate fate and exposure risk of atmospheric VOCs. Experimental data for rate constants are not available for many emerging VOCs and the computational methods reported so far address a single target modeling only. In this study, we have developed a multi-target (mt) QSPR model for simultaneous prediction of multiple kinetic rate constants (kO3, kOH, kNO3) of diverse organic chemicals considering an experimental data set of VOCs for which values of all the three rate constants are available. The mt-QSPR model identified and used five descriptors related to the molecular size, degree of saturation and electron density in a molecule, which were mechanistically interpretable. These descriptors successfully predicted three rate constants simultaneously. The model yielded high correlations (R2 = 0.874-0.924) between the experimental and simultaneously predicted endpoint rate constant (kO3, kOH, kNO3) values in test arrays for all the three systems. The model also passed all the stringent statistical validation tests for external predictivity. The proposed multi-target QSPR model can be successfully used for predicting reactivity of new VOCs simultaneously for their exposure risk assessment.

Mathematical modeling of antibody drug conjugates with the target and tubulin dynamics to predict AUC.

PubMed

Byun, Jong Hyuk; Jung, Il Hyo

2018-04-14

Antibody drug conjugates (ADCs)are one of the most recently developed chemotherapeutics to treat some types of tumor cells. They consist of monoclonal antibodies (mAbs), linkers, and potent cytotoxic drugs. Unlike common chemotherapies, ADCs combine selectively with a target at the surface of the tumor cell, and a potent cytotoxic drug (payload) effectively prevents microtubule polymerization. In this work, we construct an ADC model that considers both the target of antibodies and the receptor (tubulin) of the cytotoxic payloads. The model is simulated with brentuximab vedotin, one of ADCs, and used to investigate the pharmacokinetic (PK) characteristics of ADCs in vivo. It also predicts area under the curve (AUC) of ADCs and the payloads by identifying the half-life. The results show that dynamical behaviors fairly coincide with the observed data and half-life and capture AUC. Thus, the model can be used for estimating some parameters, fitting experimental observations, predicting AUC, and exploring various dynamical behaviors of the target and the receptor. Copyright © 2018 Elsevier Ltd. All rights reserved.

Enhanced guide-RNA design and targeting analysis for precise CRISPR genome editing of single and consortia of industrially relevant and non-model organisms.

PubMed

Mendoza, Brian J; Trinh, Cong T

2018-01-01

Genetic diversity of non-model organisms offers a repertoire of unique phenotypic features for exploration and cultivation for synthetic biology and metabolic engineering applications. To realize this enormous potential, it is critical to have an efficient genome editing tool for rapid strain engineering of these organisms to perform novel programmed functions. To accommodate the use of CRISPR/Cas systems for genome editing across organisms, we have developed a novel method, named CRISPR Associated Software for Pathway Engineering and Research (CASPER), for identifying on- and off-targets with enhanced predictability coupled with an analysis of non-unique (repeated) targets to assist in editing any organism with various endonucleases. Utilizing CASPER, we demonstrated a modest 2.4% and significant 30.2% improvement (F-test, P < 0.05) over the conventional methods for predicting on- and off-target activities, respectively. Further we used CASPER to develop novel applications in genome editing: multitargeting analysis (i.e. simultaneous multiple-site modification on a target genome with a sole guide-RNA requirement) and multispecies population analysis (i.e. guide-RNA design for genome editing across a consortium of organisms). Our analysis on a selection of industrially relevant organisms revealed a number of non-unique target sites associated with genes and transposable elements that can be used as potential sites for multitargeting. The analysis also identified shared and unshared targets that enable genome editing of single or multiple genomes in a consortium of interest. We envision CASPER as a useful platform to enhance the precise CRISPR genome editing for metabolic engineering and synthetic biology applications. https://github.com/TrinhLab/CASPER. ctrinh@utk.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis

PubMed Central

Kogadeeva, Maria

2016-01-01

Metabolic fluxes are a cornerstone of cellular physiology that emerge from a complex interplay of enzymes, carriers, and nutrients. The experimental assessment of in vivo intracellular fluxes using stable isotopic tracers is essential if we are to understand metabolic function and regulation. Flux estimation based on 13C or 2H labeling relies on complex simulation and iterative fitting; processes that necessitate a level of expertise that ordinarily preclude the non-expert user. To overcome this, we have developed SUMOFLUX, a methodology that is broadly applicable to the targeted analysis of 13C-metabolic fluxes. By combining surrogate modeling and machine learning, we trained a predictor to specialize in estimating flux ratios from measurable 13C-data. SUMOFLUX targets specific flux features individually, which makes it fast, user-friendly, applicable to experimental design and robust in terms of experimental noise and exchange flux magnitude. Collectively, we predict that SUMOFLUX's properties realistically pave the way to high-throughput flux analyses. PMID:27626798

DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction

PubMed Central

Jiang, Jinjian; Wang, Nian; Zhang, Jun

2017-01-01

Background Drug-target interaction is key in drug discovery, especially in the design of new lead compound. However, the work to find a new lead compound for a specific target is complicated and hard, and it always leads to many mistakes. Therefore computational techniques are commonly adopted in drug design, which can save time and costs to a significant extent. Results To address the issue, a new prediction system is proposed in this work to identify drug-target interaction. First, drug-target pairs are encoded with a fragment technique and the software “PaDEL-Descriptor.” The fragment technique is for encoding target proteins, which divides each protein sequence into several fragments in order and encodes each fragment with several physiochemical properties of amino acids. The software “PaDEL-Descriptor” creates encoding vectors for drug molecules. Second, the dataset of drug-target pairs is resampled and several overlapped subsets are obtained, which are then input into kNN (k-Nearest Neighbor) classifier to build an ensemble system. Conclusion Experimental results on the drug-target dataset showed that our method performs better and runs faster than the state-of-the-art predictors. PMID:28744468

Secondary structure prediction and structure-specific sequence analysis of single-stranded DNA.

PubMed

Dong, F; Allawi, H T; Anderson, T; Neri, B P; Lyamichev, V I

2001-08-01

DNA sequence analysis by oligonucleotide binding is often affected by interference with the secondary structure of the target DNA. Here we describe an approach that improves DNA secondary structure prediction by combining enzymatic probing of DNA by structure-specific 5'-nucleases with an energy minimization algorithm that utilizes the 5'-nuclease cleavage sites as constraints. The method can identify structural differences between two DNA molecules caused by minor sequence variations such as a single nucleotide mutation. It also demonstrates the existence of long-range interactions between DNA regions separated by >300 nt and the formation of multiple alternative structures by a 244 nt DNA molecule. The differences in the secondary structure of DNA molecules revealed by 5'-nuclease probing were used to design structure-specific probes for mutation discrimination that target the regions of structural, rather than sequence, differences. We also demonstrate the performance of structure-specific 'bridge' probes complementary to non-contiguous regions of the target molecule. The structure-specific probes do not require the high stringency binding conditions necessary for methods based on mismatch formation and permit mutation detection at temperatures from 4 to 37 degrees C. Structure-specific sequence analysis is applied for mutation detection in the Mycobacterium tuberculosis katG gene and for genotyping of the hepatitis C virus.

Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis.

PubMed

Lee, A Yeong; Park, Won; Kang, Tae-Wook; Cha, Min Ho; Chun, Jin Mi

2018-07-15

Yijin-Tang (YJT) is a traditional prescription for the treatment of hyperlipidaemia, atherosclerosis and other ailments related to dampness phlegm, a typical pathological symptom of abnormal body fluid metabolism in Traditional Korean Medicine. However, a holistic network pharmacology approach to understanding the therapeutic mechanisms underlying hyperlipidaemia and atherosclerosis has not been pursued. To examine the network pharmacological potential effects of YJT on hyperlipidaemia and atherosclerosis, we analysed components, performed target prediction and network analysis, and investigated interacting pathways using a network pharmacology approach. Information on compounds in herbal medicines was obtained from public databases, and oral bioavailability and drug-likeness was screened using absorption, distribution, metabolism, and excretion (ADME) criteria. Correlations between compounds and genes were linked using the STITCH database, and genes related to hyperlipidaemia and atherosclerosis were gathered using the GeneCards database. Human genes were identified and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Network analysis identified 447 compounds in five herbal medicines that were subjected to ADME screening, and 21 compounds and 57 genes formed the main pathways linked to hyperlipidaemia and atherosclerosis. Among them, 10 compounds (naringenin, nobiletin, hesperidin, galangin, glycyrrhizin, homogentisic acid, stigmasterol, 6-gingerol, quercetin and glabridin) were linked to more than four genes, and are bioactive compounds and key chemicals. Core genes in this network were CASP3, CYP1A1, CYP1A2, MMP2 and MMP9. The compound-target gene network revealed close interactions between multiple components and multiple targets, and facilitates a better understanding of the potential therapeutic effects of YJT. Pharmacological network analysis can help to explain the potential effects of YJT for treating dampness phlegm

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rashkin, Hannah J.; Bell, Eric B.; Choi, Yejin

People around the globe respond to major real world events through social media. To study targeted public sentiments across many languages and geographic locations, we introduce multilingual connotation frames: an extension from English connotation frames of Rashkin et al. (2016) with 10 additional European languages, focusing on the implied sentiments among event participants engaged in a frame. As a case study, we present large scale analysis on targeted public sentiments using 1.2 million multilingual connotation frames extracted from Twitter. We rely on connotation frames to build models to forecast country-specific connotation dynamics – perspective change over time towards salient entitiesmore » and events. Our results demonstrate that connotation dynamics can be accurately predicted up to half a week in advance.« less

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria.

PubMed

Chanumolu, Sree Krishna; Rout, Chittaranjan; Chauhan, Rajinder S

2012-01-01

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed. A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed. The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets. UniDrug-Target is expected to accelerate

Human L-DOPA decarboxylase mRNA is a target of miR-145: A prediction to validation workflow.

PubMed

Papadopoulos, Emmanuel I; Fragoulis, Emmanuel G; Scorilas, Andreas

2015-01-10

l-DOPA decarboxylase (DDC) is a multiply-regulated gene which encodes the enzyme that catalyzes the biosynthesis of dopamine in humans. MicroRNAs comprise a novel class of endogenously transcribed small RNAs that can post-transcriptionally regulate the expression of various genes. Given that the mechanism of microRNA target recognition remains elusive, several genes, including DDC, have not yet been identified as microRNA targets. Nevertheless, a number of specifically designed bioinformatic algorithms provide candidate miRNAs for almost every gene, but still their results exhibit moderate accuracy and should be experimentally validated. Motivated by the above, we herein sought to discover a microRNA that regulates DDC expression. By using the current algorithms according to bibliographic recommendations we found that miR-145 could be predicted with high specificity as a candidate regulatory microRNA for DDC expression. Thus, a validation experiment followed by firstly transfecting an appropriate cell culture system with a synthetic miR-145 sequence and sequentially assessing the mRNA and protein levels of DDC via real-time PCR and Western blotting, respectively. Our analysis revealed that miR-145 had no significant impact on protein levels of DDC but managed to dramatically downregulate its mRNA expression. Overall, the experimental and bioinformatic analysis conducted herein indicate that miR-145 has the ability to regulate DDC mRNA expression and potentially this occurs by recognizing its mRNA as a target. Copyright © 2014. Published by Elsevier B.V.

Analysis of a Neutronic Experiment on a Simulated Mercury Spallation Neutron Target Assembly Bombarded by Giga-Electron-Volt Protons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maekawa, Fujio; Meigo, Shin-ichiro; Kasugai, Yoshimi

2005-05-15

A neutronic benchmark experiment on a simulated spallation neutron target assembly was conducted by using the Alternating Gradient Synchrotron at Brookhaven National Laboratory and was analyzed to investigate the prediction capability of Monte Carlo simulation codes used in neutronic designs of spallation neutron sources. The target assembly consisting of a mercury target, a light water moderator, and a lead reflector was bombarded by 1.94-, 12-, and 24-GeV protons, and the fast neutron flux distributions around the target and the spectra of thermal neutrons leaking from the moderator were measured in the experiment. In this study, the Monte Carlo particle transportmore » simulation codes NMTC/JAM, MCNPX, and MCNP-4A with associated cross-section data in JENDL and LA-150 were verified based on benchmark analysis of the experiment. As a result, all the calculations predicted the measured quantities adequately; calculated integral fluxes of fast and thermal neutrons agreed approximately within {+-}40% with the experiments although the overall energy range encompassed more than 12 orders of magnitude. Accordingly, it was concluded that these simulation codes and cross-section data were adequate for neutronics designs of spallation neutron sources.« less

Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands.

PubMed

Reutlinger, Michael; Rodrigues, Tiago; Schneider, Petra; Schneider, Gisbert

2014-01-07

Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach.

PubMed

Lee, Hyung-Chul; Ryu, Ho-Geol; Chung, Eun-Jin; Jung, Chul-Woo

2018-03-01

The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P < 0.001). The deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

shRNA target prediction informed by comprehensive enquiry (SPICE): a supporting system for high-throughput screening of shRNA library.

PubMed

Kamatuka, Kenta; Hattori, Masahiro; Sugiyama, Tomoyasu

2016-12-01

RNA interference (RNAi) screening is extensively used in the field of reverse genetics. RNAi libraries constructed using random oligonucleotides have made this technology affordable. However, the new methodology requires exploration of the RNAi target gene information after screening because the RNAi library includes non-natural sequences that are not found in genes. Here, we developed a web-based tool to support RNAi screening. The system performs short hairpin RNA (shRNA) target prediction that is informed by comprehensive enquiry (SPICE). SPICE automates several tasks that are laborious but indispensable to evaluate the shRNAs obtained by RNAi screening. SPICE has four main functions: (i) sequence identification of shRNA in the input sequence (the sequence might be obtained by sequencing clones in the RNAi library), (ii) searching the target genes in the database, (iii) demonstrating biological information obtained from the database, and (iv) preparation of search result files that can be utilized in a local personal computer (PC). Using this system, we demonstrated that genes targeted by random oligonucleotide-derived shRNAs were not different from those targeted by organism-specific shRNA. The system facilitates RNAi screening, which requires sequence analysis after screening. The SPICE web application is available at http://www.spice.sugysun.org/.

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

PubMed

Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

2016-01-01

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

Measurements to predict the time of target replacement of a helical tomotherapy.

PubMed

Kampfer, Severin; Schell, Stefan; Duma, Marciana N; Wilkens, Jan J; Kneschaurek, Peter

2011-11-15

Intensity-modulated radiation therapy (IMRT) requires more beam-on time than normal open field treatment. Consequently, the machines wear out and need more spare parts. A helical tomotherapy treatment unit needs a periodical tungsten target replacement, which is a time consuming event. To be able to predict the next replacement would be quite valuable. We observed unexpected variations towards the end of the target lifetime in the performed pretreatment measurements for patient plan verification. Thus, we retrospectively analyze the measurements of our quality assurance program. The time dependence of the quotient of two simultaneous dose measurements at different depths within a phantom for a fixed open field irradiation is evaluated. We also assess the time-dependent changes of an IMRT plan measurement and of a relative depth dose curve measurement. Additionally, we performed a Monte Carlo simulation with Geant4 to understand the physical reasons for the measured values. Our measurements show that the dose at a specified depth compared to the dose in shallower regions of the phantom declines towards the end of the target lifetime. This reproducible effect can be due to the lowering of the mean energy of the X-ray spectrum. These results are supported by the measurements of the IMRT plan, as well as the study of the relative depth dose curve. Furthermore, the simulation is consistent with these findings since it provides a possible explanation for the reduction of the mean energy for thinner targets. It could be due to the lowering of low energy photon self-absorption in a worn out and therefore thinner target. We state a threshold value for our measurement at which a target replacement should be initiated. Measurements to observe a change in the energy are good predictors of the need for a target replacement. However, since all results support the softening of the spectrum hypothesis, all depth-dependent setups are viable for analyzing the deterioration of the

Transcriptome-wide Analysis of Exosome Targets

PubMed Central

Schneider, Claudia; Kudla, Grzegorz; Wlotzka, Wiebke; Tuck, Alex; Tollervey, David

2012-01-01

Summary The exosome plays major roles in RNA processing and surveillance but the in vivo target range and substrate acquisition mechanisms remain unclear. Here we apply in vivo RNA crosslinking (CRAC) to the nucleases (Rrp44, Rrp6), two structural subunits (Rrp41, Csl4) and a cofactor (Trf4) of the yeast exosome. Analysis of wild-type Rrp44 and catalytic mutants showed that both the CUT and SUT classes of non-coding RNA, snoRNAs and, most prominently, pre-tRNAs and other Pol III transcripts are targeted for oligoadenylation and exosome degradation. Unspliced pre-mRNAs were also identified as targets for Rrp44 and Rrp6. CRAC performed using cleavable proteins (split-CRAC) revealed that Rrp44 endonuclease and exonuclease activities cooperate on most substrates. Mapping oligoadenylated reads suggests that the endonuclease activity may release stalled exosome substrates. Rrp6 was preferentially associated with structured targets, which frequently did not associate with the core exosome indicating that substrates follow multiple pathways to the nucleases. PMID:23000172

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics.

PubMed

Hu, Ruifeng; Sun, Xiaobo

2016-08-01

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

Target-Independent Prediction of Drug Synergies Using Only Drug Lipophilicity

PubMed Central

2015-01-01

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms. PMID:25026390

Binding site and affinity prediction of general anesthetics to protein targets using docking.

PubMed

Liu, Renyu; Perez-Aguilar, Jose Manuel; Liang, David; Saven, Jeffery G

2012-05-01

The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explored whether a computational method, AutoDock, could serve as such a tool. High-resolution crystal data of water-soluble proteins (cytochrome C, apoferritin, and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus [GLIC]) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (http://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants were compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent cocrystallization data. Docking calculations for 6 general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known 50% effective concentration (EC(50)) values were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC(50) values and octanol/water partition coefficients for the 6 general anesthetics. All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (P = 0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site

Binding Site and Affinity Prediction of General Anesthetics to Protein Targets Using Docking

PubMed Central

Liu, Renyu; Perez-Aguilar, Jose Manuel; Liang, David; Saven, Jeffery G.

2012-01-01

Background The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explore whether a computational method, AutoDock, could serve as such a tool. Methods High-resolution crystal data of water soluble proteins (cytochrome C, apoferritin and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus, GLIC) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (https://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants are compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent co-crystallization data. Docking calculations for six general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known EC50 were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC50s and octanol/water partition coefficients for the six general anesthetics. Results All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (p=0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the

Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods.

PubMed

Kazemian, Pooyan; Lavieri, Mariel S; Van Oyen, Mark P; Andrews, Chris; Stein, Joshua D

2018-04-01

To generate personalized forecasts of how patients with open-angle glaucoma (OAG) experience disease progression at different intraocular pressure (IOP) levels to aid clinicians with setting personalized target IOPs. Secondary analyses using longitudinal data from 2 randomized controlled trials. Participants with moderate or advanced OAG from the Collaborative Initial Glaucoma Treatment Study (CIGTS) or the Advanced Glaucoma Intervention Study (AGIS). By using perimetric and tonometric data from trial participants, we developed and validated Kalman Filter (KF) models for fast-, slow-, and nonprogressing patients with OAG. The KF can generate personalized and dynamically updated forecasts of OAG progression under different target IOP levels. For each participant, we determined how mean deviation (MD) would change if the patient maintains his/her IOP at 1 of 7 levels (6, 9, 12, 15, 18, 21, or 24 mmHg) over the next 5 years. We also model and predict changes to MD over the same time horizon if IOP is increased or decreased by 3, 6, and 9 mmHg from the level attained in the trials. Personalized estimates of the change in MD under different target IOP levels. A total of 571 participants (mean age, 64.2 years; standard deviation, 10.9) were followed for a mean of 6.5 years (standard deviation, 2.8). Our models predicted that, on average, fast progressors would lose 2.1, 6.7, and 11.2 decibels (dB) MD under target IOPs of 6, 15, and 24 mmHg, respectively, over 5 years. In contrast, on average, slow progressors would lose 0.8, 2.1, and 4.1 dB MD under the same target IOPs and time frame. When using our tool to quantify the OAG progression dynamics for all 571 patients, we found no statistically significant differences over 5 years between progression for black versus white, male versus female, and CIGTS versus AGIS participants under different target IOPs (P > 0.05 for all). To our knowledge, this is the first clinical decision-making tool that generates personalized

Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives.

PubMed

Romero-Durán, Francisco J; Alonso, Nerea; Yañez, Matilde; Caamaño, Olga; García-Mera, Xerardo; González-Díaz, Humberto

2016-04-01

The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Microarray‑based bioinformatics analysis of the prospective target gene network of key miRNAs influenced by long non‑coding RNA PVT1 in HCC.

PubMed

Zhang, Yu; Mo, Wei-Jia; Wang, Xiao; Zhang, Tong-Tong; Qin, Yuan; Wang, Han-Lin; Chen, Gang; Wei, Dan-Ming; Dang, Yi-Wu

2018-05-02

The long non‑coding RNA (lncRNA) PVT1 plays vital roles in the tumorigenesis and development of various types of cancer. However, the potential expression profiling, functions and pathways of PVT1 in HCC remain unknown. PVT1 was knocked down in SMMC‑7721 cells, and a miRNA microarray analysis was performed to detect the differentially expressed miRNAs. Twelve target prediction algorithms were used to predict the underlying targets of these differentially expressed miRNAs. Bioinformatics analysis was performed to explore the underlying functions, pathways and networks of the targeted genes. Furthermore, the relationship between PVT1 and the clinical parameters in HCC was confirmed based on the original data in the TCGA database. Among the differentially expressed miRNAs, the top two upregulated and downregulated miRNAs were selected for further analysis based on the false discovery rate (FDR), fold‑change (FC) and P‑values. Based on the TCGA database, PVT1 was obviously highly expressed in HCC, and a statistically higher PVT1 expression was found for sex (male), ethnicity (Asian) and pathological grade (G3+G4) compared to the control groups (P<0.05). Furthermore, Gene Ontology (GO) analysis revealed that the target genes were involved in complex cellular pathways, such as the macromolecule biosynthetic process, compound metabolic process, and transcription. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the MAPK and Wnt signaling pathways may be correlated with the regulation of the four candidate miRNAs. The results therefore provide significant information on the differentially expressed miRNAs associated with PVT1 in HCC, and we hypothesized that PVT1 may play vital roles in HCC by regulating different miRNAs or target gene expression (particularly MAPK8) via the MAPK or Wnt signaling pathways. Thus, further investigation of the molecular mechanism of PVT1 in HCC is needed.

Computer-based prediction of mitochondria-targeting peptides.

PubMed

Martelli, Pier Luigi; Savojardo, Castrense; Fariselli, Piero; Tasco, Gianluca; Casadio, Rita

2015-01-01

Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where the mature protein is active and very difficult to characterize experimentally. Mitochondrial proteins encoded on the cytosolic ribosomes carry specific patterns in the precursor sequence from where it is possible to recognize a peptide targeting the protein to its final destination. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequences and benchmark our and other methods on the human mitochondrial proteins endowed with experimentally characterized targeting peptides. Furthermore, we illustrate our newly implemented web server and its usage on the whole human proteome in order to infer mitochondrial targeting peptides, their cleavage sites, and whether the targeting peptide regions contain or not arginine-rich recurrent motifs. By this, we add some other 2,800 human proteins to the 124 ones already experimentally annotated with a mitochondrial targeting peptide.

Target Fortification of Breast Milk: Predicting the Final Osmolality of the Feeds

PubMed Central

Choi, Arum; Fusch, Gerhard; Rochow, Niels; Fusch, Christoph

2016-01-01

For preterm infants, it is common practice to add human milk fortifiers to native breast milk to enhance protein and calorie supply because the growth rates and nutritional requirements of preterm infants are considerably higher than those of term infants. However, macronutrient intake may still be inadequate because the composition of native breast milk has individual inter- and intra-sample variation. Target fortification (TFO) of breast milk is a new nutritional regime aiming to reduce such variations by individually measuring and adding deficient macronutrients. Added TFO components contribute to the final osmolality of milk feeds. It is important to predict the final osmolality of TFO breast milk to ensure current osmolality recommendations are followed to minimize feeding intolerance and necrotizing enterocolitis. This study aims to develop and validate equations to predict the osmolality of TFO milk batches. To establish prediction models, the osmolalities of either native or supplemented breast milk with known amounts of fat, protein, and carbohydrates were analyzed. To validate prediction models, the osmolalities of each macronutrient and combinations of macronutrients were measured in an independent sample set. Additionally, osmolality was measured in TFO milk samples obtained from a previous clinical study and compared with predicted osmolality using the prediction equations. Following the addition of 1 g of carbohydrates (glucose polymer), 1 g of hydrolyzed protein, or 1 g of whey protein per 100 mL breast milk, the average increase in osmolality was 20, 38, and 4 mOsm/kg respectively. Adding fat decreased osmolality only marginally due to dilution effect. Measured and predicted osmolality of combinations of macronutrients as well as single macronutrient (R2 = 0.93) were highly correlated. Using clinical data (n = 696), the average difference between the measured and predicted osmolality was 3 ± 11 mOsm/kg and was not statistically significant. In

Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.

PubMed

Raia, Valentina; Schilling, Marcel; Böhm, Martin; Hahn, Bettina; Kowarsch, Andreas; Raue, Andreas; Sticht, Carsten; Bohl, Sebastian; Saile, Maria; Möller, Peter; Gretz, Norbert; Timmer, Jens; Theis, Fabian; Lehmann, Wolf-Dieter; Lichter, Peter; Klingmüller, Ursula

2011-02-01

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study.

PubMed

Yu, Su Jong; Kim, Hyunsoo; Min, Hophil; Sohn, Areum; Cho, Young Youn; Yoo, Jeong-Ju; Lee, Dong Hyeon; Cho, Eun Ju; Lee, Jeong-Hoon; Gim, Jungsoo; Park, Taesung; Kim, Yoon Jun; Kim, Chung Yong; Yoon, Jung-Hwan; Kim, Youngsoo

2017-03-03

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

Current target acquisition methodology in force on force simulations

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks. This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors' performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.

Molecular Analysis of Sarcoidosis Granulomas Reveals Antimicrobial Targets

PubMed Central

Celada, Lindsay J.; Polosukhin, Vasiliy V.; Atkinson, James B.; Drake, Wonder P.

2016-01-01

Sarcoidosis is a granulomatous disease of unknown cause. Prior molecular and immunologic studies have confirmed the presence of mycobacterial virulence factors, such as catalase peroxidase and superoxide dismutase A, within sarcoidosis granulomas. Molecular analysis of granulomas can identify targets of known antibiotics classes. Currently, major antibiotics are directed against DNA synthesis, protein synthesis, and cell wall formation. We conducted molecular analysis of 40 sarcoidosis diagnostic specimens and compared them with 33 disease control specimens for the presence of mycobacterial genes that encode antibiotic targets. We assessed for genes involved in DNA synthesis (DNA gyrase A [gyrA] and DNA gyrase B), protein synthesis (RNA polymerase subunit β), cell wall synthesis (embCAB operon and enoyl reductase), and catalase peroxidase. Immunohistochemical analysis was conducted to investigate the locale of mycobacterial genes such as gyrA within 12 sarcoidosis specimens and 12 disease controls. Mycobacterial DNA was detected in 33 of 39 sarcoidosis specimens by quantitative real-time polymerase chain reaction compared with 2 of 30 disease control specimens (P < 0.001, two-tailed Fisher’s test). Twenty of 39 were positive for three or more mycobacterial genes, compared with 1 of 30 control specimens (P < 0.001, two-tailed Fisher’s test). Immunohistochemistry analysis localized mycobacterial gyrA nucleic acids to sites of granuloma formation in 9 of 12 sarcoidosis specimens compared with 1 of 12 disease controls (P < 0.01). Microbial genes encoding enzymes that can be targeted by currently available antimycobacterial antibiotics are present in sarcoidosis specimens and localize to sites of granulomatous inflammation. Use of antimicrobials directed against target enzymes may be an innovative treatment alternative. PMID:26807608

Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants.

PubMed

Goldstein-Piekarski, Andrea N; Korgaonkar, Mayuresh S; Green, Erin; Suppes, Trisha; Schatzberg, Alan F; Hastie, Trevor; Nemeroff, Charles B; Williams, Leanne M

2016-10-18

Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person.

Full-field dynamic strain prediction on a wind turbine using displacements of optical targets measured by stereophotogrammetry

NASA Astrophysics Data System (ADS)

Baqersad, Javad; Niezrecki, Christopher; Avitabile, Peter

2015-10-01

Health monitoring of rotating structures (e.g. wind turbines and helicopter blades) has historically been a challenge due to sensing and data transmission problems. Unfortunately mechanical failure in many structures initiates at components on or inside the structure where there is no sensor located to predict the failure. In this paper, a wind turbine was mounted with a semi-built-in configuration and was excited using a mechanical shaker. A series of optical targets was distributed along the blades and the fixture and the displacement of those targets during excitation was measured using a pair of high speed cameras. Measured displacements with three dimensional point tracking were transformed to all finite element degrees of freedom using a modal expansion algorithm. The expanded displacements were applied to the finite element model to predict the full-field dynamic strain on the surface of the structure as well as within the interior points. To validate the methodology of dynamic strain prediction, the predicted strain was compared to measured strain by using six mounted strain-gages. To verify if a simpler model of the turbine can be used for the expansion, the expansion process was performed both by using the modes of the entire turbine and modes of a single cantilever blade. The results indicate that the expansion approach can accurately predict the strain throughout the turbine blades from displacements measured by using stereophotogrammetry.

TU-G-210-03: Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vyas, U.

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS Urvi Vyas – Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

Sentiment analysis enhancement with target variable in Kumar’s Algorithm

NASA Astrophysics Data System (ADS)

Arman, A. A.; Kawi, A. B.; Hurriyati, R.

2016-04-01

Sentiment analysis (also known as opinion mining) refers to the use of text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews discussion that is being talked in social media for many purposes, ranging from marketing, customer service, or public opinion of public policy. One of the popular algorithm for Sentiment Analysis implementation is Kumar algorithm that developed by Kumar and Sebastian. Kumar algorithm can identify the sentiment score of the statement, sentence or tweet, but cannot determine the relationship of the object or target related to the sentiment being analysed. This research proposed solution for that challenge by adding additional component that represent object or target to the existing algorithm (Kumar algorithm). The result of this research is a modified algorithm that can give sentiment score based on a given object or target.

Spatiotemporal Analysis of Malaria in Urban Ahmedabad (Gujarat), India: Identification of Hot Spots and Risk Factors for Targeted Intervention

PubMed Central

Parizo, Justin; Sturrock, Hugh J. W.; Dhiman, Ramesh C.; Greenhouse, Bryan

2016-01-01

The world population, especially in developing countries, has experienced a rapid progression of urbanization over the last half century. Urbanization has been accompanied by a rise in cases of urban infectious diseases, such as malaria. The complexity and heterogeneity of the urban environment has made study of specific urban centers vital for urban malaria control programs, whereas more generalizable risk factor identification also remains essential. Ahmedabad city, India, is a large urban center located in the state of Gujarat, which has experienced a significant Plasmodium vivax and Plasmodium falciparum disease burden. Therefore, a targeted analysis of malaria in Ahmedabad city was undertaken to identify spatiotemporal patterns of malaria, risk factors, and methods of predicting future malaria cases. Malaria incidence in Ahmedabad city was found to be spatially heterogeneous, but temporally stable, with high spatial correlation between species. Because of this stability, a prediction method utilizing historic cases from prior years and seasons was used successfully to predict which areas of Ahmedabad city would experience the highest malaria burden and could be used to prospectively target interventions. Finally, spatial analysis showed that normalized difference vegetation index, proximity to water sources, and location within Ahmedabad city relative to the dense urban core were the best predictors of malaria incidence. Because of the heterogeneity of urban environments and urban malaria itself, the study of specific large urban centers is vital to assist in allocating resources and informing future urban planning. PMID:27382081

The predictive validity of ideal partner preferences: a review and meta-analysis.

PubMed

Eastwick, Paul W; Luchies, Laura B; Finkel, Eli J; Hunt, Lucy L

2014-05-01

A central element of interdependence theory is that people have standards against which they compare their current outcomes, and one ubiquitous standard in the mating domain is the preference for particular attributes in a partner (ideal partner preferences). This article reviews research on the predictive validity of ideal partner preferences and presents a new integrative model that highlights when and why ideals succeed or fail to predict relational outcomes. Section 1 examines predictive validity by reviewing research on sex differences in the preference for physical attractiveness and earning prospects. Men and women reliably differ in the extent to which these qualities affect their romantic evaluations of hypothetical targets. Yet a new meta-analysis spanning the attraction and relationships literatures (k = 97) revealed that physical attractiveness predicted romantic evaluations with a moderate-to-strong effect size (r = ∼.40) for both sexes, and earning prospects predicted romantic evaluations with a small effect size (r = ∼.10) for both sexes. Sex differences in the correlations were small (r difference = .03) and uniformly nonsignificant. Section 2 reviews research on individual differences in ideal partner preferences, drawing from several theoretical traditions to explain why ideals predict relational evaluations at different relationship stages. Furthermore, this literature also identifies alternative measures of ideal partner preferences that have stronger predictive validity in certain theoretically sensible contexts. Finally, a discussion highlights a new framework for conceptualizing the appeal of traits, the difference between live and hypothetical interactions, and the productive interplay between mating research and broader psychological theories.

Confirming therapeutic target of protopine using immobilized β2 -adrenoceptor coupled with site-directed molecular docking and the target-drug interaction by frontal analysis and injection amount-dependent method.

PubMed

Liu, Guangxin; Wang, Pei; Li, Chan; Wang, Jing; Sun, Zhenyu; Zhao, Xinfeng; Zheng, Xiaohui

2017-07-01

Drug-protein interaction analysis is pregnant in designing new leads during drug discovery. We prepared the stationary phase containing immobilized β 2 -adrenoceptor (β 2 -AR) by linkage of the receptor on macroporous silica gel surface through N,N'-carbonyldiimidazole method. The stationary phase was applied in identifying antiasthmatic target of protopine guided by the prediction of site-directed molecular docking. Subsequent application of immobilized β 2 -AR in exploring the binding of protopine to the receptor was realized by frontal analysis and injection amount-dependent method. The association constants of protopine to β 2 -AR by the 2 methods were (1.00 ± 0.06) × 10 5 M -1 and (1.52 ± 0.14) × 10 4 M -1 . The numbers of binding sites were (1.23 ± 0.07) × 10 -7 M and (9.09 ± 0.06) × 10 -7 M, respectively. These results indicated that β 2 -AR is the specific target for therapeutic action of protopine in vivo. The target-drug binding occurred on Ser 169 in crystal structure of the receptor. Compared with frontal analysis, injection amount-dependent method is advantageous to drug saving, improvement of sampling efficiency, and performing speed. It has grave potential in high-throughput drug-receptor interaction analysis. Copyright © 2017 John Wiley & Sons, Ltd.

Sequence Alignment to Predict Across Species Susceptibility ...

EPA Pesticide Factsheets

Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to simplify, streamline, and quantitatively assess protein sequence/structural similarity across taxonomic groups as a means to predict relative intrinsic susceptibility. The intent of the tool is to allow for evaluation of any potential protein target, so it is amenable to variable degrees of protein characterization, depending on available information about the chemical/protein interaction and the molecular target itself. To allow for flexibility in the analysis, a layered strategy was adopted for the tool. The first level of the SeqAPASS analysis compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity (including detection of candidate orthologs), the second level evaluates sequence similarity within selected domains (e.g., ligand-binding domain, DNA binding domain), and the third level of analysis compares individual amino acid residue positions identified as being of importance for protein conformation and/or ligand binding upon chemical perturbation. Each level of the SeqAPASS analysis provides increasing evidence to apply toward rapid, screening-level assessments of probable cross species susceptibility. Such analyses can support prioritization of chemicals for further ev

In vitro perturbations of targets in cancer hallmark processes predict rodent chemical carcinogenesis.

PubMed

Kleinstreuer, Nicole C; Dix, David J; Houck, Keith A; Kavlock, Robert J; Knudsen, Thomas B; Martin, Matthew T; Paul, Katie B; Reif, David M; Crofton, Kevin M; Hamilton, Kerry; Hunter, Ronald; Shah, Imran; Judson, Richard S

2013-01-01

Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes to build models for priority setting and further testing. We describe a model for predicting rodent carcinogenicity based on HTS data from 292 chemicals tested in 672 assays mapping to 455 genes. All data come from the EPA ToxCast project. The model was trained on a subset of 232 chemicals with in vivo rodent carcinogenicity data in the Toxicity Reference Database (ToxRefDB). Individual HTS assays strongly associated with rodent cancers in ToxRefDB were linked to genes, pathways, and hallmark processes documented to be involved in tumor biology and cancer progression. Rodent liver cancer endpoints were linked to well-documented pathways such as peroxisome proliferator-activated receptor signaling and TP53 and novel targets such as PDE5A and PLAUR. Cancer hallmark genes associated with rodent thyroid tumors were found to be linked to human thyroid tumors and autoimmune thyroid disease. A model was developed in which these genes/pathways function as hypothetical enhancers or promoters of rat thyroid tumors, acting secondary to the key initiating event of thyroid hormone disruption. A simple scoring function was generated to identify chemicals with significant in vitro evidence that was predictive of in vivo carcinogenicity in different rat tissues and organs. This scoring function was applied to an external test set of 33 compounds with carcinogenicity classifications from the EPA's Office of Pesticide Programs and successfully (p = 0.024) differentiated between chemicals classified as "possible"/"probable"/"likely" carcinogens and those designated as "not likely" or with "evidence of noncarcinogenicity." This model represents a chemical carcinogenicity prioritization tool supporting targeted

Attentional Control via Parallel Target-Templates in Dual-Target Search

PubMed Central

Barrett, Doug J. K.; Zobay, Oliver

2014-01-01

Simultaneous search for two targets has been shown to be slower and less accurate than independent searches for the same two targets. Recent research suggests this ‘dual-target cost’ may be attributable to a limit in the number of target-templates than can guide search at any one time. The current study investigated this possibility by comparing behavioural responses during single- and dual-target searches for targets defined by their orientation. The results revealed an increase in reaction times for dual- compared to single-target searches that was largely independent of the number of items in the display. Response accuracy also decreased on dual- compared to single-target searches: dual-target accuracy was higher than predicted by a model restricting search guidance to a single target-template and lower than predicted by a model simulating two independent single-target searches. These results are consistent with a parallel model of dual-target search in which attentional control is exerted by more than one target-template at a time. The requirement to maintain two target-templates simultaneously, however, appears to impose a reduction in the specificity of the memory representation that guides search for each target. PMID:24489793

Nuclear Security: Target Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Surinder Paul; Gibbs, Philip W.; Bultz, Garl A.

2014-03-01

This objectives of this session were to understand the basic steps of target identification; describe the SNRI targets in detail; characterize specific targets with more detail; prioritize targets based on guidance documents; understand the graded safeguards concept; identify roll up and understand why it is a concern; and recognize the category for different materials.

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Economically sustainable scaling of photovoltaics to meet climate targets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Needleman, David Berney; Poindexter, Jeremy R.; Kurchin, Rachel C.

To meet climate targets, power generation capacity from photovoltaics (PV) in 2030 will have to be much greater than is predicted from either steady state growth using today's manufacturing capacity or industry roadmaps. Analysis of whether current technology can scale, in an economically sustainable way, to sufficient levels to meet these targets has not yet been undertaken, nor have tools to perform this analysis been presented. Here, we use bottom-up cost modeling to predict cumulative capacity as a function of technological and economic variables. We find that today's technology falls short in two ways: profits are too small relative tomore » upfront factory costs to grow manufacturing capacity rapidly enough to meet climate targets, and costs are too high to generate enough demand to meet climate targets. We show that decreasing the capital intensity (capex) of PV manufacturing to increase manufacturing capacity and effectively reducing cost (e.g., through higher efficiency) to increase demand are the most effective and least risky ways to address these barriers to scale. We also assess the effects of variations in demand due to hard-to-predict factors, like public policy, on the necessary reductions in cost.Lastly, we review examples of redundant technology pathways for crystalline silicon PV to achieve the necessary innovations in capex, performance, and price.« less

Economically sustainable scaling of photovoltaics to meet climate targets

DOE PAGES

Needleman, David Berney; Poindexter, Jeremy R.; Kurchin, Rachel C.; ...

2016-04-21

To meet climate targets, power generation capacity from photovoltaics (PV) in 2030 will have to be much greater than is predicted from either steady state growth using today's manufacturing capacity or industry roadmaps. Analysis of whether current technology can scale, in an economically sustainable way, to sufficient levels to meet these targets has not yet been undertaken, nor have tools to perform this analysis been presented. Here, we use bottom-up cost modeling to predict cumulative capacity as a function of technological and economic variables. We find that today's technology falls short in two ways: profits are too small relative tomore » upfront factory costs to grow manufacturing capacity rapidly enough to meet climate targets, and costs are too high to generate enough demand to meet climate targets. We show that decreasing the capital intensity (capex) of PV manufacturing to increase manufacturing capacity and effectively reducing cost (e.g., through higher efficiency) to increase demand are the most effective and least risky ways to address these barriers to scale. We also assess the effects of variations in demand due to hard-to-predict factors, like public policy, on the necessary reductions in cost.Lastly, we review examples of redundant technology pathways for crystalline silicon PV to achieve the necessary innovations in capex, performance, and price.« less

Properties of Protein Drug Target Classes

PubMed Central

Bull, Simon C.; Doig, Andrew J.

2015-01-01

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme. PMID

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

NASA Astrophysics Data System (ADS)

Huang, Lu; Jiang, Yuyang; Chen, Yuzong

2017-01-01

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Inference of Expanded Lrp-Like Feast/Famine Transcription Factor Targets in a Non-Model Organism Using Protein Structure-Based Prediction

PubMed Central

Ashworth, Justin; Plaisier, Christopher L.; Lo, Fang Yin; Reiss, David J.; Baliga, Nitin S.

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer. PMID:25255272

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

PubMed

Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin; Reiss, David J; Baliga, Nitin S

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.

Classifying transcription factor targets and discovering relevant biological features

PubMed Central

Holloway, Dustin T; Kon, Mark; DeLisi, Charles

2008-01-01

Background An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties. Principal Findings (1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related

The Predicted Secretome of the Plant Pathogenic Fungus Fusarium graminearum: A Refined Comparative Analysis

PubMed Central

Brown, Neil A.; Antoniw, John; Hammond-Kosack, Kim E.

2012-01-01

The fungus Fusarium graminearum forms an intimate association with the host species wheat whilst infecting the floral tissues at anthesis. During the prolonged latent period of infection, extracellular communication between live pathogen and host cells must occur, implying a role for secreted fungal proteins. The wheat cells in contact with fungal hyphae subsequently die and intracellular hyphal colonisation results in the development of visible disease symptoms. Since the original genome annotation analysis was done in 2007, which predicted the secretome using TargetP, the F. graminearum gene call has changed considerably through the combined efforts of the BROAD and MIPS institutes. As a result of the modifications to the genome and the recent findings that suggested a role for secreted proteins in virulence, the F. graminearum secretome was revisited. In the current study, a refined F. graminearum secretome was predicted by combining several bioinformatic approaches. This strategy increased the probability of identifying truly secreted proteins. A secretome of 574 proteins was predicted of which 99% was supported by transcriptional evidence. The function of the annotated and unannotated secreted proteins was explored. The potential role(s) of the annotated proteins including, putative enzymes, phytotoxins and antifungals are discussed. Characterisation of the unannotated proteins included the analysis of Pfam domains and features associated with known fungal effectors, for example, small size, cysteine-rich and containing internal amino acid repeats. A comprehensive comparative genomic analysis involving 57 fungal and oomycete genomes revealed that only a small number of the predicted F. graminearum secreted proteins can be considered to be either species or sequenced strain specific. PMID:22493673

Large scale RNAi screen in Tribolium reveals novel target genes for pest control and the proteasome as prime target.

PubMed

Ulrich, Julia; Dao, Van Anh; Majumdar, Upalparna; Schmitt-Engel, Christian; Schwirz, Jonas; Schultheis, Dorothea; Ströhlein, Nadi; Troelenberg, Nicole; Grossmann, Daniela; Richter, Tobias; Dönitz, Jürgen; Gerischer, Lizzy; Leboulle, Gérard; Vilcinskas, Andreas; Stanke, Mario; Bucher, Gregor

2015-09-03

Insect pest control is challenged by insecticide resistance and negative impact on ecology and health. One promising pest specific alternative is the generation of transgenic plants, which express double stranded RNAs targeting essential genes of a pest species. Upon feeding, the dsRNA induces gene silencing in the pest resulting in its death. However, the identification of efficient RNAi target genes remains a major challenge as genomic tools and breeding capacity is limited in most pest insects impeding whole-animal-high-throughput-screening. We use the red flour beetle Tribolium castaneum as a screening platform in order to identify the most efficient RNAi target genes. From about 5,000 randomly screened genes of the iBeetle RNAi screen we identify 11 novel and highly efficient RNAi targets. Our data allowed us to determine GO term combinations that are predictive for efficient RNAi target genes with proteasomal genes being most predictive. Finally, we show that RNAi target genes do not appear to act synergistically and that protein sequence conservation does not correlate with the number of potential off target sites. Our results will aid the identification of RNAi target genes in many pest species by providing a manageable number of excellent candidate genes to be tested and the proteasome as prime target. Further, the identified GO term combinations will help to identify efficient target genes from organ specific transcriptomes. Our off target analysis is relevant for the sequence selection used in transgenic plants.

Prognostic and predictive value of VHL gene alteration in renal cell carcinoma: a meta-analysis and review.

PubMed

Kim, Bum Jun; Kim, Jung Han; Kim, Hyeong Su; Zang, Dae Young

2017-02-21

The von Hippel-Lindau (VHL) gene is often inactivated in sporadic renal cell carcinoma (RCC) by mutation or promoter hypermethylation. The prognostic or predictive value of VHL gene alteration is not well established. We conducted this meta-analysis to evaluate the association between the VHL alteration and clinical outcomes in patients with RCC. We searched PUBMED, MEDLINE and EMBASE for articles including following terms in their titles, abstracts, or keywords: 'kidney or renal', 'carcinoma or cancer or neoplasm or malignancy', 'von Hippel-Lindau or VHL', 'alteration or mutation or methylation', and 'prognostic or predictive'. There were six studies fulfilling inclusion criteria and a total of 633 patients with clear cell RCC were included in the study: 244 patients who received anti-vascular endothelial growth factor (VEGF) therapy in the predictive value analysis and 419 in the prognostic value analysis. Out of 663 patients, 410 (61.8%) had VHL alteration. The meta-analysis showed no association between the VHL gene alteration and overall response rate (relative risk = 1.47 [95% CI, 0.81-2.67], P = 0.20) or progression free survival (hazard ratio = 1.02 [95% CI, 0.72-1.44], P = 0.91) in patients with RCC who received VEGF-targeted therapy. There was also no correlation between the VHL alteration and overall survival (HR = 0.80 [95% CI, 0.56-1.14], P = 0.21). In conclusion, this meta-analysis indicates that VHL gene alteration has no prognostic or predictive value in patients with clear cell RCC.

Chemical Structural Novelty: On-Targets and Off-Targets

PubMed Central

Yera, Emmanuel R.; Cleves, Ann. E.; Jain, Ajay N.

2011-01-01

Drug structures may be quantitatively compared based on 2D topological structural considerations and based on 3D characteristics directly related to binding. A framework for combining multiple similarity computations is presented along with its systematic application to 358 drugs with overlapping pharmacology. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, or their combination. For prediction of primary targets, the benefit of 3D over 2D was relatively small, but for prediction of off-targets, the added benefit was large. In addition to assessing prediction, the relationship between chemical similarity and pharmacological novelty was studied. Drug pairs that shared high 3D similarity but low 2D similarity (i.e. a novel scaffold) were shown to be much more likely to exhibit pharmacologically relevant differences in terms of specific protein target modulation. PMID:21916467

Predicting spillover risk to non-target plants pre-release: Bikasha collaris a potential biological control agent of Chinese tallowtree (Triadica sebifera)

USDA-ARS?s Scientific Manuscript database

Quarantine host range tests accurately predict direct risk of biological control agents to non-target species. However, a well-known indirect effect of biological control of weeds releases is spillover damage to non-target species. Spillover damage may occur when the population of agents achieves ou...

Monitoring urban subsidence based on SAR lnterferometric point target analysis

USGS Publications Warehouse

Zhang, Y.; Zhang, Jiahua; Gong, W.; Lu, Z.

2009-01-01

lnterferometric point target analysis (IPTA) is one of the latest developments in radar interferometric processing. It is achieved by analysis of the interferometric phases of some individual point targets, which are discrete and present temporarily stable backscattering characteristics, in long temporal series of interferometric SAR images. This paper analyzes the interferometric phase model of point targets, and then addresses two key issues within IPTA process. Firstly, a spatial searching method is proposed to unwrap the interferometric phase difference between two neighboring point targets. The height residual error and linear deformation rate of each point target can then be calculated, when a global reference point with known height correction and deformation history is chosen. Secondly, a spatial-temporal filtering scheme is proposed to further separate the atmosphere phase and nonlinear deformation phase from the residual interferometric phase. Finally, an experiment of the developed IPTA methodology is conducted over Suzhou urban area. Totally 38 ERS-1/2 SAR scenes are analyzed, and the deformation information over 3 546 point targets in the time span of 1992-2002 are generated. The IPTA-derived deformation shows very good agreement with the published result, which demonstrates that the IPTA technique can be developed into an operational tool to map the ground subsidence over urban area.

Identifying Drug-Target Interactions with Decision Templates.

PubMed

Yan, Xiao-Ying; Zhang, Shao-Wu

2018-01-01

During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can

An efficient hybrid technique in RCS predictions of complex targets at high frequencies

NASA Astrophysics Data System (ADS)

Algar, María-Jesús; Lozano, Lorena; Moreno, Javier; González, Iván; Cátedra, Felipe

2017-09-01

Most computer codes in Radar Cross Section (RCS) prediction use Physical Optics (PO) and Physical theory of Diffraction (PTD) combined with Geometrical Optics (GO) and Geometrical Theory of Diffraction (GTD). The latter approaches are computationally cheaper and much more accurate for curved surfaces, but not applicable for the computation of the RCS of all surfaces of a complex object due to the presence of caustic problems in the analysis of concave surfaces or flat surfaces in the far field. The main contribution of this paper is the development of a hybrid method based on a new combination of two asymptotic techniques: GTD and PO, considering the advantages and avoiding the disadvantages of each of them. A very efficient and accurate method to analyze the RCS of complex structures at high frequencies is obtained with the new combination. The proposed new method has been validated comparing RCS results obtained for some simple cases using the proposed approach and RCS using the rigorous technique of Method of Moments (MoM). Some complex cases have been examined at high frequencies contrasting the results with PO. This study shows the accuracy and the efficiency of the hybrid method and its suitability for the computation of the RCS at really large and complex targets at high frequencies.

TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

PubMed

Hu, Jun; Han, Ke; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

2016-11-01

The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the "intermediate" decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys .

Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma

PubMed Central

Akbari, Hamed; Macyszyn, Luke; Da, Xiao; Bilello, Michel; Wolf, Ronald L.; Martinez-Lage, Maria; Biros, George; Alonso-Basanta, Michelle; O’Rourke, Donald M.; Davatzikos, Christos

2016-01-01

Background Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging (MRI), which is insufficient for delineating surrounding infiltrating tumor. Objective To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. Methods Preoperative multiparametric MRIs (T1, T1-Gad, T2-weighted [T2], T2-fluid-attenuated inversion recovery [FLAIR], diffusion tensor imaging (DTI), and dynamic susceptibility contrast-enhanced [DSC]-MRI) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing likelihood of tumor infiltration and future early recurrence were compared to regions of recurrence on postresection follow-up studies with pathology confirmation. Results This technique produced predictions of early recurrence with a mean area under the curve (AUC) of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% CI, 8.95–9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. Conclusion Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved

Analysis of phase II studies on targeted agents and subsequent phase III trials: what are the predictors for success?

PubMed

Chan, John K; Ueda, Stefanie M; Sugiyama, Valerie E; Stave, Christopher D; Shin, Jacob Y; Monk, Bradley J; Sikic, Branimir I; Osann, Kathryn; Kapp, Daniel S

2008-03-20

To identify the characteristics of phase II studies that predict for subsequent "positive" phase III trials (those that reached the proposed primary end points of study or those wherein the study drug was superior to the standard regimen investigating targeted agents in advanced tumors. We identified all phase III clinical trials of targeted therapies against advanced cancers published from 1985 to 2005. Characteristics of the preceding phase II studies were reviewed to identify predictive factors for success of the subsequent phase III trial. Data were analyzed using the chi(2) test and logistic regression models. Of 351 phase II studies, 167 (47.6%) subsequent phase III trials were positive and 184 (52.4%) negative. Phase II studies from multiple rather than single institutions were more likely to precede a successful trial (60.4% v 39.4%; P < .001). Positive phase II results were more likely to lead to a successful phase III trial (50.8% v 22.5%; P = .003). The percentage of successful trials from pharmaceutical companies was significantly higher compared with academic, cooperative groups, and research institutes (89.5% v 44.2%, 45.2%, and 46.3%, respectively; P = .002). On multivariate analysis, these factors and shorter time interval between publication of phase II results and III study publication were independent predictive factors for a positive phase III trial. In phase II studies of targeted agents, multiple- versus single-institution participation, positive phase II trial, pharmaceutical company-based trials, and shorter time period between publication of phase II to phase III trial were independent predictive factors of success in a phase III trial. Investigators should be cognizant of these factors in phase II studies before designing phase III trials.

Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watney, W.L.

1994-12-01

Reservoirs in the Lansing-Kansas City limestone result from complex interactions among paleotopography (deposition, concurrent structural deformation), sea level, and diagenesis. Analysis of reservoirs and surface and near-surface analogs has led to developing a {open_quotes}strandline grainstone model{close_quotes} in which relative sea-level stabilized during regressions, resulting in accumulation of multiple grainstone buildups along depositional strike. Resulting stratigraphy in these carbonate units are generally predictable correlating to inferred topographic elevation along the shelf. This model is a valuable predictive tool for (1) locating favorable reservoirs for exploration, and (2) anticipating internal properties of the reservoir for field development. Reservoirs in the Lansing-Kansas Citymore » limestones are developed in both oolitic and bioclastic grainstones, however, re-analysis of oomoldic reservoirs provides the greatest opportunity for developing bypassed oil. A new technique, the {open_quotes}Super{close_quotes} Pickett crossplot (formation resistivity vs. porosity) and its use in an integrated petrophysical characterization, has been developed to evaluate extractable oil remaining in these reservoirs. The manual method in combination with 3-D visualization and modeling can help to target production limiting heterogeneities in these complex reservoirs and moreover compute critical parameters for the field such as bulk volume water. Application of this technique indicates that from 6-9 million barrels of Lansing-Kansas City oil remain behind pipe in the Victory-Northeast Lemon Fields. Petroleum geologists are challenged to quantify inferred processes to aid in developing rationale geologically consistent models of sedimentation so that acceptable levels of prediction can be obtained.« less

Lung adenocarcinoma in the era of targeted therapies: histological classification, sample prioritization, and predictive biomarkers.

PubMed

Conde, E; Angulo, B; Izquierdo, E; Paz-Ares, L; Belda-Iniesta, C; Hidalgo, M; López-Ríos, F

2013-07-01

The arrival of targeted therapies has presented both a conceptual and a practical challenge in the treatment of patients with advanced non-small cell lung carcinomas (NSCLCs). The relationship of these treatments with specific histologies and predictive biomarkers has made the handling of biopsies the key factor for success. In this study, we highlight the balance between precise histological diagnosis and the practice of conducting multiple predictive assays simultaneously. This can only be achieved where there is a commitment to multidisciplinary working by the tumor board to ensure that a sensible protocol is applied. This proposal for prioritizing samples includes both recent technological advances and the some of the latest discoveries in the molecular classification of NSCLCs.

Predicting Rediated Noise With Power Flow Finite Element Analysis

DTIC Science & Technology

2007-02-01

Defence R&D Canada – Atlantic DEFENCE DÉFENSE & Predicting Rediated Noise With Power Flow Finite Element Analysis D. Brennan T.S. Koko L. Jiang J...PREDICTING RADIATED NOISE WITH POWER FLOW FINITE ELEMENT ANALYSIS D.P. Brennan T.S. Koko L. Jiang J.C. Wallace Martec Limited Martec Limited...model- or full-scale data before it is available for general use. Brennan, D.P., Koko , T.S., Jiang, L., Wallace, J.C. 2007. Predicting Radiated

An enhanced MMW and SMMW/THz imaging system performance prediction and analysis tool for concealed weapon detection and pilotage obstacle avoidance

NASA Astrophysics Data System (ADS)

Murrill, Steven R.; Jacobs, Eddie L.; Franck, Charmaine C.; Petkie, Douglas T.; De Lucia, Frank C.

2015-10-01

The U.S. Army Research Laboratory (ARL) has continued to develop and enhance a millimeter-wave (MMW) and submillimeter- wave (SMMW)/terahertz (THz)-band imaging system performance prediction and analysis tool for both the detection and identification of concealed weaponry, and for pilotage obstacle avoidance. The details of the MATLAB-based model which accounts for the effects of all critical sensor and display components, for the effects of atmospheric attenuation, concealment material attenuation, and active illumination, were reported on at the 2005 SPIE Europe Security and Defence Symposium (Brugge). An advanced version of the base model that accounts for both the dramatic impact that target and background orientation can have on target observability as related to specular and Lambertian reflections captured by an active-illumination-based imaging system, and for the impact of target and background thermal emission, was reported on at the 2007 SPIE Defense and Security Symposium (Orlando). Further development of this tool that includes a MODTRAN-based atmospheric attenuation calculator and advanced system architecture configuration inputs that allow for straightforward performance analysis of active or passive systems based on scanning (single- or line-array detector element(s)) or staring (focal-plane-array detector elements) imaging architectures was reported on at the 2011 SPIE Europe Security and Defence Symposium (Prague). This paper provides a comprehensive review of a newly enhanced MMW and SMMW/THz imaging system analysis and design tool that now includes an improved noise sub-model for more accurate and reliable performance predictions, the capability to account for postcapture image contrast enhancement, and the capability to account for concealment material backscatter with active-illumination- based systems. Present plans for additional expansion of the model's predictive capabilities are also outlined.

Pan-genome analysis of human gastric pathogen H. pylori: comparative genomics and pathogenomics approaches to identify regions associated with pathogenicity and prediction of potential core therapeutic targets.

PubMed

Ali, Amjad; Naz, Anam; Soares, Siomar C; Bakhtiar, Marriam; Tiwari, Sandeep; Hassan, Syed S; Hanan, Fazal; Ramos, Rommel; Pereira, Ulisses; Barh, Debmalya; Figueiredo, Henrique César Pereira; Ussery, David W; Miyoshi, Anderson; Silva, Artur; Azevedo, Vasco

2015-01-01

Helicobacter pylori is a human gastric pathogen implicated as the major cause of peptic ulcer and second leading cause of gastric cancer (~70%) around the world. Conversely, an increased resistance to antibiotics and hindrances in the development of vaccines against H. pylori are observed. Pan-genome analyses of the global representative H. pylori isolates consisting of 39 complete genomes are presented in this paper. Phylogenetic analyses have revealed close relationships among geographically diverse strains of H. pylori. The conservation among these genomes was further analyzed by pan-genome approach; the predicted conserved gene families (1,193) constitute ~77% of the average H. pylori genome and 45% of the global gene repertoire of the species. Reverse vaccinology strategies have been adopted to identify and narrow down the potential core-immunogenic candidates. Total of 28 nonhost homolog proteins were characterized as universal therapeutic targets against H. pylori based on their functional annotation and protein-protein interaction. Finally, pathogenomics and genome plasticity analysis revealed 3 highly conserved and 2 highly variable putative pathogenicity islands in all of the H. pylori genomes been analyzed.

The search for drug-targetable diagnostic, prognostic and predictive biomarkers in chronic graft-versus-host disease.

PubMed

Ren, Hong-Gang; Adom, Djamilatou; Paczesny, Sophie

2018-05-01

Chronic graft-versus-host disease (cGVHD) continues to be the leading cause of late morbidity and mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT), which is an increasingly applied curative method for both benign and malignant hematologic disorders. Biomarker identification is crucial for the development of noninvasive and cost-effective cGVHD diagnostic, prognostic, and predictive test for use in clinic. Furthermore, biomarkers may help to gain a better insight on ongoing pathophysiological processes. The recent widespread application of omics technologies including genomics, transcriptomics, proteomics and cytomics provided opportunities to discover novel biomarkers. Areas covered: This review focuses on biomarkers identified through omics that play a critical role in target identification for drug development, and that were verified in at least two independent cohorts. It also summarizes the current status on omics tools used to identify these useful cGVHD targets. We briefly list the biomarkers identified and verified so far. We further address challenges associated to their exploitation and application in the management of cGVHD patients. Finally, insights on biomarkers that are drug targetable and represent potential therapeutic targets are discussed. Expert commentary: We focus on biomarkers that play an essential role in target identification.

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0

PubMed Central

2014-01-01

Background We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. Results We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. Conclusions TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool. PMID:25302078

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.

PubMed

Clark, Alex M; Sarker, Malabika; Ekins, Sean

2014-01-01

We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.

Analysis of uncertainties in turbine metal temperature predictions

NASA Technical Reports Server (NTRS)

Stepka, F. S.

1980-01-01

An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.

A mathematical analysis of multiple-target SELEX.

PubMed

Seo, Yeon-Jung; Chen, Shiliang; Nilsen-Hamilton, Marit; Levine, Howard A

2010-10-01

SELEX (Systematic Evolution of Ligands by Exponential Enrichment) is a procedure by which a mixture of nucleic acids can be fractionated with the goal of identifying those with specific biochemical activities. One combines the mixture with a specific target molecule and then separates the target-NA complex from the resulting reactions. The target-NA complex is separated from the unbound NA by mechanical means (such as by filtration), the NA is eluted from the complex, amplified by PCR (polymerase chain reaction), and the process repeated. After several rounds, one should be left with the nucleic acids that best bind to the target. The problem was first formulated mathematically in Irvine et al. (J. Mol. Biol. 222:739-761, 1991). In Levine and Nilsen-Hamilton (Comput. Biol. Chem. 31:11-25, 2007), a mathematical analysis of the process was given. In Vant-Hull et al. (J. Mol. Biol. 278:579-597, 1998), multiple target SELEX was considered. It was assumed that each target has a single nucleic acid binding site that permits occupation by no more than one nucleic acid. Here, we revisit Vant-Hull et al. (J. Mol. Biol. 278:579-597, 1998) using the same assumptions. The iteration scheme is shown to be convergent and a simplified algorithm is given. Our interest here is in the behavior of the multiple target SELEX process as a discrete "time" dynamical system. Our goal is to characterize the limiting states and their dependence on the initial distribution of nucleic acid and target fraction components. (In multiple target SELEX, we vary the target component fractions, but not their concentrations, as fixed and the initial pool of nucleic acids as a variable starting condition). Given N nucleic acids and a target consisting of M subtarget component species, there is an M × N matrix of affinities, the (i,j) entry corresponding to the affinity of the jth nucleic acid for the ith subtarget. We give a structure condition on this matrix that is equivalent to the following

A Waveform Detector that Targets Template-Decorrelated Signals and Achieves its Predicted Performance: Demonstration with IMS Data

NASA Astrophysics Data System (ADS)

Carmichael, J.

2016-12-01

Waveform correlation detectors used in seismic monitoring scan multichannel data to test two competing hypotheses: that data contain (1) a noisy, amplitude-scaled version of a template waveform, or, (2) only noise. In reality, seismic wavefields include signals triggered by non-target sources (background seismicity) and target signals that are only partially correlated with the waveform template. We reform the waveform correlation detector hypothesis test to accommodate deterministic uncertainty in template/target waveform similarity and thereby derive a new detector from convex set projections (the "cone detector") for use in explosion monitoring. Our analyses give probability density functions that quantify the detectors' degraded performance with decreasing waveform similarity. We then apply our results to three announced North Korean nuclear tests and use International Monitoring System (IMS) arrays to determine the probability that low magnitude, off-site explosions can be reliably detected with a given waveform template. We demonstrate that cone detectors provide (1) an improved predictive capability over correlation detectors to identify such spatially separated explosive sources, (2) competitive detection rates, and (3) reduced false alarms on background seismicity. Figure Caption: Observed and predicted receiver operating characteristic curves for correlation statistic r(x) (left) and cone statistic s(x) (right) versus semi-empirical explosion magnitude. a: Shaded region shows range of ROC curves for r(x) that give the predicted detection performance in noise conditions recorded over 24 hrs on 8 October 2006. Superimposed stair plot shows the empirical detection performance (recorded detections/total events) averaged over 24 hr of data. Error bars indicate the demeaned range in observed detection probability over the day; means are removed to avoid risk of misinterpreting range to indicate probabilities can exceed one. b: Shaded region shows range of ROC

TARGET - TASK ANALYSIS REPORT GENERATION TOOL, VERSION 1.0

NASA Technical Reports Server (NTRS)

Ortiz, C. J.

1994-01-01

The Task Analysis Report Generation Tool, TARGET, is a graphical interface tool used to capture procedural knowledge and translate that knowledge into a hierarchical report. TARGET is based on VISTA, a knowledge acquisition tool developed by the Naval Systems Training Center. TARGET assists a programmer and/or task expert organize and understand the steps involved in accomplishing a task. The user can label individual steps in the task through a dialogue-box and get immediate graphical feedback for analysis. TARGET users can decompose tasks into basic action kernels or minimal steps to provide a clear picture of all basic actions needed to accomplish a job. This method allows the user to go back and critically examine the overall flow and makeup of the process. The user can switch between graphics (box flow diagrams) and text (task hierarchy) versions to more easily study the process being documented. As the practice of decomposition continues, tasks and their subtasks can be continually modified to more accurately reflect the user's procedures and rationale. This program is designed to help a programmer document an expert's task thus allowing the programmer to build an expert system which can help others perform the task. Flexibility is a key element of the system design and of the knowledge acquisition session. If the expert is not able to find time to work on the knowledge acquisition process with the program developer, the developer and subject matter expert may work in iterative sessions. TARGET is easy to use and is tailored to accommodate users ranging from the novice to the experienced expert systems builder. TARGET is written in C-language for IBM PC series and compatible computers running MS-DOS and Microsoft Windows version 3.0 or 3.1. No source code is supplied. The executable also requires 2Mb of RAM, a Microsoft compatible mouse, a VGA display and an 80286, 386 or 486 processor machine. The standard distribution medium for TARGET is one 5.25 inch 360K

[Segment analysis of the target market of physiotherapeutic services].

PubMed

Babaskin, D V

2010-01-01

The objective of the present study was to demonstrate the possibilities to analyse selected segments of the target market of physiotherapeutic services provided by medical and preventive-facilities of two major types. The main features of a target segment, such as provision of therapeutic massage, are illustrated in terms of two characteristics, namely attractiveness to the users and the ability of a given medical facility to satisfy their requirements. Based on the analysis of portfolio of the available target segments the most promising ones (winner segments) were selected for further marketing studies. This choice does not exclude the possibility of involvement of other segments of medical services in marketing activities.

Radiomic analysis in prediction of Human Papilloma Virus status.

PubMed

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

Label-Free Raman Microspectral Analysis for Comparison of Cellular Uptake and Distribution between Non-Targeted and EGFR-Targeted Biodegradable Polymeric Nanoparticles

PubMed Central

Chernenko, Tatyana; Buyukozturk, Fulden; Miljkovic, Milos; Carrier, Rebecca; Diem, Max; Amiji, Mansoor

2013-01-01

Active targeted delivery of nanoparticle-encapsulated agents to tumor cells in vivo is expected to enhance therapeutic effect with significantly less non-specific toxicity. Active targeting is based on surface modification of nanoparticles with ligands that bind with extracellular targets and enhance payload delivery in the cells. In this study, we have used label-free Raman micro-spectral analysis and kinetic modeling to study cellular interactions and intracellular delivery of C6-ceramide using a non-targeted and an epidermal growth factor receptor (EGFR) targeted biodegradable polymeric nano-delivery systems, in EGFR-expressing human ovarian adenocarcinoma (SKOV3) cells. The results show that EGFR peptide-modified nanoparticles were rapidly internalized in SKOV3 cells leading to significant intracellular accumulation as compared to non-specific uptake by the non-targeted nanoparticles. Raman micro-spectral analysis enables visualization and quantification of the carrier system, drug-load, and responses of the biological systems interrogated, without exogenous staining and labeling procedures. PMID:24298430

Predictive value of bacterial analysis of laparotomy wounds.

PubMed

Minutolo, Mario; Blandino, Giovanna; Arena, Manuel; Licciardello, Alessio; Di Stefano, Biagio; Lanteri, Raffaele; Puleo, Stefano; Licata, Antonio; Minutolo, Vincenzo

2014-01-01

Despite improvements in antibiotic prophylaxis, surgical site infections represent the most common postoperative complication with important clinical consequences for patients. The hypothesis that a bacterial analysis of the surgical wound in the operating room could predict the likelihood of developing a clinical infection, and might allow a tailored and preemptive approach, aimed to reduce the consequences of an infection, seems appealing. We would like to present a prospective study on the predictive value of the bacterial analysis of laparotomy wounds. Seventy eight prospective patients undergoing surgery were included in the study. To evaluate the risk factors associated with increased rate of wound infection, we performed a bacterial analysis of the wound. 48 patients out of 78 (61%) had positive cultures. 23 patients out of 32 patients (72%) who didn't receive antibiotic prophylaxis were positive to the wound culture whereas 25 patients out of 46 patients (54%) grew positive cultures in the group of patients that received antibiotic prophylaxis. None of the 30 patients with negative cultures developed clinical infection. Only 6 patients out of 48 patients who had positive cultures (12.5%) developed wound infection. Clinical infection occurred in 5 patients who had gram-negative contamination of the wound. No clinical infection occurred in patients who had gram-positive contamination. Wound cultures and their positivity are predictive tools to identify the patients that are at risk to develop wound infection. The positive predictive value of the bacterial analysis of the wound was 12.5%. Abdominal surgery, Bacterial analysis, Wound infection.

Tools for in silico target fishing.

PubMed

Cereto-Massagué, Adrià; Ojeda, María José; Valls, Cristina; Mulero, Miquel; Pujadas, Gerard; Garcia-Vallve, Santiago

2015-01-01

Computational target fishing methods are designed to identify the most probable target of a query molecule. This process may allow the prediction of the bioactivity of a compound, the identification of the mode of action of known drugs, the detection of drug polypharmacology, drug repositioning or the prediction of the adverse effects of a compound. The large amount of information regarding the bioactivity of thousands of small molecules now allows the development of these types of methods. In recent years, we have witnessed the emergence of many methods for in silico target fishing. Most of these methods are based on the similarity principle, i.e., that similar molecules might bind to the same targets and have similar bioactivities. However, the difficult validation of target fishing methods hinders comparisons of the performance of each method. In this review, we describe the different methods developed for target prediction, the bioactivity databases most frequently used by these methods, and the publicly available programs and servers that enable non-specialist users to obtain these types of predictions. It is expected that target prediction will have a large impact on drug development and on the functional food industry. Copyright © 2014 Elsevier Inc. All rights reserved.

Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets.

PubMed

Tichy, Diana; Pickl, Julia Maria Anna; Benner, Axel; Sültmann, Holger

2017-03-31

The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years; however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking.Here, we investigate the experimental design for Ago-RIP-Seq and examine biostatistical methods to identify de novo miRNA target genes. Statistical approaches considered are either based on a negative binomial model fit to the read count data or applied to transformed data using a normal distribution-based generalized linear model. We compare them by a real data simulation study using plasmode data sets and evaluate the suitability of the approaches to detect true miRNA targets by sensitivity and false discovery rates. Our results suggest that simple approaches like linear regression models on (appropriately) transformed read count data are preferable. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

PubMed

Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

2017-12-12

Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

Top-attack modeling and automatic target detection using synthetic FLIR scenery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Penn, Joseph A.

2004-09-01

A series of experiments have been performed to verify the utility of algorithmic tools for the modeling and analysis of cold-target signatures in synthetic, top-attack, FLIR video sequences. The tools include: MuSES/CREATION for the creation of synthetic imagery with targets, an ARL target detection algorithm to detect imbedded synthetic targets in scenes, and an ARL scoring algorithm, using Receiver-Operating-Characteristic (ROC) curve analysis, to evaluate detector performance. Cold-target detection variability was examined as a function of target emissivity, surrounding clutter type, and target placement in non-obscuring clutter locations. Detector metrics were also individually scored so as to characterize the effect of signature/clutter variations. Results show that using these tools, a detailed, physically meaningful, target detection analysis is possible and that scenario specific target detectors may be developed by selective choice and/or weighting of detector metrics. However, developing these tools into a reliable predictive capability will require the extension of these results to the modeling and analysis of a large number of data sets configured for a wide range of target and clutter conditions. Finally, these tools should also be useful for the comparison of competitive detection algorithms by providing well defined, and controllable target detection scenarios, as well as for the training and testing of expert human observers.

Making predictions skill level analysis

NASA Astrophysics Data System (ADS)

Katarína, Krišková; Marián, Kireš

2017-01-01

The current trend in the education is focused on skills that are cross-subject and have a great importance for the pupil future life. Pupils should acquire different types of skills during their education to be prepared for future careers and life in the 21st century. Physics as a subject offers many opportunities for pupils' skills development. One of the skills that are expected to be developed in physics and also in other sciences is making predictions. The prediction, in the meaning of the argument about what may happen in the future, is an integral part of the empirical cognition, in which students confront existing knowledge and experience with new, hitherto unknown and surprising phenomena. The extent of the skill is the formulation of hypotheses, which is required in the upper secondary physics education. In the contribution, the prediction skill is specified and its eventual levels are classified. Authors focus on the tools for skill level determination based on the analysis of pupils` worksheets. Worksheets are the part of the educational activities conducted within the Inquiry Science Laboratory Steelpark. Based on the formulation of pupils' prediction the pupils thinking can be seen and their understanding of the topic, as well as preconceptions and misconceptions.

RobOKoD: microbial strain design for (over)production of target compounds.

PubMed

Stanford, Natalie J; Millard, Pierre; Swainston, Neil

2015-01-01

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.

Rotation to a Partially Specified Target Matrix in Exploratory Factor Analysis: How Many Targets?

ERIC Educational Resources Information Center

Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying

2013-01-01

The purpose of this study was to explore the influence of the number of targets specified on the quality of exploratory factor analysis solutions with a complex underlying structure and incomplete substantive measurement theory. Three Monte Carlo studies were performed based on the ratio of the number of observed variables to the number of…

Reduction of interferences in the analysis of Children's Dimetapp using ultraviolet spectroscopy data and target factor analysis

NASA Astrophysics Data System (ADS)

Msimanga, Huggins Z.; Lam, Truong Thach Ho; Latinwo, Nathaniel; Song, Mihyang Kristy; Tavakoli, Newsha

2018-03-01

A calibration matrix has been developed and successfully applied to quantify actives in Children's Dimetapp®, a cough mixture whose active components suffer from heavy spectral interference. High-performance liquid chromatography/photodiode array instrument was used to identify the actives and any other UV-detectable excipients that might contribute to interferences. The instrument was also used to obtain reference data on the actives, instead of relying on the manufacturer's claims. Principal component analysis was used during the developmental stages of the calibration matrix to highlight any mismatch between the calibration and sample spectra, making certain that "apples" were not compared with "oranges". The prediction model was finally calculated using target factor analysis and partial least squares regression. In addition to the actives in Children's Dimetapp® (brompheniramine maleate, phenylephrine hydrogen chloride, and dextromethorphan hydrogen bromide), sodium benzoate was identified as the major and FD&C Blue #1, FD&C Red #40, and methyl anthranilate as minor spectral interferences. Model predictions were compared before and after the interferences were included into the calibration matrix. Before including interferences, the following results were obtained: brompheniramine maleate = 481.3 mg L- 1 ± 134% RE; phenylephrine hydrogen chloride = 1041 mg L- 1 ± 107% RE; dextromethorphan hydrogen bromide = 1571 mg L- 1 ± 107% RE, where % RE = percent relative error based on the reference HPLC data. After including interferences, the results were as follows: brompheniramine maleate = 196.3 mg L- 1 ± 4.4% RE; phenylephrine hydrogen chloride = 501.3 mg L- 1 ± 0.10% RE; dextromethorphan hydrogen bromide = 998.7 mg L- 1 ± 1.6% RE as detailed in Table 6.

Failure mode analysis to predict product reliability.

NASA Technical Reports Server (NTRS)

Zemanick, P. P.

1972-01-01

The failure mode analysis (FMA) is described as a design tool to predict and improve product reliability. The objectives of the failure mode analysis are presented as they influence component design, configuration selection, the product test program, the quality assurance plan, and engineering analysis priorities. The detailed mechanics of performing a failure mode analysis are discussed, including one suggested format. Some practical difficulties of implementation are indicated, drawn from experience with preparing FMAs on the nuclear rocket engine program.

Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

NASA Astrophysics Data System (ADS)

Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

2018-03-01

Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2D seismic reflection data processing flow focused on pre - stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching (BHM), to estimate the uncertainties of the depths of key horizons near the borehole DSDP-258 located in the Mentelle Basin, south west of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ± 2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program (IODP), leg 369.

Integrative ChIP-seq/microarray analysis identifies a CTNNB1 target signature enriched in intestinal stem cells and colon cancer.

PubMed

Watanabe, Kazuhide; Biesinger, Jacob; Salmans, Michael L; Roberts, Brian S; Arthur, William T; Cleary, Michele; Andersen, Bogi; Xie, Xiaohui; Dai, Xing

2014-01-01

Deregulation of canonical Wnt/CTNNB1 (beta-catenin) pathway is one of the earliest events in the pathogenesis of colon cancer. Mutations in APC or CTNNB1 are highly frequent in colon cancer and cause aberrant stabilization of CTNNB1, which activates the transcription of Wnt target genes by binding to chromatin via the TCF/LEF transcription factors. Here we report an integrative analysis of genome-wide chromatin occupancy of CTNNB1 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and gene expression profiling by microarray analysis upon RNAi-mediated knockdown of CTNNB1 in colon cancer cells. We observed 3629 CTNNB1 binding peaks across the genome and a significant correlation between CTNNB1 binding and knockdown-induced gene expression change. Our integrative analysis led to the discovery of a direct Wnt target signature composed of 162 genes. Gene ontology analysis of this signature revealed a significant enrichment of Wnt pathway genes, suggesting multiple feedback regulations of the pathway. We provide evidence that this gene signature partially overlaps with the Lgr5+ intestinal stem cell signature, and is significantly enriched in normal intestinal stem cells as well as in clinical colorectal cancer samples. Interestingly, while the expression of the CTNNB1 target gene set does not correlate with survival, elevated expression of negative feedback regulators within the signature predicts better prognosis. Our data provide a genome-wide view of chromatin occupancy and gene regulation of Wnt/CTNNB1 signaling in colon cancer cells.

In silico pharmacology for drug discovery: applications to targets and beyond

PubMed Central

Ekins, S; Mestres, J; Testa, B

2007-01-01

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets. PMID:17549046

Prognostic and predictive value of VHL gene alteration in renal cell carcinoma: a meta-analysis and review

PubMed Central

Kim, Bum Jun; Kim, Jung Han; Kim, Hyeong Su; Zang, Dae Young

2017-01-01

The von Hippel-Lindau (VHL) gene is often inactivated in sporadic renal cell carcinoma (RCC) by mutation or promoter hypermethylation. The prognostic or predictive value of VHL gene alteration is not well established. We conducted this meta-analysis to evaluate the association between the VHL alteration and clinical outcomes in patients with RCC. We searched PUBMED, MEDLINE and EMBASE for articles including following terms in their titles, abstracts, or keywords: ‘kidney or renal’, ‘carcinoma or cancer or neoplasm or malignancy’, ‘von Hippel-Lindau or VHL’, ‘alteration or mutation or methylation’, and ‘prognostic or predictive’. There were six studies fulfilling inclusion criteria and a total of 633 patients with clear cell RCC were included in the study: 244 patients who received anti-vascular endothelial growth factor (VEGF) therapy in the predictive value analysis and 419 in the prognostic value analysis. Out of 663 patients, 410 (61.8%) had VHL alteration. The meta-analysis showed no association between the VHL gene alteration and overall response rate (relative risk = 1.47 [95% CI, 0.81-2.67], P = 0.20) or progression free survival (hazard ratio = 1.02 [95% CI, 0.72-1.44], P = 0.91) in patients with RCC who received VEGF-targeted therapy. There was also no correlation between the VHL alteration and overall survival (HR = 0.80 [95% CI, 0.56-1.14], P = 0.21). In conclusion, this meta-analysis indicates that VHL gene alteration has no prognostic or predictive value in patients with clear cell RCC. PMID:28103578

Prediction of TF target sites based on atomistic models of protein-DNA complexes

PubMed Central

Angarica, Vladimir Espinosa; Pérez, Abel González; Vasconcelos, Ana T; Collado-Vides, Julio; Contreras-Moreira, Bruno

2008-01-01

Background The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence. Results Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models. Conclusion Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition. PMID:18922190

Nuclear Security: Target Analysis-rev

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Surinder Paul; Gibbs, Philip W.; Bultz, Garl A.

2014-03-01

The objectives of this presentation are to understand target identification, including roll-up and protracted theft; evaluate target identification in the SNRI; recognize the target characteristics and consequence levels; and understand graded safeguards.

[Analysis on "component-target-pathway" of Paeonia lactiflora in treating cardiac diseases based on data mining].

PubMed

Liu, Yang; Zhang, Fang-Bo; Tang, Shi-Huan; Wang, Ping; Li, Sen; Su, Jin; Zhou, Rong-Rong; Zhang, Jia-Qi; Sun, Hui-Feng

2018-04-01

Based on the literature review and modern application of Paeonia lactiflora in heart diseases, this article would predict the target of drug and disease by intergrative pharmacology platform of traditional Chinese medicine (TCMIP, http://www.tcmip.cn), and then explore the molecular mechanism of P. lactiflora in treatment of heart disease, providing theoretical basis and method for further studies on P. lactiflora. According to the ancient books, P. lactiflora with functions of "removing the vascular obstruction, removing the lumps, relieving pain, diuretic, nutrient qi" and other effects, have been used for many times to treat heart disease. Some prescriptions are also favored by the modern physicians nowadays. With the development of science, the chemical components that play a role in heart disease and the interrelation between these components and the body become the research hotspot. In order to further reveal the pharmacological substance base and molecular mechanism of P. lactiflora for the treatment of such diseases, TCM-IP was used to obtain multiple molecular targets and signaling pathways in treatment of heart disease. ATP1A1, a common target of drug and disease, was related to energy, and HDAC2 mainly regulated cardiomyocyte hypertrophy gene and cardiomyocyte expression. Other main drug targets such as GCK, CHUK and PRKAA2 indirectly regulated heart disease through many pathways; multiple disease-associated signaling pathways interfered with various heart diseases including coronary heart disease, myocardial ischemia and myocardial hypertrophy through influencing energy metabolism, enzyme activity and gene expression. In conclusion, P. lactiflora plays a role in protecting heart function by regulating the gene expression of cardiomyocytes directly. Meanwhile, it can indirectly intervene in other pathways of heart function, and thus participate in the treatment of heart disease. In this paper, the molecular mechanism of P. lactiflora for treatment of

A computational approach for predicting off-target toxicity of antiviral ribonucleoside analogues to mitochondrial RNA polymerase.

PubMed

Freedman, Holly; Winter, Philip; Tuszynski, Jack; Tyrrell, D Lorne; Houghton, Michael

2018-06-22

In the development of antiviral drugs that target viral RNA-dependent RNA polymerases, off-target toxicity caused by the inhibition of the human mitochondrial RNA polymerase (POLRMT) is a major liability. Therefore, it is essential that all new ribonucleoside analogue drugs be accurately screened for POLRMT inhibition. A computational tool that can accurately predict NTP binding to POLRMT could assist in evaluating any potential toxicity and in designing possible salvaging strategies. Using the available crystal structure of POLRMT bound to an RNA transcript, here we created a model of POLRMT with an NTP molecule bound in the active site. Furthermore, we implemented a computational screening procedure that determines the relative binding free energy of an NTP analogue to POLRMT by free energy perturbation (FEP), i.e. a simulation in which the natural NTP molecule is slowly transformed into the analogue and back. In each direction, the transformation was performed over 40 ns of simulation on our IBM Blue Gene Q supercomputer. This procedure was validated across a panel of drugs for which experimental dissociation constants were available, showing that NTP relative binding free energies could be predicted to within 0.97 kcal/mol of the experimental values on average. These results demonstrate for the first time that free-energy simulation can be a useful tool for predicting binding affinities of NTP analogues to a polymerase. We expect that our model, together with similar models of viral polymerases, will be very useful in the screening and future design of NTP inhibitors of viral polymerases that have no mitochondrial toxicity. © 2018 Freedman et al.

Prediction and functional analysis of the sweet orange protein-protein interaction network.

PubMed

Ding, Yu-Duan; Chang, Ji-Wei; Guo, Jing; Chen, Dijun; Li, Sen; Xu, Qiang; Deng, Xiu-Xin; Cheng, Yun-Jiang; Chen, Ling-Ling

2014-08-05

Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species. In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks. Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

MEM spectral analysis for predicting influenza epidemics in Japan.

PubMed

Sumi, Ayako; Kamo, Ken-ichi

2012-03-01

The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan. The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977. On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively. Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

The siRNA Non-seed Region and Its Target Sequences Are Auxiliary Determinants of Off-Target Effects.

PubMed

Kamola, Piotr J; Nakano, Yuko; Takahashi, Tomoko; Wilson, Paul A; Ui-Tei, Kumiko

2015-12-01

RNA interference (RNAi) is a powerful tool for post-transcriptional gene silencing. However, the siRNA guide strand may bind unintended off-target transcripts via partial sequence complementarity by a mechanism closely mirroring micro RNA (miRNA) silencing. To better understand these off-target effects, we investigated the correlation between sequence features within various subsections of siRNA guide strands, and its corresponding target sequences, with off-target activities. Our results confirm previous reports that strength of base-pairing in the siRNA seed region is the primary factor determining the efficiency of off-target silencing. However, the degree of downregulation of off-target transcripts with shared seed sequence is not necessarily similar, suggesting that there are additional auxiliary factors that influence the silencing potential. Here, we demonstrate that both the melting temperature (Tm) in a subsection of siRNA non-seed region, and the GC contents of its corresponding target sequences, are negatively correlated with the efficiency of off-target effect. Analysis of experimentally validated miRNA targets demonstrated a similar trend, indicating a putative conserved mechanistic feature of seed region-dependent targeting mechanism. These observations may prove useful as parameters for off-target prediction algorithms and improve siRNA 'specificity' design rules.

Meta-Analysis of PECS with Individuals with ASD: Investigation of Targeted versus Non-Targeted Outcomes, Participant Characteristics, and Implementation Phase

ERIC Educational Resources Information Center

Ganz, Jennifer B.; Davis, John L.; Lund, Emily M.; Goodwyn, Fara D.; Simpson, Richard L.

2012-01-01

The Picture Exchange Communication System (PECS) is a widely used picture/icon aided augmentative communication system designed for learners with autism and other developmental disorders. This meta-analysis analyzes the extant empirical literature for PECS relative to targeted (functional communication) and non-targeted concomitant outcomes…

Effects of target typicality on categorical search.

PubMed

Maxfield, Justin T; Stalder, Westri D; Zelinsky, Gregory J

2014-10-01

The role of target typicality in a categorical visual search task was investigated by cueing observers with a target name, followed by a five-item target present/absent search array in which the target images were rated in a pretest to be high, medium, or low in typicality with respect to the basic-level target cue. Contrary to previous work, we found that search guidance was better for high-typicality targets compared to low-typicality targets, as measured by both the proportion of immediate target fixations and the time to fixate the target. Consistent with previous work, we also found an effect of typicality on target verification times, the time between target fixation and the search judgment; as target typicality decreased, verification times increased. To model these typicality effects, we trained Support Vector Machine (SVM) classifiers on the target categories, and tested these on the corresponding specific targets used in the search task. This analysis revealed significant differences in classifier confidence between the high-, medium-, and low-typicality groups, paralleling the behavioral results. Collectively, these findings suggest that target typicality broadly affects both search guidance and verification, and that differences in typicality can be predicted by distance from an SVM classification boundary. © 2014 ARVO.

Integrative Analysis Reveals an Outcome-associated and Targetable Pattern of p53 and Cell Cycle Deregulation in Diffuse Large B-cell Lymphoma

PubMed Central

Monti, Stefano; Chapuy, Bjoern; Takeyama, Kunihiko; Rodig, Scott J; Hao, Yangsheng; Yeda, Kelly T.; Inguilizian, Haig; Mermel, Craig; Curie, Treeve; Dogan, Ahmed; Kutok, Jeffery L; Beroukim, Rameen; Neuberg, Donna; Habermann, Thomas; Getz, Gad; Kung, Andrew L; Golub, Todd R; Shipp, Margaret A

2013-01-01

Summary Diffuse large B-cell lymphoma (DLBCL) is a clinically and biologically heterogeneous disease with a high proliferation rate. By integrating copy number data with transcriptional profiles and performing pathway analysis in primary DLBCLs, we identified a comprehensive set of copy number alterations (CNAs) that decreased p53 activity and perturbed cell cycle regulation. Primary tumors either had multiple complementary alterations of p53 and cell cycle components or largely lacked these lesions. DLBCLs with p53 and cell cycle pathway CNAs had decreased abundance of p53 target transcripts and increased expression of E2F target genes and the Ki67 proliferation marker. CNAs of the CDKN2A-TP53-RB-E2F axis provide a structural basis for increased proliferation in DLBCL, predict outcome with current therapy and suggest targeted treatment approaches. PMID:22975378

Dual-targeting siRNAs

PubMed Central

Tiemann, Katrin; Höhn, Britta; Ehsani, Ali; Forman, Stephen J.; Rossi, John J.; Sætrom, Pål

2010-01-01

We have developed an algorithm for the prediction of dual-targeting short interfering RNAs (siRNAs) in which both strands are deliberately designed to separately target different mRNA transcripts with complete complementarity. An advantage of this approach versus the use of two separate duplexes is that only two strands, as opposed to four, are competing for entry into the RNA-induced silencing complex. We chose to design our dual-targeting siRNAs as Dicer substrate 25/27mer siRNAs, since design features resembling pre-microRNAs (miRNAs) can be introduced for Dicer processing. Seven different dual-targeting siRNAs targeting genes that are potential targets in cancer therapy have been developed including Bcl2, Stat3, CCND1, BIRC5, and MYC. The dual-targeting siRNAs have been characterized for dual target knockdown in three different cell lines (HEK293, HCT116, and PC3), where they were as effective as their corresponding single-targeting siRNAs in target knockdown. The algorithm developed in this study should prove to be useful for predicting dual-targeting siRNAs in a variety of different targets and is available from http://demo1.interagon.com/DualTargeting/. PMID:20410240

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

Linking removal targets to the ecological effects of invaders: a predictive model and field test.

PubMed

Green, Stephanie J; Dulvy, Nicholas K; Brooks, Annabelle M L; Akins, John L; Cooper, Andrew B; Miller, Skylar; Côté, Isabelle M

Species invasions have a range of negative effects on recipient ecosystems, and many occur at a scale and magnitude that preclude complete eradication. When complete extirpation is unlikely with available management resources, an effective strategy may be to suppress invasive populations below levels predicted to cause undesirable ecological change. We illustrated this approach by developing and testing targets for the control of invasive Indo-Pacific lionfish (Pterois volitans and P. miles) on Western Atlantic coral reefs. We first developed a size-structured simulation model of predation by lionfish on native fish communities, which we used to predict threshold densities of lionfish beyond which native fish biomass should decline. We then tested our predictions by experimentally manipulating lionfish densities above or below reef-specific thresholds, and monitoring the consequences for native fish populations on 24 Bahamian patch reefs over 18 months. We found that reducing lionfish below predicted threshold densities effectively protected native fish community biomass from predation-induced declines. Reductions in density of 25–92%, depending on the reef, were required to suppress lionfish below levels predicted to overconsume prey. On reefs where lionfish were kept below threshold densities, native prey fish biomass increased by 50–70%. Gains in small (<6 cm) size classes of native fishes translated into lagged increases in larger size classes over time. The biomass of larger individuals (>15 cm total length), including ecologically important grazers and economically important fisheries species, had increased by 10–65% by the end of the experiment. Crucially, similar gains in prey fish biomass were realized on reefs subjected to partial and full removal of lionfish, but partial removals took 30% less time to implement. By contrast, the biomass of small native fishes declined by >50% on all reefs with lionfish densities exceeding reef-specific thresholds

RobOKoD: microbial strain design for (over)production of target compounds

PubMed Central

Stanford, Natalie J.; Millard, Pierre; Swainston, Neil

2015-01-01

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design. PMID:25853130

Analysis of Infrared Signature Variation and Robust Filter-Based Supersonic Target Detection

PubMed Central

Sun, Sun-Gu; Kim, Kyung-Tae

2014-01-01

The difficulty of small infrared target detection originates from the variations of infrared signatures. This paper presents the fundamental physics of infrared target variations and reports the results of variation analysis of infrared images acquired using a long wave infrared camera over a 24-hour period for different types of backgrounds. The detection parameters, such as signal-to-clutter ratio were compared according to the recording time, temperature and humidity. Through variation analysis, robust target detection methodologies are derived by controlling thresholds and designing a temporal contrast filter to achieve high detection rate and low false alarm rate. Experimental results validate the robustness of the proposed scheme by applying it to the synthetic and real infrared sequences. PMID:24672290

Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas' disease treatment.

PubMed

Capriles, Priscila V S Z; Guimarães, Ana C R; Otto, Thomas D; Miranda, Antonio B; Dardenne, Laurent E; Degrave, Wim M

2010-10-29

Trypanosoma cruzi is the etiological agent of Chagas' disease, an endemic infection that causes thousands of deaths every year in Latin America. Therapeutic options remain inefficient, demanding the search for new drugs and/or new molecular targets. Such efforts can focus on proteins that are specific to the parasite, but analogous enzymes and enzymes with a three-dimensional (3D) structure sufficiently different from the corresponding host proteins may represent equally interesting targets. In order to find these targets we used the workflows MHOLline and AnEnΠ obtaining 3D models from homologous, analogous and specific proteins of Trypanosoma cruzi versus Homo sapiens. We applied genome wide comparative modelling techniques to obtain 3D models for 3,286 predicted proteins of T. cruzi. In combination with comparative genome analysis to Homo sapiens, we were able to identify a subset of 397 enzyme sequences, of which 356 are homologous, 3 analogous and 38 specific to the parasite. In this work, we present a set of 397 enzyme models of T. cruzi that can constitute potential structure-based drug targets to be investigated for the development of new strategies to fight Chagas' disease. The strategies presented here support the concept of structural analysis in conjunction with protein functional analysis as an interesting computational methodology to detect potential targets for structure-based rational drug design. For example, 2,4-dienoyl-CoA reductase (EC 1.3.1.34) and triacylglycerol lipase (EC 3.1.1.3), classified as analogous proteins in relation to H. sapiens enzymes, were identified as new potential molecular targets.

Integrative ChIP-seq/Microarray Analysis Identifies a CTNNB1 Target Signature Enriched in Intestinal Stem Cells and Colon Cancer

PubMed Central

Watanabe, Kazuhide; Biesinger, Jacob; Salmans, Michael L.; Roberts, Brian S.; Arthur, William T.; Cleary, Michele; Andersen, Bogi; Xie, Xiaohui; Dai, Xing

2014-01-01

Background Deregulation of canonical Wnt/CTNNB1 (beta-catenin) pathway is one of the earliest events in the pathogenesis of colon cancer. Mutations in APC or CTNNB1 are highly frequent in colon cancer and cause aberrant stabilization of CTNNB1, which activates the transcription of Wnt target genes by binding to chromatin via the TCF/LEF transcription factors. Here we report an integrative analysis of genome-wide chromatin occupancy of CTNNB1 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and gene expression profiling by microarray analysis upon RNAi-mediated knockdown of CTNNB1 in colon cancer cells. Results We observed 3629 CTNNB1 binding peaks across the genome and a significant correlation between CTNNB1 binding and knockdown-induced gene expression change. Our integrative analysis led to the discovery of a direct Wnt target signature composed of 162 genes. Gene ontology analysis of this signature revealed a significant enrichment of Wnt pathway genes, suggesting multiple feedback regulations of the pathway. We provide evidence that this gene signature partially overlaps with the Lgr5+ intestinal stem cell signature, and is significantly enriched in normal intestinal stem cells as well as in clinical colorectal cancer samples. Interestingly, while the expression of the CTNNB1 target gene set does not correlate with survival, elevated expression of negative feedback regulators within the signature predicts better prognosis. Conclusion Our data provide a genome-wide view of chromatin occupancy and gene regulation of Wnt/CTNNB1 signaling in colon cancer cells. PMID:24651522

Comprehensive analysis of microRNA-Seq and target mRNAs of rice sheath blight pathogen provides new insights into pathogenic regulatory mechanisms.

PubMed

Lin, Runmao; He, Liye; He, Jiayu; Qin, Peigang; Wang, Yanran; Deng, Qiming; Yang, Xiaoting; Li, Shuangcheng; Wang, Shiquan; Wang, Wenming; Liu, Huainian; Li, Ping; Zheng, Aiping

2016-07-03

MicroRNAs (miRNAs) are ∼22 nucleotide non-coding RNAs that regulate gene expression by targeting mRNAs for degradation or inhibiting protein translation. To investigate whether miRNAs regulate the pathogenesis in necrotrophic fungus Rhizoctonia solani AG1 IA, which causes significant yield loss in main economically important crops, and to determine the regulatory mechanism occurring during pathogenesis, we constructed hyphal small RNA libraries from six different infection periods of the rice leaf. Through sequencing and analysis, 177 miRNA-like small RNAs (milRNAs) were identified, including 15 candidate pathogenic novel milRNAs predicted by functional annotations of their target mRNAs and expression patterns of milRNAs and mRNAs during infection. Reverse transcription-quantitative polymerase chain reaction results for randomly selected milRNAs demonstrated that our novel comprehensive predictions had a high level of accuracy. In our predicted pathogenic protein-protein interaction network of R. solani, we added the related regulatory milRNAs of these core coding genes into the network, and could understand the relationships among these regulatory factors more clearly at the systems level. Furthermore, the putative pathogenic Rhi-milR-16, which negatively regulates target gene expression, was experimentally validated to have regulatory functions by a dual-luciferase reporter assay. Additionally, 23 candidate rice miRNAs that may involve in plant immunity against R. solani were discovered. This first study on novel pathogenic milRNAs of R. solani AG1 IA and the recognition of target genes involved in pathogenicity, as well as rice miRNAs, participated in defence against R. solani could provide new insights into revealing the pathogenic mechanisms of the severe rice sheath blight disease. © The Author 2016. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Single-Molecule Analysis for RISC Assembly and Target Cleavage.

PubMed

Sasaki, Hiroshi M; Tadakuma, Hisashi; Tomari, Yukihide

2018-01-01

RNA-induced silencing complex (RISC) is a small RNA-protein complex that mediates silencing of complementary target RNAs. Biochemistry has been successfully used to characterize the molecular mechanism of RISC assembly and function for nearly two decades. However, further dissection of intermediate states during the reactions has been warranted to fill in the gaps in our understanding of RNA silencing mechanisms. Single-molecule analysis with total internal reflection fluorescence (TIRF) microscopy is a powerful imaging-based approach to interrogate complex formation and dynamics at the individual molecule level with high sensitivity. Combining this technique with our recently established in vitro reconstitution system of fly Ago2-RISC, we have developed a single-molecule observation system for RISC assembly. In this chapter, we summarize the detailed protocol for single-molecule analysis of chaperone-assisted assembly of fly Ago2-RISC as well as its target cleavage reaction.

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

PubMed Central

2014-01-01

Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved

Target organs in chronic bioassays of 533 chemical carcinogens

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gold, L.S.; Slone, T.H.; Manley, N.B.

1991-06-01

A compendium of carcinogenesis bioassay results organized by target organ is presented for 533 chemicals that are carcinogenic in at least one species. This compendium is based primarily on experiments in rats or mice; results in hamsters, nonhuman primates, and dogs are also reported. The compendium can be used to identify chemicals that induce tumors at particular sites, and to determine whether target sites are the same for chemicals positive in more than one species. The Carcinogenic Potency Database (CPDB), which includes results of 3969 experiments, is used in the analysis. The published CPDB includes details on each test, andmore » literature references. Chemical carcinogens are reported for 35 different target organs in rats or mice. More than 80% of the carcinogens in each of these species are positive in at least one of the 8 most frequent target sites; liver, lung, mammary gland, stomach, vascular system, kidney, hematopoietic system, and urinary bladder. An analysis is presented of how well one can predict the carcinogenic response in mice from results in rats, or vice versa. Among chemicals tested in both species, 76% of rat carcinogens are positive in mice, and 71% of mouse carcinogens are positive in rats. Prediction is less accurate to the same target site: 52% of rat carcinogens are positive in the same site in mice, and 48% of mouse carcinogens are positive in the same site in rats. The liver is the most frequent site in common between rats and mice.« less

High-fidelity modeling and impact footprint prediction for vehicle breakup analysis

NASA Astrophysics Data System (ADS)

Ling, Lisa

For decades, vehicle breakup analysis had been performed for space missions that used nuclear heater or power units in order to assess aerospace nuclear safety for potential launch failures leading to inadvertent atmospheric reentry. Such pre-launch risk analysis is imperative to assess possible environmental impacts, obtain launch approval, and for launch contingency planning. In order to accurately perform a vehicle breakup analysis, the analysis tool should include a trajectory propagation algorithm coupled with thermal and structural analyses and influences. Since such a software tool was not available commercially or in the public domain, a basic analysis tool was developed by Dr. Angus McRonald prior to this study. This legacy software consisted of low-fidelity modeling and had the capability to predict vehicle breakup, but did not predict the surface impact point of the nuclear component. Thus the main thrust of this study was to develop and verify the additional dynamics modeling and capabilities for the analysis tool with the objectives to (1) have the capability to predict impact point and footprint, (2) increase the fidelity in the prediction of vehicle breakup, and (3) reduce the effort and time required to complete an analysis. The new functions developed for predicting the impact point and footprint included 3-degrees-of-freedom trajectory propagation, the generation of non-arbitrary entry conditions, sensitivity analysis, and the calculation of impact footprint. The functions to increase the fidelity in the prediction of vehicle breakup included a panel code to calculate the hypersonic aerodynamic coefficients for an arbitrary-shaped body and the modeling of local winds. The function to reduce the effort and time required to complete an analysis included the calculation of node failure criteria. The derivation and development of these new functions are presented in this dissertation, and examples are given to demonstrate the new capabilities and the

Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

NASA Astrophysics Data System (ADS)

Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

2018-06-01

Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2-D seismic reflection data processing flow focused on pre-stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching, to estimate the uncertainties of the depths of key horizons near the Deep Sea Drilling Project (DSDP) borehole 258 (DSDP-258) located in the Mentelle Basin, southwest of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ±2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent to the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program, leg 369.

How reliable are ligand-centric methods for Target Fishing?

NASA Astrophysics Data System (ADS)

Peon, Antonio; Dang, Cuong; Ballester, Pedro

2016-04-01

Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict.

Integrative testis transcriptome analysis reveals differentially expressed miRNAs and their mRNA targets during early puberty in Atlantic salmon.

PubMed

Skaftnesmo, K O; Edvardsen, R B; Furmanek, T; Crespo, D; Andersson, E; Kleppe, L; Taranger, G L; Bogerd, J; Schulz, R W; Wargelius, A

2017-10-18

Our understanding of the molecular mechanisms implementing pubertal maturation of the testis in vertebrates is incomplete. This topic is relevant in Atlantic salmon aquaculture, since precocious male puberty negatively impacts animal welfare and growth. We hypothesize that certain miRNAs modulate mRNAs relevant for the initiation of puberty. To explore which miRNAs regulate mRNAs during initiation of puberty in salmon, we performed an integrated transcriptome analysis (miRNA and mRNA-seq) of salmon testis at three stages of development: an immature, long-term quiescent stage, a prepubertal stage just before, and a pubertal stage just after the onset of single cell proliferation activity in the testis. Differentially expressed miRNAs clustered into 5 distinct expression profiles related to the immature, prepubertal and pubertal salmon testis. Potential mRNA targets of these miRNAs were predicted with miRmap and filtered for mRNAs displaying negatively correlated expression patterns. In summary, this analysis revealed miRNAs previously known to be regulated in immature vertebrate testis (miR-101, miR-137, miR-92b, miR-18a, miR-20a), but also miRNAs first reported here as regulated in the testis (miR-new289, miR-30c, miR-724, miR-26b, miR-new271, miR-217, miR-216a, miR-135a, miR-new194 and the novel predicted n268). By KEGG enrichment analysis, progesterone signaling and cell cycle pathway genes were found regulated by these differentially expressed miRNAs. During the transition into puberty we found differential expression of miRNAs previously associated (let7a/b/c), or newly associated (miR-15c, miR-2184, miR-145 and the novel predicted n7a and b) with this stage. KEGG enrichment analysis revealed that mRNAs of the Wnt, Hedgehog and Apelin signaling pathways were potential regulated targets during the transition into puberty. Likewise, several regulated miRNAs in the pubertal stage had earlier been associated (miR-20a, miR-25, miR-181a, miR-202, let7c/d/a, miR-125b

Interacting with target tracking algorithms in a gaze-enhanced motion video analysis system

NASA Astrophysics Data System (ADS)

Hild, Jutta; Krüger, Wolfgang; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

2016-05-01

Motion video analysis is a challenging task, particularly if real-time analysis is required. It is therefore an important issue how to provide suitable assistance for the human operator. Given that the use of customized video analysis systems is more and more established, one supporting measure is to provide system functions which perform subtasks of the analysis. Recent progress in the development of automated image exploitation algorithms allow, e.g., real-time moving target tracking. Another supporting measure is to provide a user interface which strives to reduce the perceptual, cognitive and motor load of the human operator for example by incorporating the operator's visual focus of attention. A gaze-enhanced user interface is able to help here. This work extends prior work on automated target recognition, segmentation, and tracking algorithms as well as about the benefits of a gaze-enhanced user interface for interaction with moving targets. We also propose a prototypical system design aiming to combine both the qualities of the human observer's perception and the automated algorithms in order to improve the overall performance of a real-time video analysis system. In this contribution, we address two novel issues analyzing gaze-based interaction with target tracking algorithms. The first issue extends the gaze-based triggering of a target tracking process, e.g., investigating how to best relaunch in the case of track loss. The second issue addresses the initialization of tracking algorithms without motion segmentation where the operator has to provide the system with the object's image region in order to start the tracking algorithm.

An analysis of possible off target effects following CAS9/CRISPR targeted deletions of neuropeptide gene enhancers from the mouse genome.

PubMed

Hay, Elizabeth Anne; Khalaf, Abdulla Razak; Marini, Pietro; Brown, Andrew; Heath, Karyn; Sheppard, Darrin; MacKenzie, Alasdair

2017-08-01

We have successfully used comparative genomics to identify putative regulatory elements within the human genome that contribute to the tissue specific expression of neuropeptides such as galanin and receptors such as CB1. However, a previous inability to rapidly delete these elements from the mouse genome has prevented optimal assessment of their function in-vivo. This has been solved using CAS9/CRISPR genome editing technology which uses a bacterial endonuclease called CAS9 that, in combination with specifically designed guide RNA (gRNA) molecules, cuts specific regions of the mouse genome. However, reports of "off target" effects, whereby the CAS9 endonuclease is able to cut sites other than those targeted, limits the appeal of this technology. We used cytoplasmic microinjection of gRNA and CAS9 mRNA into 1-cell mouse embryos to rapidly generate enhancer knockout mouse lines. The current study describes our analysis of the genomes of these enhancer knockout lines to detect possible off-target effects. Bioinformatic analysis was used to identify the most likely putative off-target sites and to design PCR primers that would amplify these sequences from genomic DNA of founder enhancer deletion mouse lines. Amplified DNA was then sequenced and blasted against the mouse genome sequence to detect off-target effects. Using this approach we were unable to detect any evidence of off-target effects in the genomes of three founder lines using any of the four gRNAs used in the analysis. This study suggests that the problem of off-target effects in transgenic mice have been exaggerated and that CAS9/CRISPR represents a highly effective and accurate method of deleting putative neuropeptide gene enhancer sequences from the mouse genome. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Meta-analysis of PECS with individuals with ASD: investigation of targeted versus non-targeted outcomes, participant characteristics, and implementation phase.

PubMed

Ganz, Jennifer B; Davis, John L; Lund, Emily M; Goodwyn, Fara D; Simpson, Richard L

2012-01-01

The Picture Exchange Communication System (PECS) is a widely used picture/icon aided augmentative communication system designed for learners with autism and other developmental disorders. This meta-analysis analyzes the extant empirical literature for PECS relative to targeted (functional communication) and non-targeted concomitant outcomes (behavior, social skills, and speech) for learners with autism, learners with autism and intellectual disabilities and those with autism and multiple disabilities. Effect size analyses were done using the Improvement Rate Difference method, an advanced metric. Effect sizes were independently analyzed for targeted and non-targeted outcomes, student age, learner disability, and number of phases in the PECS protocol acquired by learners. Results supported the judgment that PECS is a promising intervention method. Analysis also revealed that functional communication outcomes associated with the PECS protocol were most impacted, that preschool children and those with autism generally showed the strongest training effects, and that in general students who advanced through the most PECS protocol phases had the best outcomes. Copyright © 2011 Elsevier Ltd. All rights reserved.

Impact of target mRNA structure on siRNA silencing efficiency: A large-scale study.

PubMed

Gredell, Joseph A; Berger, Angela K; Walton, S Patrick

2008-07-01

The selection of active siRNAs is generally based on identifying siRNAs with certain sequence and structural properties. However, the efficiency of RNA interference has also been shown to depend on the structure of the target mRNA, primarily through studies using exogenous transcripts with well-defined secondary structures in the vicinity of the target sequence. While these studies provide a means for examining the impact of target sequence and structure independently, the predicted secondary structures for these transcripts are often not reflective of structures that form in full-length, native mRNAs where interactions can occur between relatively remote segments of the mRNAs. Here, using a combination of experimental results and analysis of a large dataset, we demonstrate that the accessibility of certain local target structures on the mRNA is an important determinant in the gene silencing ability of siRNAs. siRNAs targeting the enhanced green fluorescent protein were chosen using a minimal siRNA selection algorithm followed by classification based on the predicted minimum free energy structures of the target transcripts. Transfection into HeLa and HepG2 cells revealed that siRNAs targeting regions of the mRNA predicted to have unpaired 5'- and 3'-ends resulted in greater gene silencing than regions predicted to have other types of secondary structure. These results were confirmed by analysis of gene silencing data from previously published siRNAs, which showed that mRNA target regions unpaired at either the 5'-end or 3'-end were silenced, on average, approximately 10% more strongly than target regions unpaired in the center or primarily paired throughout. We found this effect to be independent of the structure of the siRNA guide strand. Taken together, these results suggest minimal requirements for nucleation of hybridization between the siRNA guide strand and mRNA and that both mRNA and guide strand structure should be considered when choosing candidate si

Impact of target mRNA structure on siRNA silencing efficiency: a large-scale study

PubMed Central

Gredell, Joseph A.; Berger, Angela K.; Walton, S. Patrick

2009-01-01

The selection of active siRNAs is generally based on identifying siRNAs with certain sequence and structural properties. However, the efficiency of RNA interference has also been shown to depend on the structure of the target mRNA, primarily through studies using exogenous transcripts with well-defined secondary structures in the vicinity of the target sequence. While these studies provide a means for examining the impact of target sequence and structure independently, the predicted secondary structures for these transcripts are often not reflective of structures that form in full-length, native mRNAs where interactions can occur between relatively remote segments of the mRNAs. Here, using a combination of experimental results and analysis of a large dataset, we demonstrate that the accessibility of certain local target structures on the mRNA is an important determinant in the gene silencing ability of siRNAs. siRNAs targeting the enhanced green fluorescent protein were chosen using a minimal siRNA selection algorithm followed by classification based on the predicted minimum free energy structures of the target transcripts. Transfection into HeLa and HepG2 cells revealed that siRNAs targeting regions of the mRNA predicted to have unpaired 5’- and 3’-ends resulted in greater gene silencing than regions predicted to have other types of secondary structure. These results were confirmed by analysis of gene silencing data from previously published siRNAs, which showed that mRNA target regions unpaired at either the 5’-end or 3’-end were silenced, on average, ~10% more strongly than target regions unpaired in the center or primarily paired throughout. We found this effect to be independent of the structure of the siRNA guide strand. Taken together, these results suggest minimal requirements for nucleation of hybridization between the siRNA guide strand and mRNA and that both mRNA and guide strand structure should be considered when choosing candidate siRNAs. PMID

Solar prediction analysis

NASA Technical Reports Server (NTRS)

Smith, Jesse B.

1992-01-01

Solar Activity prediction is essential to definition of orbital design and operational environments for space flight. This task provides the necessary research to better understand solar predictions being generated by the solar community and to develop improved solar prediction models. The contractor shall provide the necessary manpower and facilities to perform the following tasks: (1) review, evaluate, and assess the time evolution of the solar cycle to provide probable limits of solar cycle behavior near maximum end during the decline of solar cycle 22, and the forecasts being provided by the solar community and the techniques being used to generate these forecasts; and (2) develop and refine prediction techniques for short-term solar behavior flare prediction within solar active regions, with special emphasis on the correlation of magnetic shear with flare occurrence.

Predicting adult obesity from childhood obesity: a systematic review and meta-analysis.

PubMed

Simmonds, M; Llewellyn, A; Owen, C G; Woolacott, N

2016-02-01

A systematic review and meta-analysis was performed to investigate the ability of simple measures of childhood obesity such as body mass index (BMI) to predict future obesity in adolescence and adulthood. Large cohort studies, which measured obesity both in childhood and in later adolescence or adulthood, using any recognized measure of obesity were sought. Study quality was assessed. Studies were pooled using diagnostic meta-analysis methods. Fifteen prospective cohort studies were included in the meta-analysis. BMI was the only measure of obesity reported in any study, with 200,777 participants followed up. Obese children and adolescents were around five times more likely to be obese in adulthood than those who were not obese. Around 55% of obese children go on to be obese in adolescence, around 80% of obese adolescents will still be obese in adulthood and around 70% will be obese over age 30. Therefore, action to reduce and prevent obesity in these adolescents is needed. However, 70% of obese adults were not obese in childhood or adolescence, so targeting obesity reduction solely at obese or overweight children needs to be considered carefully as this may not substantially reduce the overall burden of adult obesity. © 2015 World Obesity.

Nuclease Target Site Selection for Maximizing On-target Activity and Minimizing Off-target Effects in Genome Editing

PubMed Central

Lee, Ciaran M; Cradick, Thomas J; Fine, Eli J; Bao, Gang

2016-01-01

The rapid advancement in targeted genome editing using engineered nucleases such as ZFNs, TALENs, and CRISPR/Cas9 systems has resulted in a suite of powerful methods that allows researchers to target any genomic locus of interest. A complementary set of design tools has been developed to aid researchers with nuclease design, target site selection, and experimental validation. Here, we review the various tools available for target selection in designing engineered nucleases, and for quantifying nuclease activity and specificity, including web-based search tools and experimental methods. We also elucidate challenges in target selection, especially in predicting off-target effects, and discuss future directions in precision genome editing and its applications. PMID:26750397

In silico prediction of escherichia coli proteins targeting the host cell nucleus, with special reference to their role in colon cancer etiology.

PubMed

Khan, Abdul Arif

2014-06-01

The potential role of Escherichia coli in the development of colorectal carcinoma (CRC) has been investigated in many studies. Although the exact mechanism is not clear, chronic inflammation caused by E. coli and other related events are suggested as possible causes behind E. coli-induced colon cancer. It has been found that CRC cells, but not normal cells, are colonized by an intracellular form of E. coli. We predicted nuclear targeting of bacterial proteins in the host cell through computational tools nuclear localization signal (NLS) mapper and balanced subcellular localization predictor (BaCeILo). During intracellular E. coli residence, such targeting is highly likely and may have a possible role in colon cancer etiology. We observed that several gene expression-associated proteins of E. coli can migrate to the host nucleus during intracellular infections. This situation provides an opportunity for competitive interaction of host and pathogen proteins with similar cellular substrates, thereby increasing the chances of development of colon cancer. Moreover, the results indicated that proteins localized in the membrane of E. coli mostly act as secretary proteins in host cells. No exact correlation was observed between NLS prediction and nuclear localization prediction by BaCeILo. This is partly because of a number of reasons, including that only 30% of nuclear proteins carry NLS and that proteins <40 kDa molecular weight can passively target the host nucleus. This study concludes that detection of gene expression-specific E. coli proteins and their targeting of the nucleus may have a profound impact on CRC etiology.

Identification of microRNAs in Caragana intermedia by high-throughput sequencing and expression analysis of 12 microRNAs and their targets under salt stress.

PubMed

Zhu, Jianfeng; Li, Wanfeng; Yang, Wenhua; Qi, Liwang; Han, Suying

2013-09-01

142 miRNAs were identified and 38 miRNA targets were predicted, 4 of which were validated, in C. intermedia . The expression of 12 miRNAs in salt-stressed leaves was assessed by qRT-PCR. MicroRNAs (miRNAs) are endogenous small RNAs that play important roles in various biological and metabolic processes in plants. Caragana intermedia is an important ecological and economic tree species prominent in the desert environment of west and northwest China. To date, no investigation into C. intermedia miRNAs has been reported. In this study, high-throughput sequencing of small RNAs and analysis of transcriptome data were performed to identify both conserved and novel miRNAs, and also their target mRNA genes in C. intermedia. Based on sequence similarity and hairpin structure prediction, 132 putative conserved miRNAs (12 of which were confirmed to form hairpin precursors) belonging to 31 known miRNA families were identified. Ten novel miRNAs (including the miRNA* sequences of three novel miRNAs) were also discovered. Furthermore, 36 potential target genes of 17 known miRNA families and 2 potential target genes of 1 novel miRNA were predicted; 4 of these were validated by 5' RACE. The expression of 12 miRNAs was validated in different tissues, and these and five target mRNAs were assessed by qRT-PCR after salt treatment. The expression levels of seven miRNAs (cin-miR157a, cin-miR159a, cin-miR165a, cin-miR167b, cin-miR172b, cin-miR390a and cin-miR396a) were upregulated, while cin-miR398a expression was downregulated after salt treatment. The targets of cin-miR157a, cin-miR165a, cin-miR172b and cin-miR396a were downregulated and showed an approximately negative correlation with their corresponding miRNAs under salt treatment. These results would help further understanding of miRNA regulation in response to abiotic stress in C. intermedia.

Novel proteases from the genome of the carnivorous plant Drosera capensis: structural prediction and comparative analysis

PubMed Central

Butts, Carter T.; Bierma, Jan C.; Martin, Rachel W.

2016-01-01

In his 1875 monograph on insectivorous plants, Darwin described the feeding reactions of Drosera flypaper traps and predicted that their secretions contained a “ferment” similar to mammalian pepsin, an aspartic protease. Here we report a high-quality draft genome sequence for the cape sundew, Drosera capensis, the first genome of a carnivorous plant from order Caryophyllales, which also includes the Venus flytrap (Dionaea) and the tropical pitcher plants (Nepenthes). This species was selected in part for its hardiness and ease of cultivation, making it an excellent model organism for further investigations of plant carnivory. Analysis of predicted protein sequences yields genes encoding proteases homologous to those found in other plants, some of which display sequence and structural features that suggest novel functionalities. Because the sequence similarity to proteins of known structure is in most cases too low for traditional homology modeling, 3D structures of representative proteases are predicted using comparative modeling with all-atom refinement. Although the overall folds and active residues for these proteins are conserved, we find structural and sequence differences consistent with a diversity of substrate recognition patterns. Finally, we predict differences in substrate specificities using in silico experiments, providing targets for structure/function studies of novel enzymes with biological and technological significance. PMID:27353064

Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach

PubMed Central

Mondal, Shakhinur Islam; Ferdous, Sabiha; Jewel, Nurnabi Azad; Akter, Arzuba; Mahmud, Zabed; Islam, Md Muzahidul; Afrin, Tanzila; Karim, Nurul

2015-01-01

Bacterial enteric infections resulting in diarrhea, dysentery, or enteric fever constitute a huge public health problem, with more than a billion episodes of disease annually in developing and developed countries. In this study, the deadly agent of hemorrhagic diarrhea and hemolytic uremic syndrome, Escherichia coli O157:H7 was investigated with extensive computational approaches aimed at identifying novel and broad-spectrum antibiotic targets. A systematic in silico workflow consisting of comparative genomics, metabolic pathways analysis, and additional drug prioritizing parameters was used to identify novel drug targets that were essential for the pathogen’s survival but absent in its human host. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified 350 putative target proteins in E. coli O157:H7 which showed no similarity to human proteins. Further bio-informatic approaches including prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characteristics greatly aided in filtering the potential drug targets from 350 to 120. Ultimately, 44 non-homologous essential proteins of E. coli O157:H7 were prioritized and proved to have the eligibility to become novel broad-spectrum antibiotic targets and DNA polymerase III alpha (dnaE) was the top-ranked among these targets. Moreover, druggability of each of the identified drug targets was evaluated by the DrugBank database. In addition, 3D structure of the dnaE was modeled and explored further for in silico docking with ligands having potential druggability. Finally, we confirmed that the compounds N-coeleneterazine and N-(1,4-dihydro-5H-tetrazol-5-ylidene)-9-oxo-9H-xanthene-2-sulfon-amide were the most suitable ligands of dnaE and hence proposed as the potential inhibitors of this target protein. The results of this study could facilitate the discovery and release of new and effective drugs against E

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

PubMed Central

Sarmady, Mahdi; Dampier, William; Tozeren, Aydin

2011-01-01

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk. PMID:21738584

Using cost-effectiveness analysis to evaluate targeting strategies: the case of vitamin A supplementation.

PubMed

Loevinsohn, B P; Sutter, R W; Costales, M O

1997-03-01

Given the demonstrated efficacy of vitamin A supplements in reducing childhood mortality, health officials now have to decide whether it would be efficient to target the supplements to high risk children. Decisions about targeting are complex because they depend on a number of factors; the degree of clustering of preventable deaths, the cost of the intervention, the side-effects of the intervention, the cost of identifying the high risk group, and the accuracy of the 'diagnosis' of risk. A cost-effectiveness analysis was used in the Philippines to examine whether vitamin A supplements should be given universally to all children 6-59 months, targeted broadly to children suffering from mild, moderate, or severe malnutrition, or targeted narrowly to pre-schoolers with moderate and severe malnutrition. The first year average cost of the universal approach was US$67.21 per death averted compared to $144.12 and $257.20 for the broad and narrow targeting approaches respectively. When subjected to sensitivity analysis the conclusion about the most cost-effective strategy was robust to changes in underlying assumptions such as the efficacy of supplements, clustering of deaths, and toxicity. Targeting vitamin A supplements to high risk children is not an efficient use of resources. Based on the results of this cost-effectiveness analysis and a consideration of alternate strategies, it is apparent that vitamin A, like immunization, should be provided to all pre-schoolers in the developing world. Issues about targeting public health interventions can usefully be addressed by cost-effectiveness analysis.

Trajectory Control of Rendezvous with Maneuver Target Spacecraft

NASA Technical Reports Server (NTRS)

Zhou, Zhinqiang

2012-01-01

In this paper, a nonlinear trajectory control algorithm of rendezvous with maneuvering target spacecraft is presented. The disturbance forces on the chaser and target spacecraft and the thrust forces on the chaser spacecraft are considered in the analysis. The control algorithm developed in this paper uses the relative distance and relative velocity between the target and chaser spacecraft as the inputs. A general formula of reference relative trajectory of the chaser spacecraft to the target spacecraft is developed and applied to four different proximity maneuvers, which are in-track circling, cross-track circling, in-track spiral rendezvous and cross-track spiral rendezvous. The closed-loop differential equations of the proximity relative motion with the control algorithm are derived. It is proven in the paper that the tracking errors between the commanded relative trajectory and the actual relative trajectory are bounded within a constant region determined by the control gains. The prediction of the tracking errors is obtained. Design examples are provided to show the implementation of the control algorithm. The simulation results show that the actual relative trajectory tracks the commanded relative trajectory tightly. The predicted tracking errors match those calculated in the simulation results. The control algorithm developed in this paper can also be applied to interception of maneuver target spacecraft and relative trajectory control of spacecraft formation flying.

Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens

PubMed Central

Lo, Yu-Chen; Senese, Silvia; Li, Chien-Ming; Hu, Qiyang; Huang, Yong; Damoiseaux, Robert; Torres, Jorge Z.

2015-01-01

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). PMID:25826798

MicroTrout: A comprehensive, genome-wide miRNA target prediction framework for rainbow trout, Oncorhynchus mykiss.

PubMed

Mennigen, Jan A; Zhang, Dapeng

2016-12-01

Rainbow trout represent an important teleost research model and aquaculture species. As such, rainbow trout are employed in diverse areas of biological research, including basic biological disciplines such as comparative physiology, toxicology, and, since rainbow trout have undergone both teleost- and salmonid-specific rounds of genome duplication, molecular evolution. In recent years, microRNAs (miRNAs, small non-protein coding RNAs) have emerged as important posttranscriptional regulators of gene expression in animals. Given the increasingly recognized importance of miRNAs as an additional layer in the regulation of gene expression and hence biological function, recent efforts using RNA- and genome sequencing approaches have resulted in the creation of several resources for the construction of a comprehensive repertoire of rainbow trout miRNAs and isomiRs (variant miRNA sequences that all appear to derive from the same gene but vary in sequence due to post-transcriptional processing). Importantly, through the recent publication of the rainbow trout genome (Berthelot et al., 2014), mRNA 3'UTR information has become available, allowing for the first time the genome-wide prediction of miRNA-target RNA relationships in this species. We here report the creation of the microtrout database, a comprehensive resource for rainbow trout miRNA and annotated 3'UTRs. The comprehensive database was used to implement an algorithm to predict genome-wide rainbow trout-specific miRNA-mRNA target relationships, generating an improved predictive framework over previously published approaches. This work will serve as a useful framework and sequence resource to experimentally address the role of miRNAs in several research areas using the rainbow trout model, examples of which are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Kinetic analysis of the effects of target structure on siRNA efficiency

NASA Astrophysics Data System (ADS)

Chen, Jiawen; Zhang, Wenbing

2012-12-01

RNAi efficiency for target cleavage and protein expression is related to the target structure. Considering the RNA-induced silencing complex (RISC) as a multiple turnover enzyme, we investigated the effect of target mRNA structure on siRNA efficiency with kinetic analysis. The 4-step model was used to study the target cleavage kinetic process: hybridization nucleation at an accessible target site, RISC-mRNA hybrid elongation along with mRNA target structure melting, target cleavage, and enzyme reactivation. At this model, the terms accounting for the target accessibility, stability, and the seed and the nucleation site effects are all included. The results are in good agreement with that of experiments which show different arguments about the structure effects on siRNA efficiency. It shows that the siRNA efficiency is influenced by the integrated factors of target's accessibility, stability, and the seed effects. To study the off-target effects, a simple model of one siRNA binding to two mRNA targets was designed. By using this model, the possibility for diminishing the off-target effects by the concentration of siRNA was discussed.

Screening the molecular targets of ovarian cancer based on bioinformatics analysis.

PubMed

Du, Lei; Qian, Xiaolei; Dai, Chenyang; Wang, Lihua; Huang, Ding; Wang, Shuying; Shen, Xiaowei

2015-01-01

Ovarian cancer (OC) is the most lethal gynecologic malignancy. This study aims to explore the molecular mechanisms of OC and identify potential molecular targets for OC treatment. Microarray gene expression data (GSE14407) including 12 normal ovarian surface epithelia samples and 12 OC epithelia samples were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) between 2 kinds of ovarian tissue were identified by using limma package in R language (|log2 fold change| gt;1 and false discovery rate [FDR] lt;0.05). Protein-protein interactions (PPIs) and known OC-related genes were screened from COXPRESdb and GenBank database, respectively. Furthermore, PPI network of top 10 upregulated DEGs and top 10 downregulated DEGs was constructed and visualized through Cytoscape software. Finally, for the genes involved in PPI network, functional enrichment analysis was performed by using DAVID (FDR lt;0.05). In total, 1136 DEGs were identified, including 544 downregulated and 592 upregulated DEGs. Then, PPI network was constructed, and DEGs CDKN2A, MUC1, OGN, ZIC1, SOX17, and TFAP2A interacted with known OC-related genes CDK4, EGFR/JUN, SRC, CLI1, CTNNB1, and TP53, respectively. Moreover, functions about oxygen transport and embryonic development were enriched by the genes involved in the network of downregulated DEGs. We propose that 4 DEGs (OGN, ZIC1, SOX17, and TFAP2A) and 2 functions (oxygen transport and embryonic development) might play a role in the development of OC. These 4 DEGs and known OC-related genes might serve as therapeutic targets for OC. Further studies are required to validate these predictions.

A BRDF-BPDF database for the analysis of Earth target reflectances

NASA Astrophysics Data System (ADS)

Breon, Francois-Marie; Maignan, Fabienne

2017-01-01

Land surface reflectance is not isotropic. It varies with the observation geometry that is defined by the sun, view zenith angles, and the relative azimuth. In addition, the reflectance is linearly polarized. The reflectance anisotropy is quantified by the bidirectional reflectance distribution function (BRDF), while its polarization properties are defined by the bidirectional polarization distribution function (BPDF). The POLDER radiometer that flew onboard the PARASOL microsatellite remains the only space instrument that measured numerous samples of the BRDF and BPDF of Earth targets. Here, we describe a database of representative BRDFs and BPDFs derived from the POLDER measurements. From the huge number of data acquired by the spaceborne instrument over a period of 7 years, we selected a set of targets with high-quality observations. The selection aimed for a large number of observations, free of significant cloud or aerosol contamination, acquired in diverse observation geometries with a focus on the backscatter direction that shows the specific hot spot signature. The targets are sorted according to the 16-class International Geosphere-Biosphere Programme (IGBP) land cover classification system, and the target selection aims at a spatial representativeness within the class. The database thus provides a set of high-quality BRDF and BPDF samples that can be used to assess the typical variability of natural surface reflectances or to evaluate models. It is available freely from the PANGAEA website (target="_blank">doi:10.1594/PANGAEA.864090). In addition to the database, we provide a visualization and analysis tool based on the Interactive Data Language (IDL). It allows an interactive analysis of the measurements and a comparison against various BRDF and BPDF analytical models. The present paper describes the input data, the selection principles, the database format, and the analysis tool

Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas' disease treatment

PubMed Central

2010-01-01

Background Trypanosoma cruzi is the etiological agent of Chagas' disease, an endemic infection that causes thousands of deaths every year in Latin America. Therapeutic options remain inefficient, demanding the search for new drugs and/or new molecular targets. Such efforts can focus on proteins that are specific to the parasite, but analogous enzymes and enzymes with a three-dimensional (3D) structure sufficiently different from the corresponding host proteins may represent equally interesting targets. In order to find these targets we used the workflows MHOLline and AnEnΠ obtaining 3D models from homologous, analogous and specific proteins of Trypanosoma cruzi versus Homo sapiens. Results We applied genome wide comparative modelling techniques to obtain 3D models for 3,286 predicted proteins of T. cruzi. In combination with comparative genome analysis to Homo sapiens, we were able to identify a subset of 397 enzyme sequences, of which 356 are homologous, 3 analogous and 38 specific to the parasite. Conclusions In this work, we present a set of 397 enzyme models of T. cruzi that can constitute potential structure-based drug targets to be investigated for the development of new strategies to fight Chagas' disease. The strategies presented here support the concept of structural analysis in conjunction with protein functional analysis as an interesting computational methodology to detect potential targets for structure-based rational drug design. For example, 2,4-dienoyl-CoA reductase (EC 1.3.1.34) and triacylglycerol lipase (EC 3.1.1.3), classified as analogous proteins in relation to H. sapiens enzymes, were identified as new potential molecular targets. PMID:21034488

Dependency of human target detection performance on clutter and quality of supporting image analysis algorithms in a video surveillance task

NASA Astrophysics Data System (ADS)

Huber, Samuel; Dunau, Patrick; Wellig, Peter; Stein, Karin

2017-10-01

Background: In target detection, the success rates depend strongly on human observer performances. Two prior studies tested the contributions of target detection algorithms and prior training sessions. The aim of this Swiss-German cooperation study was to evaluate the dependency of human observer performance on the quality of supporting image analysis algorithms. Methods: The participants were presented 15 different video sequences. Their task was to detect all targets in the shortest possible time. Each video sequence showed a heavily cluttered simulated public area from a different viewing angle. In each video sequence, the number of avatars in the area was altered to 100, 150 and 200 subjects. The number of targets appearing was kept at 10%. The number of marked targets varied from 0, 5, 10, 20 up to 40 marked subjects while keeping the positive predictive value of the detection algorithm at 20%. During the task, workload level was assessed by applying an acoustic secondary task. Detection rates and detection times for the targets were analyzed using inferential statistics. Results: The study found Target Detection Time to increase and Target Detection Rates to decrease with increasing numbers of avatars. The same is true for the Secondary Task Reaction Time while there was no effect on Secondary Task Hit Rate. Furthermore, we found a trend for a u-shaped correlation between the numbers of markings and RTST indicating increased workload. Conclusion: The trial results may indicate useful criteria for the design of training and support of observers in observational tasks.

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

PubMed

Zhao, Lue Ping; Carlsson, Annelie; Larsson, Helena Elding; Forsander, Gun; Ivarsson, Sten A; Kockum, Ingrid; Ludvigsson, Johnny; Marcus, Claude; Persson, Martina; Samuelsson, Ulf; Örtqvist, Eva; Pyo, Chul-Woo; Bolouri, Hamid; Zhao, Michael; Nelson, Wyatt C; Geraghty, Daniel E; Lernmark, Åke

2017-11-01

It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10 -92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations. Copyright © 2017 John Wiley & Sons, Ltd.

Diversifying Selection Analysis Predicts Antigenic Evolution of 2009 Pandemic H1N1 Influenza A Virus in Humans.

PubMed

Lee, Alexandra J; Das, Suman R; Wang, Wei; Fitzgerald, Theresa; Pickett, Brett E; Aevermann, Brian D; Topham, David J; Falsey, Ann R; Scheuermann, Richard H

2015-05-01

Although a large number of immune epitopes have been identified in the influenza A virus (IAV) hemagglutinin (HA) protein using various experimental systems, it is unclear which are involved in protective immunity to natural infection in humans. We developed a data mining approach analyzing natural H1N1 human isolates to identify HA protein regions that may be targeted by the human immune system and can predict the evolution of IAV. We identified 16 amino acid sites experiencing diversifying selection during the evolution of prepandemic seasonal H1N1 strains and found that 11 sites were located in experimentally determined B-cell/antibody (Ab) epitopes, including three distinct neutralizing Caton epitopes: Sa, Sb, and Ca2 [A. J. Caton, G. G. Brownlee, J. W. Yewdell, and W. Gerhard, Cell 31:417-427, 1982, http://dx.doi.org/10.1016/0092-8674(82)90135-0]. We predicted that these diversified epitope regions would be the targets of mutation as the 2009 H1N1 pandemic (pH1N1) lineage evolves in response to the development of population-level protective immunity in humans. Using a chi-squared goodness-of-fit test, we identified 10 amino acid sites that significantly differed between the pH1N1 isolates and isolates from the recent 2012-2013 and 2013-2014 influenza seasons. Three of these sites were located in the same diversified B-cell/Ab epitope regions as identified in the analysis of prepandemic sequences, including Sa and Sb. As predicted, hemagglutination inhibition (HI) assays using human sera from subjects vaccinated with the initial pH1N1 isolate demonstrated reduced reactivity against 2013-2014 isolates. Taken together, these results suggest that diversifying selection analysis can identify key immune epitopes responsible for protective immunity to influenza virus in humans and thereby predict virus evolution. The WHO estimates that approximately 5 to 10% of adults and 20 to 30% of children in the world are infected by influenza virus each year. While an adaptive

Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Joo, Keehyoung; Lee, Jooyoung; Czaplewski, Cezary; Liwo, Adam

2015-06-22

A new approach to the prediction of protein structures that uses distance and backbone virtual-bond dihedral angle restraints derived from template-based models and simulations with the united residue (UNRES) force field is proposed. The approach combines the accuracy and reliability of template-based methods for the segments of the target sequence with high similarity to those having known structures with the ability of UNRES to pack the domains correctly. Multiplexed replica-exchange molecular dynamics with restraints derived from template-based models of a given target, in which each restraint is weighted according to the accuracy of the prediction of the corresponding section of the molecule, is used to search the conformational space, and the weighted histogram analysis method and cluster analysis are applied to determine the families of the most probable conformations, from which candidate predictions are selected. To test the capability of the method to recover template-based models from restraints, five single-domain proteins with structures that have been well-predicted by template-based methods were used; it was found that the resulting structures were of the same quality as the best of the original models. To assess whether the new approach can improve template-based predictions with incorrectly predicted domain packing, four such targets were selected from the CASP10 targets; for three of them the new approach resulted in significantly better predictions compared with the original template-based models. The new approach can be used to predict the structures of proteins for which good templates can be found for sections of the sequence or an overall good template can be found for the entire sequence but the prediction quality is remarkably weaker in putative domain-linker regions.

Curation of inhibitor-target data: process and impact on pathway analysis.

PubMed

Devidas, Sreenivas

2009-01-01

The past decade has seen a significant emergence in the availability and use of pathway analysis tools. The workflow that is supported by most of the pathway analysis tools is limited to either of the following: a. a network of genes based on the input data set, or b. the resultant network filtered down by a few criteria such as (but not limited to) i. disease association of the genes in the network; ii. targets known to be the target of one or more launched drugs; iii. targets known to be the target of one or more compounds in clinical trials; and iv. targets reasonably known to be potential candidate or clinical biomarkers. Almost all the tools in use today are biased towards the biological side and contain little, if any, information on the chemical inhibitors associated with the components of a given biological network. The limitation resides as follows: The fact that the number of inhibitors that have been published or patented is probably several fold (probably greater than 10-fold) more than the number of published protein-protein interactions. Curation of such data is both expensive and time consuming and could impact ROI significantly. The non-standardization associated with protein and gene names makes mapping reasonably non-straightforward. The number of patented and published inhibitors across target classes increases by over a million per year. Therefore, keeping the databases current becomes a monumental problem. Modifications required in the product architectures to accommodate chemistry-related content. GVK Bio has, over the past 7 years, curated the compound-target data that is necessary for the addition of such compound-centric workflows. This chapter focuses on identification, curation and utility of such data.

Sea surface temperature predictions using a multi-ocean analysis ensemble scheme

NASA Astrophysics Data System (ADS)

Zhang, Ying; Zhu, Jieshun; Li, Zhongxian; Chen, Haishan; Zeng, Gang

2017-08-01

This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982-2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Niño and Atlantic Niño regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

Temporal analysis of reciprocal miRNA-mRNA expression patterns predicts regulatory networks during differentiation in human skeletal muscle cells

PubMed Central

Sjögren, Rasmus J. O.; Egan, Brendan; Katayama, Mutsumi; Zierath, Juleen R.

2014-01-01

microRNAs (miRNAs) are short noncoding RNAs that regulate gene expression through posttranscriptional repression of target genes. miRNAs exert a fundamental level of control over many developmental processes, but their role in the differentiation and development of skeletal muscle from myogenic progenitor cells in humans remains incompletely understood. Using primary cultures established from human skeletal muscle satellite cells, we performed microarray profiling of miRNA expression during differentiation of myoblasts (day 0) into myotubes at 48 h intervals (day 2, 4, 6, 8, and 10). Based on a time-course analysis, we identified 44 miRNAs with altered expression [false discovery rate (FDR) < 5%, fold change > ±1.2] during differentiation, including the marked upregulation of the canonical myogenic miRNAs miR-1, miR-133a, miR-133b, and miR-206. Microarray profiling of mRNA expression at day 0, 4, and 10 identified 842 and 949 genes differentially expressed (FDR < 10%) at day 4 and 10, respectively. At day 10, 42% of altered transcripts demonstrated reciprocal expression patterns in relation to the directional change of their in silico predicted regulatory miRNAs based on analysis using Ingenuity Pathway Analysis microRNA Target Filter. Bioinformatic analysis predicted networks of regulation during differentiation including myomiRs miR-1/206 and miR-133a/b, miRNAs previously established in differentiation including miR-26 and miR-30, and novel miRNAs regulated during differentiation of human skeletal muscle cells such as miR-138-5p and miR-20a. These reciprocal expression patterns may represent new regulatory nodes in human skeletal muscle cell differentiation. This analysis serves as a reference point for future studies of human skeletal muscle differentiation and development in healthy and disease states. PMID:25547110

A pointing facilitation system for motor-impaired users combining polynomial smoothing and time-weighted gradient target prediction models.

PubMed

Blow, Nikolaus; Biswas, Pradipta

2017-01-01

As computers become more and more essential for everyday life, people who cannot use them are missing out on an important tool. The predominant method of interaction with a screen is a mouse, and difficulty in using a mouse can be a huge obstacle for people who would otherwise gain great value from using a computer. If mouse pointing were to be made easier, then a large number of users may be able to begin using a computer efficiently where they may previously have been unable to. The present article aimed to improve pointing speeds for people with arm or hand impairments. The authors investigated different smoothing and prediction models on a stored data set involving 25 people, and the best of these algorithms were chosen. A web-based prototype was developed combining a polynomial smoothing algorithm with a time-weighted gradient target prediction model. The adapted interface gave an average improvement of 13.5% in target selection times in a 10-person study of representative users of the system. A demonstration video of the system is available at https://youtu.be/sAzbrKHivEY.

Computational Methods for Failure Analysis and Life Prediction

NASA Technical Reports Server (NTRS)

Noor, Ahmed K. (Compiler); Harris, Charles E. (Compiler); Housner, Jerrold M. (Compiler); Hopkins, Dale A. (Compiler)

1993-01-01

This conference publication contains the presentations and discussions from the joint UVA/NASA Workshop on Computational Methods for Failure Analysis and Life Prediction held at NASA Langley Research Center 14-15 Oct. 1992. The presentations focused on damage failure and life predictions of polymer-matrix composite structures. They covered some of the research activities at NASA Langley, NASA Lewis, Southwest Research Institute, industry, and universities. Both airframes and propulsion systems were considered.

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Intercepting moving targets: does memory from practice in a specific condition of target displacement affect movement timing?

PubMed

de Azevedo Neto, Raymundo Machado; Teixeira, Luis Augusto

2011-05-01

This investigation aimed at assessing the extent to which memory from practice in a specific condition of target displacement modulates temporal errors and movement timing of interceptive movements. We compared two groups practicing with certainty of future target velocity either in unchanged target velocity or in target velocity decrease. Following practice, both experimental groups were probed in the situations of unchanged target velocity and target velocity decrease either under the context of certainty or uncertainty about target velocity. Results from practice showed similar improvement of temporal accuracy between groups, revealing that target velocity decrease did not disturb temporal movement organization when fully predictable. Analysis of temporal errors in the probing trials indicated that both groups had higher timing accuracy in velocity decrease in comparison with unchanged velocity. Effect of practice was detected by increased temporal accuracy of the velocity decrease group in situations of decreased velocity; a trend consistent with the expected effect of practice was observed for temporal errors in the unchanged velocity group and in movement initiation at a descriptive level. An additional point of theoretical interest was the fast adaptation in both groups to a target velocity pattern different from that practiced. These points are discussed under the perspective of integration of vision and motor control by means of an internal forward model of external motion.

Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses.

PubMed

Lingner, Thomas; Kataya, Amr R; Antonicelli, Gerardo E; Benichou, Aline; Nilssen, Kjersti; Chen, Xiong-Yan; Siemsen, Tanja; Morgenstern, Burkhard; Meinicke, Peter; Reumann, Sigrun

2011-04-01

In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.