Do I Upload Hts Data Into Public
Chem Inform. Author manuscript; available in PMC 2018 May 21.
Published in terminal edited form as:
Published online 2017 April 26.
PMCID: PMC5962024
NIHMSID: NIHMS936862
High-Throughput Screening Analysis Datasets from the PubChem Database
Mariusz Butkiewicz
aneDepartment of Chemistry, Pharmacology and Biomedical Informatics, Center for Structural Biology, Institute of Chemical Biology, Vanderbilt University, Nashville, Us
Yanli Wang
iiNational Institutes of Health, National Center for Biotechnology Information, The states National Library of Medicine, Bethesda, United states of america
Stephen H Bryant
twoNational Institutes of Health, National Centre for Biotechnology Information, US National Library of Medicine, Bethesda, USA
Edward Westward Lowe, Jr
iSection of Chemical science, Pharmacology and Biomedical Informatics, Center for Structural Biology, Found of Chemical Biology, Vanderbilt University, Nashville, USA
David C Weaver
1Department of Chemical science, Pharmacology and Biomedical Information science, Center for Structural Biology, Institute of Chemical Biology, Vanderbilt University, Nashville, United states
Jens Meiler
1Department of Chemistry, Pharmacology and Biomedical Informatics, Center for Structural Biology, Institute of Chemic Biology, Vanderbilt Academy, Nashville, United states
Abstract
Availability of high-throughput screening (HTS) information in the public domain offers great potential to foster development of ligand-based computer-aided drug discovery (LB-CADD) methods crucial for drug discovery efforts in academia and manufacture. LB-CADD method development depends on loftier-quality HTS assay data, i.e., datasets that comprise both active and inactive compounds. These active compounds are hits from primary screens that have been tested in concentration-response experiments and where the target-specificity of the hits has been validated through suitable secondary screening experiments. Publicly available HTS repositories such equally PubChem oft provide such data in a convoluted style: compounds that are classified every bit inactive need to exist extracted from the primary screening record. However, compounds classified every bit active in the primary screening record are not suitable every bit a set of active compounds for LB-CADD experiments due to high false-positive rate. A suitable fix of actives can be derived by carefully analysing results in often upward to 5 or more than assays that are used to confirm and classify the activeness of compounds. These assays, in part, build on each other. However, often not all hit compounds from the previous screen have been tested. Sometimes a compound can be classified as 'agile', though its meaning is 'inactive' on the target of interest as it is 'active' on a unlike target protein. Here, a curation process of hierarchically related confirmatory screens is illustrated based on two specifically chosen protein employ-cases. The subsequent re-upload procedure into PubChem is described for the findings of those 2 scenarios. Further, we provide 9 publicly accessible high quality datasets for future LB-CADD method development that provide a mutual baseline for comparison of future methods to the scientific customs. We also provide a protocol researchers can follow to upload additional datasets for benchmarking.
Keywords: HTS, PubChem, Datasets, LB-CADD
Introduction
The development of ligand-based computer-aided drug discovery (LB-CADD) methods for in silico (virtual) high-throughput screening (HTS) shows promising results for identifying potential hit compounds, i.eastward., compounds that share a biological activity of interest [ane]. With the popularity gain of HTS in academia, the need for LB-CADD method development continues to increase [2,3]. The cost of an HTS screen correlates virtually linearly with the number of physically screened compounds. LB-CADD has the potential to reduce these costs in a resource-limited bookish environment past helping to prioritize which compounds to include in a screening entrada. However, LB-CADD method development depends on the availability of reliable HTS assay datasets to study the relationship of ligand structure and biological activity. It is a claiming to identify suitable refined datasets for LB-CADD benchmarking that are available to the inquiry community. Often in both manufacture and academia, proprietary datasets are not disclosed to the research community for use in LB-CADD benchmarking and methods development. Therefore, novel methods cannot be direct compared to existing algorithm implementations and scientific progress is difficult to approximate. In other research fields, east.g., car learning, standardized datasets are bachelor and serve equally foundation for evaluation and benchmarking of novel algorithms. Examples are the MNIST database for manus-written digits and UCI Machine Learning Repository [4,v]. These datasets provide a common ground for testing new methods and allowing for easy comparison of novel and previously established approaches.
Compound data repositories host libraries of molecular compounds and associated biological activities
PubChem is a public repository providing HTS experiment results containing biological activities for several hundred thousand of compounds tested against different biological targets [vi–8]. Information technology provides a platform to host target-related HTS datasets. PubChem is maintained by the National Center for Biotechnology Information (NCBI), a sectionalisation of the National Library of Medicine, which is role of the National Institutes of Wellness (NIH). Over 1,000,000 bioassays for more than nine,000 protein targets can be accessed online contributed by more than seventy small molecule and RNAi screening centers and research laboratories. It is also supported by over 300 small molecule vendors contributing to the growing chemical compound database of PubChem worldwide. Vendors include US government-funded institutions, research laboratories pharmaceutical companies, and collaborators hosting chemical biological science databases. Other HTS repositories, such as ChEMBL or BindingDB, are alternatives to PubChem with different philosophies of annotation and evaluation of chemical biology datasets with their respective databases. A review of these HTS repositories can exist found here [9–13].
False positive rate in primary HTS experiments is loftier
Typically, principal HTS experiments categorize small molecules as hit, inactive, or unspecified nigh the desired biological activity. However, depending on the design of the HTS experiment, there are many other reasons why a chemical compound might be designated as striking ranging from activity of the chemical compound an undeclared target in the cell to optical interference. Therefore, primary screens are simply a offset iteration that reduces the bachelor chemical compound library to a smaller set that can exist interrogated in more detail. Equally compounds are tested without replication (singleton?) and the cutting off for activity is typically loose to minimize the number of false negatives, the false positive rate can be loftier. Although outliers are common in HTS experiments, statistically robust methods non sensitive to outliers are necessary for hit selection, e.chiliad., z*-score, SSMD*, B-score, and quantile-based methods [xiv]. Confirmatory screens act equally a validation filter by testing hit compounds with multiple replications of the experiment, recording concentration response curves, examination hitting compounds with an identical assay setup but in the absence of the putative target poly peptide, and sometimes exclude fifty-fifty compounds that act on the target protein but non selectively.
Hierarchical confirmatory screening experiments validate chief hit compounds
The biological analysis database of PubChem allows for the deposition of primary besides as confirmatory HTS experiments. Due to the requirement from funding agency on data sharing, primary screening results from NIH funded HTS projects were often deposited to PubChem prior the deposition of confirmatory assays and counter screens. Confirmatory assays seek to plant the relationship between chemical structure and a defined biological outcome (SAR). Confirmatory assays applications range from validating active compounds identified in the main screen, over the target confirmation through orthogonal assays, and determination of specificity through testing confronting other subtypes of the target protein or related proteins. For molecular probe development, confirmatory assays are used to investigate a smaller subset of often similar compounds to investigate the SAR around the given scaffold further. A hierarchy of confirmatory assays is established when results of dependent confirmatory screens are analysed. In progressed stages of the hierarchy, concentration response experiments provide values for half maximal effective concentration (EC50) or inhibition (IC50) in addition to the determined binary active/inactive upshot. Despite of multiple update mechanisms provided by the PubChem organization, datasets regarding the same HTS analysis projection simply deposited nether dissimilar time lines are sometimes not sufficiently summarized. Upon completion of the HTS project, a curation procedure is necessary to comprise all experimental data from different stages of the assay project and provide a dataset with the ultimate bioactivity outcomes.
Previous studies underline importance of chemical data curation for LB-CADD modelling
In a previous study, we assembled nine datasets from HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods encounter (Tabular array 1). Emphasis was placed on biological target diversity and the loftier quality HTS activity obtained through confirmatory screen validation. These collated datasets provided the foundation for an extensive LB-CADD benchmarking study using the cheminformatics framework BCL: ChemInfo [15]. For the present manuscript, we collaborate with PubChem to make these datasets easily accessible for all researchers.
Table 1
Listing of datasets containing curated compounds uploaded to PubChem.
| Protein Target | Target Class | Internal ID | Number of Actives | PubChem AID |
|---|---|---|---|---|
| Orexin1 Receptor | GPCR | SAID_435008 | 234* | 743306 |
| M1 Muscarinic Receptor agonists | GPCR | SAID_1798 | 188 | 652178 |
| M1 Muscarinic Receptor antagonists | GPCR | SAID_435034 | 447* | 1053187 |
| Potassium Ion Channel Kir2.1 | Ion Channel | SAID_1843 | 172 | 743120 |
| KCNQ2 potassium channel | Ion Channel | SAID_2258 | 287* | 1159610 |
| Cav3 T-type Calcium Channels | Ion Channel | SAID_463087 | 703 | 1053190 |
| Choline Transporter | Transporter | SAID_488997 | 256* | 1053196 |
| Serine/Threonine Kinase 33 | Kinase Inhibitor | SAID_2689 | 172 | 743321 |
| Tyrosyl-DNA Phosphodiesterase | Enzyme | SAID_485290 | 292 | 489007 |
| NPY-Y1 Receptor | GPCR | SAID_1040 | 801 | 1159609 |
| NPY-Y2 Receptor | GPCR | SAID_793 | 699 | 1159608 |
These datasets were selected with the goal to encompass a broad-range of poly peptide target classes. Each target class is represented by a sampled chemical space, spanned by the screened molecules evaluated within related HTS assay experiments. Primary and confirmatory screens were curated from PubChem and this curation procedure represent a tool for more systematic benchmarking of novel LB-CADD algorithms. For this manuscript, each curated dataset was re-assembled and aligned by CIDs before beingness uploaded into PubChem. Datasets marked with an asterisk in Table 1 have been modified with respect to our previous report due to chemical compound alignment past common substructure overlap rather than PubChem identifier (CID).
Significance
LB-CADD is particularly bonny in the resource-limited environment of academia as it reduces the cost and increases quality of drug discovery and/or probe development. Quantitative structure-activity-relationship (QSAR) models developed in LB-CADD are only as good as the data quality used for training such models. Thus, there is a pressing need to develop and systematically employ HTS assay record curation protocols helpful in the pre-processing of any chemical dataset. This manuscript highlights difficulties when working with HTS experimental information in the public domain and illustrates the curation process on ii chosen examples targets also as the re-upload of the new datasets into PubChem.
Establishing a dataset "aureate standard" for benchmarking novel LB-CADD methods is of import for testing operation of new algorithms in respect to the complication of the chemic space and for different biological targets. It as well counters a trend that newly adult methods are tested on proprietary datasets which creates difficulties when reproducing results and reduces transparency when comparing methodological advances in LB-CADD method evolution. As chemical infinite differs in complexity for each protein target, it is imperative for new LB-CADD methods to be benchmarked on representative high quality datasets. The here described curation process has the potential to provide a wide range of college quality datasets freely accessible to the research field.
Materials and Methods
Curation process based on hierarchy of confirmatory high-throughput screens validates active compounds
The following curation process evaluates the description of PubChem assays, identifies the PubChem assay ID (AID) of the primary screen and discusses the validation and classification of agile compounds from confirmatory screens. Confirmatory screens tin be subdivided into the categories "confirming" and "descriptive". "Confirming" assays validated a compound equally agile at a declared molecular target (eastward.g., testing the compounds in the presence and absence of the declared target). The application of "confirming" assays results in identification of a set of validated hits.
The 2nd sub-category is "descriptive". Typically, "descriptive" assays occupy a position in the bureaucracy downstream from the confirmatory assays. An example of a descriptive assay is a "counter screen" confronting another molecular target. Since the chemical compound activeness has been validated, it is viewed equally a validated striking. Additional data add to our understanding of the chemical compound'southward activity. east.one thousand., a compound could exist demoted from "active" to "inactive" based on a descriptive analysis. Even so, this would exist in the context of a previously declared intent (e.m., antagonists of the NPY Y1 receptor) and a gating criterion (e.g., l-fold selective confronting Y1). Such criteria are usually used but demand to be highlighted in the context of curating a data prepare. Hither, validated hits are active at the declared target but tin be declared "inactive" inside the context of the curated dataset when boosted "descriptive" data is taken into consideration. To construct a final dataset, the inactive compounds are taken from the corresponding principal analysis. Withal, the authors would like to emphasize that this manuscript does not endorse or vouch for the applied HTS methods, given assay results or interpretations of the mentioned assays below. The hither described curation procedure merely utilizes the assay outcomes given by the analysis providers and the screening facilities.
Results
High-throughput screens validate active compounds associated with NPY – Y1 and Y2 HTS screens
PubChem provides publicly bachelor biological assay results for a diverse set of poly peptide targets. For the telescopic of this manuscript, we chose neuropeptide Y (NPY) receptor type 1 and two, (Y1 and Y2). These receptors are members of a larger family of NPY receptors (Y1, Y2, Y4, Y5) which are part of the family of G-poly peptide-coupled receptors (GPCR) [sixteen,17]. As their proper noun suggests, the receptors are effectors of the neuropeptide neurotransmitter NPY, studies have implicated these receptors in diverse biological events, including feeding, alcoholism, anxiety and depression, pain perception, immunity and inflammation, vascular remodeling hypothermia, and bone and energy metabolism [18–25]. Due to the varied role of these receptors in human disease and physiology, the identification of high-affinity selective probes that target each receptor subtype may provide novel tools for the study of NPY-related pathologies.
Example written report: curating primary jail cell-based high-throughput screening analysis for antagonists of the Y1 receptor
In this case study, the PubChem assay AID1040 tests compounds for their ability to human activity as antagonists of the NPY receptor Y1. A prison cell line transfected with Y1 and a cyclic-nucleotide gated aqueduct (CNGC) was used to measure Y1 antagonism past the test chemical compound. The cells were treated with the β-adrenergic receptor agonist, isoproterenol, to activate adenylate cyclase, thus increasing cytosolic cyclic adenosine monophosphate (campsite) concentrations, and therefore increasing CNGC activity. Elevated CNGC activity decreases the cell membrane potential, which is measured using a membrane potential-sensitive fluorescent probe. Because the Y1 receptor is Gi-coupled, addition of the NPY counteracts isoproterenol activeness resulting in a subtract in CNGC activity. A tested compound that is an Y1 adversary will counteract NPY action, thus the isoproterenol-evoked high level of cAMP will be maintained and high CNGC action volition be preserved. This primary assay AID1040 tested 196,255 compounds and identified 1,990 actives. A subset of 1,195 hit compounds from the set of 1,990 agile compounds was investigated further by the following two confirmatory screens. AID1254 repeated the main screen experiment to validate activity for the hit compounds. AID1255 tested selectivity of hit compounds by removing antagonists of the Y2 receptor. This assay used a jail cell line transfected with the Y2 receptor and a cyclic-nucleotide gated aqueduct (CNG) was used to mensurate receptor antagonism through CNGC opening. This assay serves as an elimination of "imitation positives" in this context that could effect from modulation of other biological protein targets. The findings of AID1255 resulted in 332 compounds active confronting Y2. 252 compounds were ultimately confirmed through AID1254 as agile and selective. The post-obit two HTS screens (AID1277 and AID1278) correspond a second level of validation and further investigated a smaller fraction of just 63 compounds. AID1277 determines concentration response curves for a subset of compounds identified every bit active in the previous experiments. Multiple criteria for testing the compounds had to be fulfilled. The compounds were agile confronting the primary screen (AID1040). Compounds confirmed inactive by the confirmatory screen AID1254 were excluded. Additionally, these compounds had to be inactive when assessing Y2 antagonism through AID1255. The final fix of active compounds is comprised of 801 active molecules, taken from the actives of AID1040, subtracting inactive compounds from AID1254, subtracting actives from AID1255, subtracting inactive from AID1277, and actives from AID1278 equally shown in Figure 1. 'Active' compounds within this context are defined as a combination of active and selective compounds for Y1.
Curation process of AID1040. The middle green pointer represents the initial set of active compounds while red arrows symbolize a specific subtraction of compounds.
Case written report: curating primary cell-based high-throughput screening assay for agonists of the Y2 receptor
This study investigated small molecules for antagonism of NPY receptor Y2. A cell line transfected with Y2 and CNGC using a primary screening assay similar to the assay described for Y1 receptor, above. This principal screen AID793) tested 140,092 molecules for action and identified 1,384 hit compounds. The confirmatory screen AID1257 evaluated a subset of 707 from the 1,384 molecules in more than item. Information technology confirmed activity of compounds that were identified as actives in the primary screen AID793 with the same experimental analysis setup. 707 compounds were tested in more detail and 479 molecules were confirmed inactive, and thus subtracted from the initial ready of active compounds. On the other hand, AID1256 was designed to place non-selective antagonists among the actives of the primary screen because of inhibition of the Y1 receptor. The same set of 707 compounds was screened and 135 compounds were removed as not-selective. The next stage of confirmatory screens evaluated a more specific subset of 119 compounds. Assay AID1279 determined whether compounds are active confronting the main screen (AID793), activity for antagonism towards Y2 had to exist confirmed in AID1257, and whether the chemical compound showed activity in the cell-based HTS assay measuring Y1 antagonism (Aid 1256). Out of the 119 actives molecules 74 compounds were confirmed and thus excluded from the pool of overall actives. The 2d assay (AID1272) screened the same 119 compounds as AID1279 but evaluated each molecule by different criteria: The compounds had to be active in the primary screen AID793. This activity had to be confirmed in AID1257. And lastly, these compounds had to exist inactive with respect to measuring Y1 antagonism (Help 1256). A total of 119 compounds were screened and 47 inactive compounds were confirmed and removed. Next, a layer of counter screens AID2210, AID2212, AID2224, involved in this series evaluated 89 compounds for cross-findings among actives for agonism of Y1 and antagonism for Y2 and inhibition of cyclic nucleotide gated ion channel (CNGC) activity. Agile compounds found through those assays were excluded from the set up of final actives. Finally, every bit assays for late stage results from probe development efforts to place antagonists of NPY-Y2, AID2211 and AID2220 were set up up with the aforementioned conditions as AIDs 793, 1256, 1257, 1272, and 1279. Non-selective Y2 agonists and compounds acting every bit Y1 agonists were excluded. Figure 2 shows a detailed flow chart depicting the individual compound subtractions.
Curation procedure of AID793. The center green arrow leads to the final ready of agile compounds while blood-red arrows and numbers and blazon of compounds in red mark compound subtractions.
In summary, the assembly of the final actives dataset, an ensemble of 699 active compounds was determined by selecting the actives from the primary screen and excluding inactive compounds of AID1257 and AID1272, besides equally subtracting actives from AID1256, AID1279, AID2210, AID2211, AID2212, AID2220, and AID222.
Discussion
Uploading of curated datasets into PubChem
PubChem provides access to biological assay information e.g., through its Power User Gateway (PUG) [26]. Data queries tin be sent via XML to request Assistance data for molecule in a specific format (due east.m., SDF, SMILES) as well as the associated biological assay data containing metadata, and activity related data. Every compound is uniquely identified past its chemical compound identifier (CID) or substance identifier (SID). Sets of molecules can exist downloaded in respect to a given Help. These identifier in conjunction with the activity categorization of a compound allows for the curation of sets of molecules of confirmatory screens as discussed, the two example studies (see above).
Through the hierarchical relationship of primary and confirmatory analysis experiments, compounds can exist aligned by their respective CID. Dependent on the outcome on each hierarchy level, compounds tin be classified equally agile or inactive depending on the result of the final involved confirmatory screen. The ensemble of molecules that satisfies all levels of the HTS bureaucracy represents the final curated dataset.
The PubChem Upload system (pubchem.ncbi.nlm.nih.gov/upload) offers a machinery to submit the newly curated set of compounds into PubChem. After specifying which compounds are involved past specified past SID identifiers the aligned hierarchy of compound activities through all involved HTS results can be uploaded. Once the submission was successful and approved by a PubChem curator the newly curated dataset is accessible to the public and tin be shared with the research community.
Conclusions
Loftier-quality HTS datasets are of import for LB-CADD method development. However, results of various validation experiments for an assay projection are often reported separately in PubChem and final fix of inactive, inconclusive, and conformed active compounds is more often than not lacked in the database. The goal of this piece of work is to provide an overview of a curation procedure, starting from master screens and their associated confirmatory screens, edifice a hierarchical construction through multiple related assay experiments. It needs to be emphasized that the applied HTS methodologies, the given assay results, and interpretations are taken 'as is'. Thus, curation process relies on a high-quality standard for experimental data given by assay providers and the screening facilities. The assembly and upload of the curated dataset to the PubChem database is discussed based on two specifically chosen protein target use-cases. The upload of curated datasets into PubChem is described and thus supports the development of a publicly available database for benchmarking LB-CADD methods. Ultimately, availability of such datasets will eliminate the need to test LB-CADD methods on proprietary datasets allowing fix reproduction and comparison of results. Furthermore, such curation projects help to raise the utility of HTS information in the PubChem database past summarizing and excluding false positives and experimental artifacts at various analysis stages, and thus to highlight confirmed biological compounds.
Acknowledgments
Work in the Meiler laboratory is supported through NIH (R01 GM080403, R01 GM099842, R01DK097376) and NSF (CHE 1305874).
Footnotes
Competing Interests
The authors declare that they have no competing interests.
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