The Nuclear Magnetic Resonance Spectral Measurement Database (NMR-SMDB) was developed for the purpose of organizing and searching NMR spectral data of protein therapeutics, linking spectra to corresponding sample information and enabling quick access to full datasets and entire studies. In addition to supporting internal NIST research, the system could facilitate data access to stakeholders outside of NIST, and future versions of the database software itself could be installed by others for their own data storage and retrieval.

This repository contains the dataset used in the manuscript "Inter-tool analysis of a NIST dataset for assessing baseline nucleic acid sequence screening". NIST constructed the test dataset based on the current screening recommendations from HHS. The dataset is a FASTA formatted file with blinded numerical sequence headers. The dataset was sent to sequence screening tool developers for initial testing and to obtain feedback about its utility for assessing baseline sequence screening. An additional metadata file provides the NIST-assigned label for each sequence, along with a more detailed description derived from the source database.

Background The C-terminal four amino acids (GEEV) of human α1A-adrenergic receptors (ARs) have been reported to interact with the PDZ domain of neuronal nitric oxide synthase (nNOS) in a yeast two-hybrid system. The other two α1-AR subtypes have no sequence homology in this region, raising the possibility of subtype-specific protein-protein interactions. Results We used co-immunoprecipitation and functional approaches with epitope-tagged α1-ARs to examine this interaction and the importance of the C-terminal tail. Following co-transfection of HEK-293 cells with hexahistidine/Flag (HF)-tagged α1A-ARs and nNOS, membranes were solubilized and immunoprecipitated with anti-FLAG affinity resin or anti-nNOS antibodies. Immunoprecipitation of HFα1A-ARs resulted in co-immunoprecipitation of nNOS and vice versa, confirming that these proteins interact. However, nNOS also co-immunoprecipitated with HFα1B- and HFα1D-ARs, suggesting that the interaction is not specific to the α1A subtype. In addition, nNOS co-immunoprecipitated with each of the three HFα1-AR subtypes which had been C-terminally truncated, suggesting that this interaction does not require the C-tails; and with Flag-tagged β1- and β2-ARs. Treatment of PC12 cells expressing HFα1A-ARs with an inhibitor of nitric oxide formation did not alter norepinephrine-mediated activation of mitogen activated protein kinases, suggesting nNOS is not involved in this response. Conclusions These results show that nNOS does interact with full-length α1A-ARs, but that this interaction is not subtype-specific and does not require the C-terminal tail, raising questions about its functional significance.

Data S1. Example of NMReDATA.sdf file including NMReDATA for benzo[a]pyrene Data S2. Pure text translation of the NMReDATA.sdf of benzo[a]pyrene. This dataset is associated with the following publication: Pupier, M., J. Nuzillard, J. Wist, N.E. Schlörer, S. Kuhn, M. Erdelyi, C. Steinbeck, A. Williams, C. Butts, T.D.W. Claridge, B. Mikhova, W. Robien, H. Dashti, H.R. Eghbalnia, C. Farès, C. Adam, P. Kessler, F. Moriaud, M. Elyashberg, D. Argyropoulos, M. Pérez, P. Giraudeau, R.R. Gil, P. Trevorrow, and D. Jeannerat. NMReDATA, a standard to report the NMR assignment and parameters of organic compounds. Magnetic Resonance in Chemistry. John Wiley & Sons, Inc., Hoboken, NJ, USA, 56(8): 703-715, (2018).

Data S1. Example of NMReDATA.sdf file including NMReDATA for benzo[a]pyrene Data S2. Pure text translation of the NMReDATA.sdf of benzo[a]pyrene. This dataset is associated with the following publication: Pupier, M., J. Nuzillard, J. Wist, N.E. Schlörer, S. Kuhn, M. Erdelyi, C. Steinbeck, A. Williams, C. Butts, T.D.W. Claridge, B. Mikhova, W. Robien, H. Dashti, H.R. Eghbalnia, C. Farès, C. Adam, P. Kessler, F. Moriaud, M. Elyashberg, D. Argyropoulos, M. Pérez, P. Giraudeau, R.R. Gil, P. Trevorrow, and D. Jeannerat. NMReDATA, a standard to report the NMR assignment and parameters of organic compounds. Magnetic Resonance in Chemistry. John Wiley & Sons, Inc., Hoboken, NJ, USA, 56(8): 703-715, (2018).

Neuronal nitric oxide synthase (nNOS) inhibition tests were carried out on peptides in addition to other bioactivity experiments, in particular, antibiotic activity. Inhibition of nNOS was measured by monitoring the conversion of [3H]arginine to [3H]citrulline.Information recorded: individual identifier for sample, peptide sequence, molecular weight, stock concentration (mg/ml), source (native peptide, synthetic derivative), solvent and concentration. To test whether the peptides inhibit nNOS. Peptides tested: aurein, caerin, citropin, cupiennin, dahlein, frenatin, lesueurin, rothein, signiferin, uperin.Sources of native peptides: Cirinia signifera; Cupiennius salei; Litoria aurea, L. caerulea, L. citropa, L. chloris, L. dahlii, L. electrica, L. gilleni, L. gracilenta, L. infrafrenata, L. lesueuri, L. rothii, L. rubella, L. splendida, hybrid L. caerulea/splendida; Uperoleia mjobergii.The first report of neuronal nitric oxide synthase inhibition by a component of a spider venom (Cupiennius salei).Subsets of the data have been used in a number of studies.

,The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) facilitates finding and comparing neuroimaging resources for functional and structural neuroimaging analyses—including popular tools as well as those that once might have been hidden in another researcher's laboratory or some obscure corner of cyberspace. NITRC collects and points to standardized information about tools, making the task of finding and comparing them easier than before. The site can help researchers find the right functional or structural neuroimaging tool or resource for their research. NITRC has recently added services such as cloud-based computing and data storage, and is broadening the range of scientific domains from MR to PET, SPECT, CT, MEG/EEG and optical imaging. Additional domains of interest are digital atlasing, and computational neuroscience, including large-scale and multi-scale modeling. The NITRC team searches out relevant research tools and resources to house on the site. Researchers can compare tools on NITRC and developers can seek and receive help from the community to make their tools more usable and accessible.,

Background High density cDNA microarray technology provides a powerful tool to survey the activity of thousands of genes in normal and diseased cells, which helps us both to understand the molecular basis of the disease and to identify potential targets for therapeutic intervention. The promise of this technology has been hampered by the large amount of biological material required for the experiments (more than 50 μg of total RNA per array). We have modified an amplification procedure that requires only 1 μg of total RNA. Analyses of the results showed that most genes that were detected as expressed or differentially expressed using the regular protocol were also detected using the amplification protocol. In addition, many genes that were undetected or weakly detected using the regular protocol were clearly detected using the amplification protocol. We have carried out a series of confirmation studies by northern blotting, western blotting, and immunohistochemistry assays. Results Our results showed that most of the new information revealed by the amplification protocol represents real gene activity in the cells. Conclusion We have confirmed a powerful and consistent cDNA microarray procedure that can be used to study minute amounts of biological tissue.

Background Sites in DNA that bind regulatory proteins can be detected computationally in various ways. Pattern discovery methods analyze collections of genes suspected to be co-regulated on the evidence, for example, of clustering of transcriptome data. Pattern searching methods use sequences with known binding sites to find other genes regulated by a given protein. Such computational methods are important strategies in the discovery and elaboration of regulatory networks and can provide the experimental biologist with a precise prediction of a binding site or identify a gene as a member of a set of co-regulated genes (a regulon). As more variations on such methods are published, however, thorough evaluation is necessary, as performance may differ depending on the conditions of use. Detailed evaluation also helps to improve and understand the behavior of the different methods and computational strategies. Results We used a collection of 86 regulons from Escherichia coli as datasets to evaluate two methods for pattern discovery and pattern searching: dyad analysis/dyad sweeping using the program Dyad-analysis, and multiple alignment using the programs Consensus/Patser. Clearly defined statistical parameters are used to evaluate the two methods in different situations. We placed particular emphasis on minimizing the rate of false positives. Conclusions As a general rule, sensors obtained from experimentally reported binding sites in DNA frequently locate true sites as the highest-scoring sequences within a given upstream region, especially using Consensus/Patser. Pattern discovery is still an unsolved problem, although in the cases where Dyad-analysis finds significant dyads (around 50%), these frequently correspond to true binding sites. With more robust methods, regulatory predictions could help identify the function of unknown genes.