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1,744 results for “peptide”
Molecular dynamics simulation data of designed cyclic peptide - ligand 4 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 4. Due to the file size limitation, ligand 1-3 data and simulation set-up files can be found here: http://doi.org/10.5281/zenodo.3780463<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Molecular dynamics simulation data of designed cyclic peptide - ligand 1-3 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 1-3. Ligand 4 data can be found here: http://doi.org/10.5281/zenodo.3782629<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Umbrella simulation data of designed cyclic peptide as MDM2 binders
<p>Umbrella simulation data of designed cyclic peptide as MDM2 binders.</p> <p>Pulling simulation: start from bound MDM2/ligand to pull the ligand out</p> <p>Pushing simulation: start from unbound ligand to push it bind to MDM2</p> <p>Details of umbrella simulation parameters can be found in simulation set-up folders.</p>
HLA Class II specificity assessed by high-density peptide microarray interactions
<p>The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery; something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I (MHC-I) prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC-I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Since MHC-II restricted CD4+ T cells control and orchestrate most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data is key to the solution of this problem; a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse and representative data. Here, we have used recombinant HLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, <em>in casu</em> containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated. Collectively, with a cost per peptide reduced to a few cents combined with the flexibility of recombinant HLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.</p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_DDA_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_NCE_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Vasoactive intestinal peptide as a mediator of the effects of a supergene on social behavior
<p>Supergenes, or linked groups of alleles that are inherited together, present excellent opportunities to understand gene-behavior relationships. In white-throated sparrows (<i>Zonotrichia albicollis</i>), a supergene on the second chromosome associates with a more aggressive and less parental phenotype. This supergene includes the gene for vasoactive intestinal peptide (VIP), a neuropeptide known to play a causal role in both aggression and parental behavior. Here, using a free-living population, we compared levels of VIP mRNA between birds with and without the supergene. We focused on the anterior hypothalamus and infundibular region, two brain regions containing VIP neurons known to play a causal role in aggression and parental behavior, respectively. First, we show that the supergene enhances VIP expression in the anterior hypothalamus and that expression positively predicts vocal aggression independently of genotype in both sexes. Next, we show that the supergene reduces VIP expression in the infundibular region, which suggests reduced secretion of prolactin, a pro-parental hormone. Thus, patterns of VIP expression in these two regions are consistent with the enhanced aggression and reduced parental behavior of birds with the supergene allele. Our results illustrate mechanisms by which elements of genomic architecture, such as supergenes, can contribute to the evolution of alternative behavioral phenotypes.</p>
Targeted Intracellular Degradation of SARS-CoV-2 via Computationally-Optimized Peptide Fusions
<p>The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has elicited a global health crisis of catastrophic proportions. With only a few vaccines approved for early or limited use, there is a critical need for effective antiviral strategies. In this study, we report a unique antiviral platform, through computational design of ACE2-derived peptides which both target the viral spike protein receptor binding domain (RBD) and recruit E3 ubiquitin ligases for subsequent intracellular degradation of SARS-CoV-2 in the proteasome. Our engineered peptide fusions demonstrate robust RBD degradation capabilities in human cells and are capable of inhibiting infection-competent viral production, thus prompting their further experimental characterization and therapeutic development. </p>
Sensitive mass spectrometric determination of kinin-kallikrein system peptides in light of COVID-19
<p>This dataset provides information on the obtained MS/MS data generated during the conducted validation runs.</p> <p> </p> <p>Abstract of the corresponding paper:</p> <p>The outbreak of COVID-19 has raised interest in the kinin–kallikrein system, as symptoms were postulated to be in connection. Viral blockade of the angiotensin-converting-enzyme 2 impedes degradation of the active kinin des-Arg(9)-bradykinin, which thus increasingly activates bradykinin receptors known to promote inflammation, cough, and edema – symptoms that are commonly observed in COVID-19. However, lean and reliable investigation of the postulated alterations is currently hindered by non-specific peptide adsorption, lacking sensitivity, and cross-reactivity of applicable assays. Here, an LC-MS/MS method was established to determine the following kinins in respiratory lavage fluids: kallidin, bradykinin, des-Arg(10)-kallidin, des-Arg(9)-bradykinin, bradykinin 1-7, bradykinin 2-9 and bradykinin 1-5. This method was fully validated according to regulatory bioanalytical guidelines of the European Medicine Agency and the US Food and Drug Administration and has a broad calibration curve range (up to a factor of 10<sup>3</sup>), encompassing low quantification limits of 4.4–22.8 pg/mL (depending on the individual kinin). The application of the developed LC-MS/MS method to nasal lavage fluid allowed for the rapid (~2 hours), comprehensive and low-volume (100 µL) determination of kinins for the first time. Hence, this novel assay may support current efforts to investigate the pathophysiology of COVID-19, but can also be extended to other diseases.</p>
Raw data for the article "Small Peptide Diversification through Photoredox-Catalyzed Oxidative C-Terminal Modification"
<p>Raw IR, NMR and Mass data for the article "Small Peptide Diversification through Photoredox-Catalyzed Oxidative C-Terminal Modification" published in Chemical Science: </p> <p><a href="https://doi.org/10.1039/D0SC06180H">https://doi.org/10.1039/D0SC06180H</a></p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Phosphotyrosine peptide abundance in control and Cul5-deficient MCF10A cells
<p>The Cullin 5 RING ligase complex inhibits Src activity and Src-dependent transformation of MCF10A epithelial cells, in part by targeting pY proteins such as pYCas for degradation by the ubiquitin-proteasome system (Teckchandani et al., 2014). Because overexpression of Cas alone did not phenocopy CRL5 inhibition (Teckchandani et al., 2014), we infer that CRL5 down-regulates additional pY proteins that become limiting when Cas is over-expressed. We sought to identify such pY proteins by screening for pY peptides whose abundance increases when Cul5 is inhibited. To this end, control and Cul5-deficient MCF10A cells were lysed under denaturing conditions, proteins were digested with trypsin, and peptides were labeled with isobaric TMT tags for quantitative pY proteomics (Zhang et al., 2007). In one experiment, samples were prepared from control and Cul5-deficient cells that were starved for epidermal growth factor (EGF) for 0, 24 or 72 hr. Starvation time had no systematic effect on peptide abundance, so, in a second experiment, we prepared biological triplicate samples from growing control and Cul5-deficient cells. Sixteen pY peptides increased significantly in Cul5-deficient cells in both experiments, including pY128 from Cas and pY117 and pY266 from BCAR3.</p>
Data from: Controlled release of basic fibroblast growth factor from a peptide biomaterial for bone regeneration
<p>Self-assembled peptide scaffolds based on D-RADA16 (D16) are an important matrix for controlled drug release and 3D cell culture. In this work, D16 peptide hydrogels were coated on artificial bone composed of nano-hydroxyapatite/polyamide 66 (NHA/PA66) to obtain a porous drug-releasing structure for treating bone defects. The developed materials were characterized via transmission electron microscopy (TEM), scanning electron microscopy (SEM). The proliferation and adhesion of bone mesenchymal stem cells (BMSCs) were examined by Confocal laser microscopy (CLS) and CCK-8 experiments. The osteogenic ability of the porous materials towards bone BMSCs was examined in vitro by staining with Alizarin Red S and alkaline phosphatase (ALP) and bioactivity were evaluated in vivo. The results revealed that NHA/PA66/D-RADA16/bFGF reduce the degradation rate of D16 hydrogels and prolong sustained release of bFGF, which would promote BMSCs proliferation, adhesion and osteogenesis in vitro and bone repair in vivo. Thus, it deserves more attention and is worthy of further research.</p>
X-ray diffraction images for DPF3 tandem PHD fingers co-crystallized with an acetylated histone-derived peptide
<p>This submission includes a tar archive of bzipped diffraction images recorded with the ADSC Q315r detector at the Advanced Photon Source of Argonne National Laboratory, Structural Biology Center beam line 19-ID. Relevant meta data can be found in the headers of those diffraction images.</p> <p>Please find below the content of an input file XDS.INP for the program XDS (Kabsch, 2010), which may be used for data reduction. The "NAME_TEMPLATE_OF_DATA_FRAMES=" item inside XDS.INP may need to be edited to point to the location of the downloaded and untarred images.</p> <p>!!! Paste lines below in to a file named XDS.INP</p> <p>DETECTOR=ADSC MINIMUM_VALID_PIXEL_VALUE=1 OVERLOAD= 65000<br /> DIRECTION_OF_DETECTOR_X-AXIS= 1.0 0.0 0.0<br /> DIRECTION_OF_DETECTOR_Y-AXIS= 0.0 1.0 0.0<br /> TRUSTED_REGION=0.0 1.05<br /> MAXIMUM_NUMBER_OF_JOBS=10<br /> ORGX= 1582.82 ORGY= 1485.54<br /> DETECTOR_DISTANCE= 150<br /> ROTATION_AXIS= -1.0 0.0 0.0<br /> OSCILLATION_RANGE=1<br /> X-RAY_WAVELENGTH= 1.2821511<br /> INCIDENT_BEAM_DIRECTION=0.0 0.0 1.0<br /> FRACTION_OF_POLARIZATION=0.90<br /> POLARIZATION_PLANE_NORMAL= 0.0 1.0 0.0<br /> SPACE_GROUP_NUMBER=20<br /> UNIT_CELL_CONSTANTS= 100.030 121.697 56.554 90.000 90.000 90.000<br /> DATA_RANGE=1 180<br /> BACKGROUND_RANGE=1 6<br /> SPOT_RANGE=1 3<br /> SPOT_RANGE=31 33<br /> MAX_CELL_AXIS_ERROR=0.03<br /> MAX_CELL_ANGLE_ERROR=2.0<br /> TEST_RESOLUTION_RANGE=8.0 3.8<br /> MIN_RFL_Rmeas= 50<br /> MAX_FAC_Rmeas=2.0<br /> VALUE_RANGE_FOR_TRUSTED_DETECTOR_PIXELS= 6000 30000<br /> INCLUDE_RESOLUTION_RANGE=50.0 1.7<br /> FRIEDEL'S_LAW= FALSE<br /> STARTING_ANGLE= -100 STARTING_FRAME=1<br /> NAME_TEMPLATE_OF_DATA_FRAMES= ../x247398/t1.0???.img</p> <p>!!! End of XDS.INP</p> <p> </p> <p> </p>
pH-Responsive, Lysine-Based, Hyperbranched Polymers Mimicking Endosomolytic Cell-Penetrating Peptides for Efficient Intracellular Delivery-DATA
<p>Original data and supporting data for the paper entitled "pH-Responsive, Lysine-Based, Hyperbranched Polymers Mimicking Endosomolytic Cell-Penetrating Peptides for Efficient Intracellular Delivery".</p>
Supplementary Material: Quantifying Intermolecular Interactions in Asymmetric Peptide Organocatalysis as a Key Towards Understanding Selectivity
<p>Supplementary material to the publication "Quantifying Intermolecular Interactions in Asymmetric Peptide Organocatalysis as a Key Towards Understanding Selectivity".</p> <p>The files contain the raw nuclear magnetic resonance (NMR) spectra, spreadsheets with the NMR observables extracted, pulse sequences used, and MATLAB scripts for data analysis.</p> <p>A readme-file with detailed information is provided.</p>
Data regarding "Development and evaluation of RADA-PDGF2 self-assembling peptide hydrogel for enhanced skin wound healing"
<p>Raw data for data published in Development and evaluation of RADA-PDGF2 self-assembling peptide hydrogel for enhanced skin wound healing, Front. Pharmacol. Sec. Experimental Pharmacology and Drug Discovery Volume 14 - 2023 | <a href="https://doi.org/10.3389/fphar.2023.1293647">doi: 10.3389/fphar.2023.1293647</a></p>
Data from: programming co-assembled peptide nanofiber morphology via anionic amino acid type: insights from molecular dynamics simulations
<p>Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C's are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter β-sheets, have stronger interactions between charged residues on opposing β-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships.</p>
Raw data for the article "Synthesis of Fluorescent Cyclic Peptides via Gold(I)-Catalyzed Macrocyclization"
<p>Raw NMR, IR, MS fluorescence and imaging data for the article "Synthesis of Fluorescent Cyclic Peptides via Gold(I)-Catalyzed Macrocyclization" published in the Journal of the American Chemical Society, DOI: </p><p><a href="https://doi.org/10.1021/jacs.3c09261">https://doi.org/10.1021/jacs.3c09261</a></p><p>The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article. For convenience, the word file version of the supporting information can be found on the top of the raw data folder.</p>
Data and Scripts for Publication: Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy
<p>Data and scripts for the publication titled Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy.</p> <p>Article available at <a href="https://doi.org/10.1088/2399-6528/ad1f73">10.1088/2399-6528/ad1f73</a></p>
The raw data from FP and SPR assays for characterizing DCAF12 interactions with CCT5 and MAGEA3 peptides
<p>The source data underlying Figs 1A-E and Figs 2D-E for the DCAF12 manuscript (Title: Probing CRL4DCAF12 interactions with MAGEA3 and CCT5 di-Glu C-terminal degrons)</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.