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266 results for “Protein Modelling”
Data table 2 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table containing a list of proteins identified by MS from samples after a pull-down experiment using cerebral cortex lysates with Dyna beads coated with control isogenic mouse IgG. The proteins from the list were considered false positive interactors of ataxin-3 in the cortex.</p>
Data table 1 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table contains LFQ intensity and other raw MS data for proteins identified in fractions 3,4,5 (i), 11, 12, 13, (ii) 18, 19, and 20 (iii) from ki150 and ki21 model brains. These fractions showed enrichment in ATXN3 protein. The fractions are visualized in Figure 4 (<a href="http://doi.org/10.3389/fnmol.2023.1122308" target="_blank" rel="noopener">doi.org/10.3389/fnmol.2023.1122308</a>)</p>
Datasets of protein models from plasmids containing conjugative Type 4 Secretion Systems
<p>In the connected article, we have created a database of all modelled protein structures encoded on plasmids that contain conjugative type 4 secretion systems. In this deposition, you will find zip files of all structures modelled by AlphaFold, as well as the ones that were modelled using EMS fold. Further, there the csv file containing the DeepFRI output, as well as a fasta file containing the sequences of the plasmids.</p> <p>The AlphaFold and ESM databases contain the structural models of the curated/triaged proteins, as described in the paper.</p> <p> </p>
Training datasets for "Multi-purpose controllable protein generation via prompted language models"
<div> <p>The datasets used to tune modular prompts of PROPEND fall into three main categories based on their design objectives: tertiary structure, secondary structure, and functional annotation.</p> </div>
Dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling"
<p>This dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling" submitted to the Journal of Chemical Information and Modeling.</p> <p>Table S 1: Chemicals with their experimental values of logKch , and values of logKow and<span> </span>logKaw used to calibrate chicken muscle protein-water 2p-LFER model.</p> <p>Table S 2: Chemicals with their experimental values of logKfish and values of logKow and<span> </span>logKaw used to calibrate fish muscle protein-water 2p-LFER model.</p> <p>Table S 3: Chemicals with their experimental values of logKBSA and values of logKow and<span> </span>logKaw used to calibrate bovine serum albumin-water 2p-LFER model.</p> <p>Table S 4: Chemicals with their experimental values of logKpw and values of logKow and<span> </span>logKaw used to calibrate combined chicken and fish muscle protein-water 2p-LFER model.</p> <p>Table S 5: Diversity of data for logKpw.</p> <p>Table S 6: Diversity of data for logKBSA.</p> <p>Table S 7: Comparison of Experimental and 2p-LFER Predicted Partition Coefficients for ionizable PFAS Compounds.</p> <p>Table S 8: List of neutral fluorotelomer PFAS Compounds.</p> <p>Table S 9: List of experimental in vivo and in vitro partitioning data for different tissues and species.</p> <p>Table S 10: List of experimental Milk-water partition coefficient and predicted values of Milk-water partitioning.</p> <p>Table S 11: Training set for logKpw</p> <p>Table S 12: Validation set for log Kpw</p> <p>Table S 13: Training set for log KBSA</p> <p>Table S 14: Validation set for log KBSA</p>
Benchmark Dataset for Structure Refinement Methods of Protein Complex Models
<p>This is the dataset used in our work entitled " Benchmarking of Structure Refinement Methods for Protein Complex Model " by Jacob Verburgt and Daisuke Kihara, which is under review.</p> <p>ZDOCK Derived Benchmark Dataset:</p> <p>The primary benchmark set used in was directly derived from the <a href="https://zlab.umassmed.edu/benchmark/">ZDOCK Benchmark set</a>. The ZDOCK set contains four structures per target: An unbound ligand, an unbound receptor, a bound ligand, and a bound receptor. The benchmark set is available in such a way where the coordinates of the bound subunits are oriented identical to their complex structure, and the unbound subunits are superimposed onto their respective bound subunits. Our dataset creates the optimially oriented "unbound" complexes by combining the superimposed and unbound subunits, along with removal of waters, ligands, and other non-protein atoms. These unbound complexes are saved in the dataset in the form "XXXX_c_u.pdb", where XXXX is the PDB ID.</p> <p>From the complete ZDOCK Benchmark of 230 targets, 18 targets were removed due to containing multiple ligand chains, which is incompatible with the standard ligand to receptor model used within CAPRI. The ZDOCK PDB ID’s of these targets are 1AKJ", "1BJ1", "1DE4", "1EER", "1EXB", "1EZU", "1GP2", "1I9R", "1JMO", "1K74", "1N2C", "1QFW", "2HMI", "3EO1", "3HMX", "4FQI", "4GXU", and "9QFW".</p> <p>There are an additional 8 targets where the superimpostion of the ligand and receptor structures onto the complex led to entanglement of the chains and were subsequently removed from the dataset. The ZDOCK PDB IDs for these targets are "1BGX", "1H1V", "1IRA", "1R8S", "1Y64", "2OT3", "3AAD", "4GAM".</p> <p>Note:</p> <p>In the work, we also used CAPRI scoring model dataset derived from CAPRI rounds 38-45. This dataset is unable to be distributed directly by us due to CAPRI guidelines, but can be derived from "Scoring round" models from the <a href="https://www.ebi.ac.uk/pdbe/complex-pred/capri/">CAPRI Website </a>.</p> <p>The targets considered were T122-T125, T131-T133, and T136, as these were targets which contained globular protein ligands and receptors. Please contact us directly if you have any further questions on this dataset.</p>
QM/MM models of 11 red fluorescent proteins
<p>Geometry configurations on the ground state potential energy surface are obtained at the QM(PBE0-D3/cc-pvdz)/MM(AMBER) level. The list of fluorescent proteins is as follows:</p> <ol> <li>mRojoA</li> <li>mRojo-THSL</li> <li>mRojo-VFAV</li> <li>mRojo-TFAL</li> <li>mRojo-VYGV</li> <li>mRojo-VYGL</li> <li>mRojo-TYGV</li> <li>RDSmCherry0.2</li> <li>RDSmCherry0.5</li> <li>mKeima</li> <li>eqFP670</li> </ol>
Consensus machine-learning models for protein-ligand binding affinity estimation
<p><strong>Motivation:</strong> In structure-based virtual screening, machine learning based scoring function gained popularity in the last few years as they outperformed classical scoring function. The protein-ligand system can be encoded by a set of orthogonal descriptor spaces, which are then mined by machine learning algorithms to find a relationship with the binding affinity experimental value.</p> <p><strong> </strong></p> <p><strong>Results:</strong> In this work we propose our modelling approach to derive a new scoring function, derived from a combination of multiple descriptor spaces coupled with machine learning algorithms ensembled in consensus. The SF has been trained on the PDBbind v.2019 data and has been extensively internally and externally validated on a large set of complexes. When benchmarked on the PDBbind core set, it achieved better performance than state-of-the-art counterparts, scoring: R<sub>Pearson </sub>= 0.85-0.86 r<sup>2</sup> = 0.70-0.72 and RMSE = 1.15-1.21. As highlights: (i) an applicability domain definition has been implemented to delimit the SF’s application boundaries, and (ii) a mechanistic interpretation is proposed by investigating the contribution of each protein-ligand atom pairs in the prediction of the binding affinity, which could provide a support in the lead-optimization process.</p> <p><strong> </strong></p> <p><strong>Availability and implementation:</strong> Our scoring function is freely available through the webportal: <a href="https://predictor.exscalate.eu/">https://predictor.exscalate.eu/</a></p>
Data for "Evolutionary velocity with protein languge models"
<p>Data tar ball for "Evolutionary velocity with protein language models"; more information can be found here: https://github.com/brianhie/evolocity#data.</p>
Predicting and modeling protein-protein interactions in E. coli envelopome
<p>Structural models and Supplementary data described in the reference:</p> <p>Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.</p> <p>Mu Gao, Davi Nakajima An, and Jeffrey Skolnick<em>. eLife</em>, 2022. <strong>11</strong>: p. e82885.</p> <p>List of files:</p> <ul> <li>af2c_fea_220331.tar -- A tarball of input features of full E coli proteome to AF2Complex version 1.3.0. Feature files are pickled and gzipped, which AF2Complex v1.3 takes as input directly.</li> </ul> <p>Results of an application to E coli envelopome on four query proteins from the outer membrane biogenesis pathway.</p> <ul> <li>Supplementary Data.xlsx -- Virtual PPI screening results of PpiD, YfgM, SurA, and BamA.</li> <li>screening_top1_models.zip -- Compressed top 1 dimeric models of top hits from the PPI screening. Note that these models are unrelaxed.</li> <li>predicted structural models.zip -- Compressed structural models of supercomplexes formed in the OMP biogenesis pathway described in the reference.</li> </ul>
PIGNet2: A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening
<p>Training and test datasets of the paper "Improving the versatility of deep learning-based protein-ligand interaction prediction for accurate binding affinity scoring and virtual screening".</p>
Alphafold predicted structures of VPS13 proteins from model organisms
<p>This upload contains AlphaFold-predicted structures of VPS13 proteins from a variety of organisms. Given the large size of these proteins, only partial sequences were predicted with AlphaFold(1) and the resulting structures were aligned in PyMOL(2). A summary of the structures uploaded here is presented as a collection of domain cartoons in the "VPS13 domain organization across eukaryotic evolution.pdf" file. </p> <p>The structures were generated with AlphaFold v2.029 on the Yale High Performance Cluster. Each *.zip file contains the best ranked predictions (out of five) for each sequence (*.pdb files) and the PyMOL assembled full structure (*.pse file). In a few cases, where a good alignment was not possible due to long disordered regions in the C-terminal portions (mostly in proteins from <em>D. discoideum</em> and <em>A. thaliana</em>), the full structures were aligned manually in PyMOL based on the continuity of the lipid transfer groove. The structures in PyMOL can be colour-coded by the confidence value of AlphaFold predictions using the following prompt:</p> <p>set_color n0, [0.051, 0.341, 0.827]<br> set_color n1, [0.416, 0.796, 0.945]<br> set_color n2, [0.996, 0.851, 0.212]<br> set_color n3, [0.992, 0.490, 0.302]<br> color n0, b < 100; color n1, b < 90<br> color n2, b < 70; color n3, b < 50</p> <p>Considering that full length structures were assembled by aligning different protein fragments and in view of the presence of flexible loops with low prediction confidence scores, the relative positions of different folded domains are not necessarily correct.</p> <p> </p> <p><strong>References</strong></p> <p>1. J. Jumper, <em>et al.</em>, Highly accurate protein structure prediction with AlphaFold. <em>Nature</em> 596, 583–589 (2021).</p> <p>2. The PyMOL Molecular Graphics System, Version 2.0. Schrödinger LLC.</p>
Data from: Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling.
<p><strong>Background</strong>: Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large–scale data sets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in natural language processing (NLP) and protein language modelling (PLM) have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. <strong>Methodology</strong>: We trained a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluated its performance on the peptide presentation prediction task. <strong>Data</strong>: The data and models used in AEGIS are contained in the uploaded tar files. <strong>Results</strong>: The transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance (see preprint). We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by the non-obese diabetic (NOD) mice enabled the in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications.</p>
Modeling flexible protein structure with AlphaFold2 and cross-linking mass spectrometry
<p>Ensembles of models predicted by AlphaFold for the proteins C3 (Complement component 3), luciferase and QBP (glutamine-binding periplasmic protein). Models interpolated between two conformations of C3, and luciferase are also included. This dataset is cited in the following paper: https://www.biorxiv.org/content/10.1101/2023.09.11.557128v1</p>
Enhanced mTORC1 signaling and protein synthesis in pathologic alpha-synuclein cellular and animal models of Parkinson’s disease
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Rapid and specific degradation of endogenous proteins in mouse models using auxin-inducible degrons
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A selective small-molecule agonist of G protein-gated inwardly-rectifying potassium channels reduces epileptiform activity in a mouse model of tumor associated epilepsy - Part 2: WT Non-Tumor MEA Data
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A selective small-molecule agonist of G protein-gated inwardly-rectifying potassium channels reduces epileptiform activity in a mouse model of tumor associated epilepsy - Part 3: Thy1-GCaMP Tumor Imaging EXCERPT
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Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
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Supplementary tables S5, S7, S9, S10, original protein models fasta files used for alignments, aligned and manually curated protein modes files used for phylogenies (PHYLIP format), and phylogenetic trees of plant cell wall decomposition gene families from 44 basidiomycete genomes (.tre files)
<p><span><span><span><span><span><span><span><span><span><span><span>Litter-decomposing Agaricales play key role in terrestrial carbon cycling, but little is known about their decomposition mechanisms. We assembled datasets of 42 gene families involved in plant-cell-wall decomposition from seven newly sequenced litter decomposers and 35 other Agaricomycotina members, mostly white-rot and brown-rot species. Using sequence similarity and phylogenetics, we split the families into phylogroups and compared their gene composition across nutritional strategies. Subsequently, we used Raman spectroscopy to examine the ability of litter decomposers, white-rot fungi, and brown-rot fungi to decompose crystalline cellulose. Both litter decomposers and white-rot fungi share the enzymatic cellulose decomposition, whereas brown-rot fungi possess a distinct mechanism that disrupts cellulose crystallinity. However, litter decomposers and white-rot fungi differ with respect to hemicellulose and lignin degradation phylogroups, suggesting adaptation of the former group to the litter environment. Litter decomposers show high phylogroup diversity, which is indicative of high functional versatility within the group, whereas a set of white-rot species shows adaptation to bulk-wood decomposition. In both groups, we detected species that have unique characteristics associated with hitherto unknown adaptations to diverse wood and litter substrates. Our results suggest that the terms white-rot fungi and litter decomposers mask a much larger functional diversity.</span></span></span></span></span></span></span></span></span></span></span></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.