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56 results for “alphafold”
Pro-IL-18 NMR and AlphaFold structure coordinate files
<p>Coordinate files for pro-interleukin(IL)-18 are provided. The coordinates were generated from solution NMR structure determination as well as AlphaFold2 and AlphaFold3 predictions.</p>
AlphaFold 3 predicted NLR resistosome structures
<p><strong>Abstract</strong></p> <p>In our paper "A disease resistance protein triggers oligomerization of its NLR helper into a hexameric resistosome to mediate innate immunity," we used the NbNRC2 hexamer structure to evaluate the capabilities of the newly introduced AlphaFold 3 in predicting activated CC-NLR oligomers. Our analysis highlights AlphaFold 3 effectiveness in confidently modelling the N-terminal alpha1-helices of NbNRC2 and other CC-NLRs, a structurally elusive region critical for NLR function but challenging to resolve through conventional structural methods. This study not only underscores the utility of AlphaFold 3 in enhancing our understanding of plant immune receptors but also extends its application to complex oligomerization processes in innate immunity. Here, we provide the Supplementary data accompanying the paper which includes metadata and predicted structures for 29 NLR immune receptors, providing valuable resources for further research in plant pathogen resistance.</p> <p> </p> <p><strong>Index:</strong></p> <ul> <li>Metadata:</li> <li> <ul> <li>Metadata, sequence, and model statistics of all modelled NLRs: <ul> <li>Data S1.xlsx</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>Predicted structures:</li> <li> <ul> <li>AlphaFold 2 vs 3 vs 3+lipids comparison for NbNRC2: <ul> <li>AF_benchmark.zip</li> </ul> </li> </ul> </li> </ul> <ul> <li> <ul> <li>[AlphaFold 3] NRC2 oliogmoeric type benchmarks (N = 10): <ol> <li>NRC2_tetramers.zip</li> <li>NRC2_pentamers.zip</li> <li>NRC2_hexamers.zip</li> <li>NRC2_heptamers.zip</li> <li>NRC2_octamers.zip</li> </ol> </li> </ul> </li> </ul> <ul> <li> <ul> <li>[AlphaFold 3] Pentamer vs Hexamer comparison for 11 representative NRC proteins: <ul> <li>NRCs.zip</li> </ul> </li> <li>[AlphaFold 3] Pnetamer vs Hexamer comparison for AtZAR1 (PDB:6j5t) and TmSr35 (PDB:7xe0) experimentally validated cryo-EM structure: <ul> <li>Benchmarks.zip</li> </ul> </li> <li>[AlphaFold 3] Pentamer vs Hexamer comparison for a selection of 8 CC, 6 CCG10, and 2 CCR-NLRs: <ul> <li>CC.zip</li> <li>CCG10.zip</li> <li>CCR.zip</li> </ul> </li> </ul> </li> </ul>
Combining AlphaFold with Focused Virtual Library Design in the Development of Novel CCR2 and CCR5 Antagonists
<p>Collection of code and data accompanying the manuscript titled "Combining AlphaFold with Focused Virtual Library Design in the Development of Novel CCR2 and CCR5 Antagonists ". Read the included README.md file for more information.</p>
The comparison of the AlphaFold and SwissModel Repository databases
<p>This dataset supplements the code at <a href="https://github.com/aozalevsky/alphafold2_vs_swissmodel">https://github.com/aozalevsky/alphafold2_vs_swissmodel</a> for the comparison of the AlphaFold2 database (<a href="https://alphafold.ebi.ac.uk/">https://alphafold.ebi.ac.uk</a>) with the SwissModel Repository (<a href="https://swissmodel.expasy.org/repository">https://swissmodel.expasy.org/repository</a>). Results of the analysis were published as part of the AlphaFold community review <a href="https://www.nature.com/articles/s41594-022-00849-w">https://www.nature.com/articles/s41594-022-00849-w</a> </p>
Application of AlphaFold on metamorphic proteins - dataset
<p>The dataset comprises a set of five structures of metamorphic proteins used for the study.</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>
Alphafold decoys on CASP15 target Fasta Files
<p>This contains the decoys along with the results produced by alphafold2 when run on a subset of CASP15 Fasta Files</p>
Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"
<p>Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".</p><p>Includes:</p><ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>
Data from: Harnessing AlphaFold to reveal hERG channel conformational state secrets
Open the record for dataset details and reuse information.
PfamSDB, The structural database of Pfam seeds made by cutting the Full-Length Alphafold predicted structures
<p>PfamSDB.tar contains the gzipped pdb file of Pfam seeds.</p><p>PfamSDB_cutFS.tar.gz contains the Foldseek database of Pfam seeds made by cutting the database of Full-length proteins.</p>
Data and structures for "How accurately can we predict binding poses with AlphaFold models?
<p>Contains structures generated by AlphaFold, models from GPCRdb, and structures of proteins from PDB. </p> <p>Additionally computed rmsds for pockets, backbone, and poses, and scripts to create figures.</p> <p> </p> <p> </p>
Importance of updated benchmark sets for statistically correct AlphaFold applications
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Domain organization of lentiviral and betaretroviral surface envelope glycoproteins modeled with AlphaFold
The surface envelope glycoproteins of non-primate lentiviruses and betaretroviruses share sequence similarity with the inner proximal domain b-sandwich of the human immunodeficiency virus type 1 (HIV-1) gp120 glycoprotein that faces the transmembrane glycoprotein as well as patterns of cysteine and glycosylation site distribution that points to a similar two-domain organization in at least some lentiviruses. Here, high reliability models of the surface glycoproteins obtained with the AlphaFold algorithm are presented for the gp135 glycoprotein of the small ruminant caprine arthritis-encephalitis (CAEV) and visna lentiviruses and the betaretroviruses jaagsiekte sheep retrovirus (JSRV), mouse mammary tumor virus (MMTV) and consensus human endogenous retrovirus type K (HERV-K). The models confirm and extend the inner domain structural conservation in these viruses and identify two outer domains with a putative receptor binding site in the CAEV and visna virus gp135. The location of that site is consistent with patterns of sequence conservation and glycosylation site distribution in gp135. In contrast, a single domain is modeled for the JSRV, MMTV and HERV-K betaretrovirus envelope proteins that is highly conserved structurally in the proximal region and structurally diverse in apical regions likely to interact with cell receptors. The models presented here identify sites in small ruminant lentivirus and betaretrovirus envelope glycoproteins likely to be critical for virus entry and virus neutralization by antibodies and will facilitate their functional and structural characterization.
human VPS13C structure predicted by AlphaFold
<p>Full-length human VPS13C isoform 1 comprises 3753 amino acids (a.a.). is predicted with AlphaFold2. Such structure is represented by a 29.3 nm long rod whose backbone is a narrow twisted β-sheet running along its entire length. The β-sheet forms the floor of a hydrophobic groove that extends throughout the rod and thus could mediate the sliding of lipids from one end to the other end of the protein.</p>
AlphaFold models of protein complexes
<p>AlphaFold models of protein complexes</p>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 1 (1-50).</p> <div> <p>Check related biorxiv preprint: AlphaFold2 knows some protein folding principles; DOI: https://doi.org/10.1101/2024.08.25.609581.</p> <p> </p> </div>
Domain organization of lentiviral and betaretroviral surface envelope glycoproteins modeled with AlphaFold
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Dataset of AlphaFold's internal representations of 4,581 proteins relevant for drug discovery
<p>This dataset contains the outputs of the AlphaFold model for 4,581 proteins that are relevant targets in drug discovery.</p> <p>More information on the dataset can be found at the following repository: </p> <p><strong>Dataset structure:</strong></p> <p>↓ <strong>data/</strong>* -> main data directory</p> <blockquote> <p>↓ <strong>data/PID/</strong>* -> data of a single protein of length <strong>L</strong></p> <blockquote> <table> <tbody><tr> <th>Filename</th> <th>Description</th> <th>Tensor shape</th> <th>Lightweight</th> </tr> </tbody><tbody> <tr> <td><strong>single.npy</strong></td> <td>( s i ) evoformer single representation</td> <td><em>[<strong>L</strong> x 384]</em></td> <td>✔️</td> </tr> <tr> <td><strong>structure.npy</strong></td> <td>( a i ) output of the last layer of structure module</td> <td><em>[<strong>L</strong> x 384]</em></td> <td>✔️</td> </tr> <tr> <td><span><em><strong>msa.npy</strong>***</em></span></td> <td>( m s i ) processed MSA representation</td> <td><em>[<strong>N</strong> x <strong>L</strong> x 256]</em></td> <td> </td> </tr> <tr> <td><em><strong>pair.npy</strong>***</em></td> <td>( z i j ) evoformer pair representation</td> <td><em>[<strong>L</strong> x <strong>L</strong> x 128]</em></td> <td> </td> </tr> <tr> <td><strong>PID.pdb</strong></td> <td>3D protein structure prediction</td> <td> </td> <td>✔️</td> </tr> <tr> <td><strong>PID_unrelaxed.pdb</strong></td> <td>3D protein structure prediction w/o relaxation step (D)</td> <td> </td> <td>✔️</td> </tr> <tr> <td><strong>confidence.npy</strong>*</td> <td>confidence in structure prediction (0-100)</td> <td><em><a title="Béquignon OJM, Bongers BJ, Jespers W, IJzerman AP, van de Water B, van Westen GJP. Papyrus - A large scale curated dataset aimed at bioactivity predictions. ChemRxiv. Cambridge: Cambridge Open Engage; 2021; This content is a preprint and has not been peer-reviewed." href="https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0" rel="nofollow">1</a></em></td> <td>✔️</td> </tr> <tr> <td><strong>plldt.npy</strong>*</td> <td>confidence in structure prediction per residue</td> <td><em>[<strong>L</strong>]</em></td> <td>✔️</td> </tr> <tr> <td><strong>PID.fasta</strong></td> <td>protein amino acid sequence and metadata</td> <td> </td> <td>✔️</td> </tr> <tr> <td><strong>timings.json</strong></td> <td>Processing log</td> <td> </td> <td>✔️</td> </tr> </tbody> </table> </blockquote> </blockquote> <blockquote> <p>↓ <strong>data/PID2/</strong>* -> data of protein #2</p> <blockquote> <p><strong>...</strong></p> </blockquote> </blockquote> <p>*<em>Note: <strong>L</strong>: sequence length, <strong>N</strong>: number of aligned sequences via MSA.</em></p>
Raw data molecular dynamics and alphafold (Lemaire et al)
<p>Raw data molecular dynamics and alphafold for manuscript NCOMMS-24-29370</p>
AlphaFold-predicted structures of IL-6 dimers
<p>1. Pre-processed files</p> <p>2. Processed files:</p> <ul> <li>CIF files from AF3 were converted to PDB format</li> <li>only C_alpha atoms were kept</li> <li> polyG linkers were deleted and chain IDs for the second part were renamed to "B"</li> <li>chain "A" was aligned to reference structure using the non-swapped amino acids</li> <li> the models were classified into "non-swapped", "swapped" and "other"</li> </ul> <table> <tbody> <tr> <td>#</td> <td>Approach</td> <td>Weights name</td> <td>Templates</td> <td>Dropout</td> <td>N_models (different weights)</td> <td>N_runs (different seeds)</td> </tr> <tr> <td>1</td> <td>AlphaFold 2.3.2 as a monomer with 50×Gly linker</td> <td>monomer_ptm</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>2</td> <td> </td> <td>monomer_ptm</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>3</td> <td>ColabFold 1.5.5 as a monomer with 50×Gly linker</td> <td>alphafold2_ptm</td> <td>Y</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>4</td> <td> </td> <td>alphafold2_ptm</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>5</td> <td> </td> <td>alphafold2_ptm</td> <td>N</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>6</td> <td> </td> <td>alphafold2_ptm</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>7</td> <td>AlphaFold 2.3.2 as a dimer</td> <td>multimer</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>8</td> <td> </td> <td>multimer</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>9</td> <td>ColabFold 1.5.5 as a dimer</td> <td>alphafold2_multimer_v3</td> <td>Y</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>10</td> <td> </td> <td>alphafold2_multimer_v3</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>11</td> <td> </td> <td>alphafold2_multimer_v3</td> <td>N</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>12</td> <td> </td> <td>alphafold2_multimer_v3</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>13</td> <td>AlphaFold3 as a monomer with 50×Gly linker</td> <td>Default (accessed on 24.06.2024)</td> <td> </td> <td> </td> <td>5</td> <td>20</td> </tr> <tr> <td>14</td> <td>AlphaFold3 as a dimer</td> <td>Default (accessed on 24.06.2024)</td> <td> </td> <td> </td> <td>5</td> <td>20</td> </tr> <tr> <td>15</td> <td>SPEACH_AF</td> <td>alphafold2_ptm</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> </tbody> </table>
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