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324 results for “protein complexes”
Structures of S-protein in complex with ligands deposited in the PDB between the 1st January 2021 and the 13th May 2021
<p>All 174 structures of SARS-CoV-2 S-protein in complex with a ligand released on the PDB between the 1<sup>st</sup> January 2021 and the 13<sup>th</sup> May 2021 were categorised by ligand type: hACE2, antibody Fab fragments, VHH antibody fragments or <em>de novo</em> designed peptide scaffolds. Information concerning the method by which the structures were determined and their resolution were retrieved from the PDB. The categorisation of ligands by S-protein binding site were achieved by visual analysis of all the structures using molecular visualisation software PyMOL, in which no new binding sites were found beyond those already categorised for the structures released on the PDB until the 1<sup>st</sup> January 2021 (10.5281/zenodo.5503855).</p> <p>The Pure project is funded by the European Union’s Horizon 2020 program under grant agreement No. 899732.</p> <p> </p>
List of the structures of S-protein in complex with ligands deposited in the Protein Data Bank until the 1st January 2021.
<p>All 131 structures of SARS-CoV-2 S-protein in complex with a ligand released on the PDB until the 1<sup>st</sup> January 2021 were categorised by ligand type: hACE2, antibody Fab fragments, VHH antibody fragments or <em>de novo</em> designed peptide scaffolds. The ligands’ amino acid sequences, the method by which the structures were determined and their resolution were retrieved from the PDB. Information regarding the ligands' production method, dissociation constants (K<sub>D</sub>), S-protein segment against which the K<sub>D</sub> were measured and the determination methods were retrieved from the respective references. The categorisation of ligands by S-protein binding site and listing of S-protein conformation in each structure were achieved by visual analysis of all the structures using molecular visualisation software PyMOL.</p>
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
SeMRA Protein Complex Mappings Database
<p>Analyze the landscape of protein complex nomenclature resources, species-agnostic. See instructions for reproduction and usage in the attached README.md.</p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
Paramecium Polycomb Repressive Complex 2 physically interacts with the small RNA binding PIWI protein to repress transposable elements
<p>Polycomb Repressive Complex 2 (PRC2) maintains transcriptionally silent genes in a repressed state via deposition of histone H3 K27 trimethyl (me3) marks. PRC2 has also been implicated in silencing transposable elements (TEs), yet how PRC2 is targeted to TEs remains unclear. To address this question, we identified proteins that physically interact with the <em>Paramecium</em> Enhancer-of-zeste Ezl1 enzyme, which catalyzes H3K9me3 and H3K27me3 deposition at TEs. We show that the <em>Paramecium</em> PRC2 core complex comprises four subunits, each required <em>in vivo</em> for catalytic activity. We also identify PRC2 cofactors, including the RNA interference (RNAi) effector Ptiwi09, which are necessary to target H3K9me3 and H3K27me3 to TEs. We find that the physical interaction between PRC2 and the RNAi pathway is mediated by a RING finger protein and that small RNA recruitment of PRC2 to TEs is analogous to the small RNA recruitment of H3K9 methylation SU(VAR)3-9 enzymes.</p>
PhasAGE Training School 1 - Structure and protein interactions of repeated and low complexity regions - LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Protein-RNA complex simulation
<p>Simulation trajectory of TSEN/pre-tRNAArgTCT generated with GROMACS. </p> <p> </p> <p>A hybrid model of truncated TSEN/pre-tRNAArgTCT with the additional TSEN2 domain from AlphaFold Jumper et al, 2021</p> <p>and in silico modeled intron bases 37 to 43 was subjected to all-atom molecular dynamics simulations</p> <p>for assessment of flexibility. Simulation trajectory can be visualised with PyMOL or VMD.</p> <p> </p> <p>The protein was described by the AMBER14ff (Maier, et al 2015), and the RNA with the OL3 force field (Zgarbova et al, 2011)</p> <p>and the TIP3P water model was employed (Jorgensen et al, 1983).</p>
Protein elution profiles accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p>Key to files</p> <p><strong>Experiment_Order.csv</strong></p> <ul> <li>Description: Meta details of each experiment.</li> <li>Format: experiment_name,ExperimentID_order,tissue,experiment_type,spec,ExperimentID</li> </ul> <p><strong>Fraction_Details.csv</strong></p> <ul> <li>Description: Meta details of each fraction</li> <li>Format:FractionID,frac_order,ExperimentID</li> </ul> <p><strong>plant_virNOG_orthology.csv.gz</strong></p> <ul> <li>Description: Conversion between orthogroup and protein IDs.</li> <li>Format:ID,ProteinID,spec</li> </ul> <p><strong>orthogroup_annotation.csv.gz</strong></p> <ul> <li>Description: Orthogroup annotations</li> <li>Format:ID,Annotation,arath_genenames,arath_Entries,arath_Entry_names,arath_Protein_names,disruptions,tair_disruptions,lloyd2012_LOFs,arath_functions,arath_misc,pathway,unipathway,BioCyc,Reactome,BRENDA,kegg_pws,ec,arath_masses,arath_protein_names,arath_GO,devstages,tissues,tair,araport,orysj_genenames,orysj_Entries,orysj_Entry_names,orysj_Protein_names,orysj_disruptions,orysj_functions,orysj_misc</li> </ul> <p><strong>panplant_tidy_elution_virNOG.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong> for all experiments. Includes parts per million in each fraction. </li> <li>Format: ExperimentID,FractionID,ID,Total_PeptideCount,spec,ExperimentID_order,FractionID_order,abundance_ppm</li> </ul> <p><strong>panplant_tidy_elution_protcount.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> for all experiments. </li> <li>Format: ExperimentID,FractionID,ProteinID,ProteinCount,spec,ExperimentID_order,FractionID_order</li> </ul> <p><strong>panplant_wide_elution_virNOG.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_virNOG_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong>, includes annotation columns.</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong>, including columns with annotations</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>[experiment_name].virNOG.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong> in one experiment</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <ul> </ul> <p><strong>[experiment_name].protcount.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) counts for all observed <strong>proteins </strong>in one experiment</li> <li>Format: ProteinID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.virNOG.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups </strong>from a particular species. Only present for species with more than one experiment. </li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.protcount.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> from a particular species. Only present for species with more than one experiment. </li> <li>Format: ProteinID,[Fractions]</li> </ul> <p> </p> <ul> </ul> <p>Species codes</p> <p>|Code | Species | Common name | Use |<br> |---|---|---|<br> | arath | Arabidopsis Thaliana | Arabidopsis | <br> | braol | Brassica oleracea | Broccoli |<br> | cansa | Cannabis sativa | hemp | <br> | cerri | Ceratopteris richardii | C-fern | <br> | chlre | Chlamydomonas reinhardtii | Chlamydomonas |<br> | chqui | Chenopodium quinoa | Quinoa | <br> | orysj | Oryza sativa var. japonica | Rice |<br> | selml | Selaginella moellendorffii | Selaginella | <br> | sollc | Solanum lycopersicum | Tomato | <br> | wheat | Triticum Aestivum | Wheat | <br> | soybn | Glycine max | Soybean | <br> | cocnu | Cocos nucifera | Coconut | </p> <p>| maize | MAIZE | maize | </p> <p> </p>
Heatmaps of orthology and protein domain preservation in RNA Processing complexes throughout the fungal kingdom
<p>An analysis of the presence/absence of orthologues for Fungal RNA Processing protein complexes, and the presence/absence of the known PFAM protein domains within each protein within these complexes in the organism's proteome. </p> <p>Each image represents one RNA Processing protein complex.</p> <p>Orthology (far left panel in each image) is relative to Yeast, and taken from a query against the EnsEMBL orthology database (black = no orthologue; red = orthologue). <br> <br> Each orthologue was then queried for its PFAM domains, and the non-redundant set of PFAM domains representing each set of orthologous proteins, spanning all species, was then scanned against the complete proteome of each species. The resulting heatmap indicates the presence or absence of that PFAM domain anywhere in the proteome of that species. (black = absent; red = 1 copy; grey->blue = more than one copy)</p>
Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile
<p>Supporting data for AFProfile</p> <p>casp15.tar.zst - predicted structures and MSAs for the CASP15 set<br>native_afm_2_6_bench.tar.zst - native cif files for all complexes with ranking confidence <0.75 in the AFM 2-6 chains benchmark ( https://doi.org/10.1093/bioinformatics/btad424)<br>pred_top_models_afm_2_6_bench.tar.zst - predicted top ranked models and scores for all 100 samples.<br>afm_opt_metrics.csv - the best models, confidences and MMscores for the AFProfile run on the AFM 2-6 chains benchmark (n=427 structures)<br>msa_shapes.csv - the shape of the MSA as input to AFM for each structure<br>directed.tar.zst - contains all models for the AFProfile run on the AFM 2-6 chains benchmark (n=42700 samples)</p> <p>The directories are compressed with zstd: https://github.com/facebook/zstd<br>Uncompress:<br>tar --use-compress-program full/path/to/zstd -xvf file.tar.zst</p>
Simulation of Receptor Binding Domain of SARS-CoV-2 spike protein (WT and variants) in complex with neutralizing antibodies.
<p>This repository contains the molecular dynamics trajectories of the SARS-CoV-2 Spike RBD bound to BD23 and B38 monoclonal antibodies. The simulations for the RBD only systems are also provided. The trajectories are available for the WT spike protein as well as for four different variants (alpha, beta, kappa and delta). The simulations of the RBD only system are propagated for 300 ns and for the RBD-Antibody complex for 500 ns. The trajectories are saved at 100 ps interval. The Steered MD simulation trajectories (WT_RBD_B38_SMD_1.dcd etc.) and collective variables files are also included (WT_RBD_B38_SMD_1.colvars.traj etc.). There are 5 SMD trajectories for each RBD antibody pair. The details of the simulation can be obtained from the preprint: https://doi.org/10.1101/2021.08.13.456317</p>
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment
<p>The file contains two benchmark sets: Heterodimer-AF2 and Docking benchmark 5.5-AF2 test. Each set includes (1) doecy folder, (2) native folder, and (3) label_info.csv. </p>
Benchmark Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three benchmark datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment: MAF2 set
<p>Multimer AF2 set. For more information, please read our paper on biorxiv:</p> <p><a href="https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2">https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2</a> </p>
Full Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
Molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants
<p>This data set accompanies the publication by S. Conti, E. Lau, and V. Ovchinnikov entitled "On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations", to be published in the MDPI journal Antibodies. It contains molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants, as described in the paper. The format of the trajectory files is CHARMM-compatible dcd. The files can be visualized with the program Visual Molecular Dynamics (VMD) (see paper by Humphrey et al. 1996, J. Molec. Graphics). The accompanying file "view" is a tcl-based script for VMD that can be executed in the Linux environment using: "vmd -e view", which will display the trajectory of the mutant specified by editing the first noncomment line of the script.<br> </p>
PSnpBind: A database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow
<p>A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at <a href="https://psnpbind.org">https://psnpbind.org</a>.<strong> </strong>The source code of the tools used in constructing PSnpBind is available on <a href="https://github.com/ammar257ammar/PSnpBind-Build">GitHub</a>.</p>
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