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261 results for “protein prediction”
Data - AlphaFold2 Predicts Alternative Conformation Populations in Green Fluorescent Protein Variants
<p><strong>MSAs.zip </strong>Multiple sequences alignments generated by AlphaFold2 structure prediction of 7 engineered GFPs.</p> <p><strong>AF2_models_column_masking.zip </strong>AlphaFold2 models of the alternative conformations of 7 engineered GFPs.</p> <p><strong>MD_trajectories_PyMOL.zip</strong> Molecular dynamics trajectories (PyMOL sessions) of the alternative conformations of 7 engineered GFPs.</p> <p><strong>MD_analysis.zip </strong>Root mean square deviation and per-residue root mean square fluctuations along molecular dynamics simulations of 7 engineered GFPs.</p> <p><strong>rmsd_values.zip</strong> Root mean square deviation relative to crystallographic GFP structure for AlphaFold2 models and molecular dynamics frames (global and central alpha-helix)</p>
Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three<br> state together (Q3) for the system is shown in Figure 3.</p>
Figure 2. PAM250 matrix for the encoded sequence-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The PAM matrix (Dayhoff et al., 1978) describes the probability that original amino acid<br> will be replaced by another amino acid over a defined evolutionary interval. The unit of<br> evolutionary divergence is defined as the interval in which 1% of the amino acids have been<br> changed between two sequences. The work uses PAM250, which assumes the occurrence of 250-<br> point mutations per 100 amino acids.<br> So, for the given the protein sequence GIVEQCCASVCSLYQLENYCN, A will be replaced<br> by 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 as shown in Figure 2.</p>
Figure 1: Snapshot of the CB396 dataset-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning AlgorithmSecondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The dataset used for this work is CB396. This dataset contains 396 non-redundant sequences<br> derived from the 3Dee database created by Cuff and Barton (Cuff & Barton, 1999). It contains 396<br> proteins with their respective secondary structure as shown in Figure 1.</p>
Data to accompany the paper "Improved fragment-based protein structure prediction by redesign of search heuristics"
<p>This repository contains the older and newer input fragment sets and other data used for the analyses in our paper. The filenames for each tarball contain the PDB identifier of each protein along with a chain ID if applicable, followed by 'old' or 'new' for old and new fragments, respectively. Each tarball contains: a .fasta file of the input sequence, a matching PDB structure file, the relevant PSIPRED secondary structure prediction file, and the 9mer and 3mer fragment files. <br> <br> An additional tarball, ScoreRMSDplots_3protocols.tgz, contains extended versions of Figure 3 which show score and RMSD distributions clearly. Additionally, the same data is shown for equivalent experiments using the older fragment set.</p>
DBv5 features and codified complexes used in BIPSPI: a method for the prediction of partner-specific protein–protein interfaces
<p>Features and protein complexes codified for DBv5 BIPSPI paper</p> <p>(BIPSPI: a method for the prediction of partner-specific protein–protein interfaces</p> <p><a>Ruben Sanchez-Garcia</a> <a>C O S Sorzano</a> <a>J M Carazo</a> <a>Joan Segura</a></p> <p><em>Bioinformatics</em>, bty647, <a href="https://doi.org/10.1093/bioinformatics/bty647">https://doi.org/10.1093/bioinformatics/bty647</a>)</p>
Extended data for the paper "Reliable generation of native-like decoys limits predictive ability in fragment-based protein structure prediction"
<p>Extended data for the paper:<br> Reliable generation of native-like decoys limits predictive ability in fragment-based protein structure prediction</p> <p>Authors:<br> Shaun M Kandathil, Mario Garza-Fabre, Simon C Lovell and Julia Handl</p> <p>--------------------------------</p> <p>Contents of the zip file:</p> <p> </p> <p>Directory 'ECDFplots':<br> ----------------------<br> Data corresponding to Figure 3 for all targets, for the bilevel and ILS protocols. Data are available following stages 3 and 4 of the low-resolution protocol.</p> <p>Directory 'ScoreRMSDplots_3archivers':<br> --------------------------------------<br> Data corresponding to Figures 6 and 9 for all targets. Data corresponding to decoys obtained after low-resolution stages 3 and 4 can be found in subdirectories 'Stage3' and 'Stage4', respectively.<br> </p>
DPCstruct Classification of AlphaFold2-Predicted Protein Structures
<p>This dataset contains DPCstruct domain classifications for protein structures predicted by AlphaFold2, as presented in the paper "Unsupervised Domain Classification of AlphaFold2-Predicted Protein Structures."</p> <p>DPCstruct was applied to a non-redundant set of the AlphaFold Database v4.0, known as Foldseek Clusters, which includes approximately 15 million representative proteins, as described in the work by <a href="https://doi.org/10.1038/s41586-023-06510-w">Barrio-Hernandez et al.</a></p> <p>This repository provides the results of our classification, along with all the data related to the analyses presented in our study. DPCstruct algorithm can be found at <a href="https://github.com/RitAreaSciencePark/DPCstruct">https://github.com/RitAreaSciencePark/DPCstruct</a> together with examples on how to use it.</p> <p><strong>FILES DESCRIPTION:</strong></p> <ul> <li><strong>dpcstruct_classification.tsv: </strong>List of domains identified by DPCstruct and their corresponding metacluster. Columns: Metacluster ID, Protein Uniprot ID, domain start, domain end.</li> <li><strong>mcs_reps.fasta:</strong> For each metacluster, two representative domains were selected: one representing the center of the cluster and the other being the domain with the highest pLDDT score. If these are the same, only one domain is included as the representative. This file contains the list of representative domains and their sequences in FASTA format.</li> <li><strong><span>mcs_reps_pdbs.zip: </span></strong>Contains a PDB file for each representative domain. The filename is structured as 'proteinID_metacluster.pdb'.</li> <li><strong>mcs_properties.tsv:</strong> Set of properties per metacluster, including: <ul> <li><strong>mcID:</strong> Metacluster ID.</li> <li><strong>size:</strong> Number of domains.</li> <li><strong>len_aa:</strong> Average length of domains (number of amino acids).</li> <li><strong>len_std:</strong> Standard deviation of domain lengths.</li> <li><strong>len_ratio:</strong> Ratio of len_std to len_aa.</li> <li><strong>plddt:</strong> Average predicted LDDT as reported by AlphaFold2.</li> <li><strong>disorder:</strong> Average intrinsic disorder score calculated with AIUPred.</li> <li><strong>alntmscore:</strong> Pairwise alignment TM-score between domains, averaged over all pairs.</li> <li><strong>tmscore:</strong> Pairwise alignment TM-score between domains, averaged over all pairs, using the maximum between TM-score normalized by query or target.</li> <li><strong>lddt:</strong> Pairwise LDDT score, averaged over all pairs.</li> <li><strong>prob:</strong> Pairwise probability of homology according to SCOPe, as reported by Foldseek.</li> <li><strong>pident:</strong> Pairwise percentage identity, averaged over all pairs.</li> </ul> </li> <li><span><strong>annotated_[cath|scop]_qc[x]_t[x]_l[x].tsv:</strong> </span>For each fold in [CATH|SCOP], we provide the best matching DPCstruct domain, if available, along with the structural alignment information as reported by Foldseek. A fold is considered annotated if its alignment values meet or exceed the following thresholds: <ul> <li>qc: query coverage.</li> <li>t: template modelling score of the alignment.</li> <li>l: lddt score of the alignment.</li> </ul> </li> <li><strong>dpcstruct_consistency.tsv:</strong> Consistency of DPCstruct metaclusters with respect to Pfam 36.0 labels. Note that we consider a Pfam label to overlap with a DPCstruct domain even if it shares just one amino acid, which is why some metaclusters have many labels. In such cases, we only display 5 representative labels.</li> <li><strong>pfam_consistency.tsv:</strong> Consistency of Pfam Clans with respecto to DPCstruct labels.</li> </ul> <p><strong>Note:</strong> All 'tsv' files contain a header as the first row.</p> <p>If there is any doubt regarding the data or there is something missing please contact us: </p> <p>federico.barone@areasciencepark.it</p>
Alphafold2 and AlphaFold-Multimer Predicted Interactions of Soybean Proteins with Macrophomina phaseolina Effectors reveals putative protease inhibitors and SUSS effectors.
<p> </p> <ul> <li> <p><strong>Kunitz Monomer Prediction</strong>:</p> <ul> <li><strong>Data</strong>: Analysis of soybean Kunitz proteins.</li> <li><strong>Details</strong>: Detected on the apoplast at 3 days post-infection with <em>Macrophomina phaseolina</em>.</li> <li><strong>File</strong>: <code>KUNITZ_monomers_outputdir.zip</code></li> </ul> </li> <li> <p><strong>Uncharacterized M. phaseolina Protein Monomer Prediction</strong>:</p> <ul> <li><strong>Data</strong>: Predictions for uncharacterized proteins.</li> <li><strong>Details</strong>: Detected on the apoplast at 3 days post-infection.</li> <li><strong>File</strong>: <code>uncharacterised_proteins_SUSS_effectoroutputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Positive Validation Set</strong>:</p> <ul> <li><strong>Data</strong>: Experimental verification of protein-inhibitor pairs.</li> <li><strong>Details</strong>: Pairs include experimentally verified interactions, specifically proteins and inhibitors, but lack resolved crystal structures.</li> <li><strong>File</strong>: <code>existing_non_existinpairs_Validation_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Soybean Serine Protease-Kunitz Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions between soybean serine proteases and Kunitz proteins.</li> <li><strong>Details</strong>: Analyzed in the apoplastic space at 3 days post-infection.</li> <li><strong>File</strong>: <code>glycine max_Serine protease_Vs_Gmaxkunitz_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Cysteine Protease without Pro-domain-MoErs-like effector Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions involving cysteine proteases.</li> <li><strong>Details</strong>: Rice RD21 and soybean cysteine proteases with pro-domains removed interacting with <em>MoErs1</em> and <em>MoErs1</em>-like M.phaseolina effectors.</li> <li><strong>File</strong>: <code>AF2-Multimer_RD21&GmaxCproteases_MoERS1_screening_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Fungal Serine Protease-Kunitz Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions between <em>Macrophomina phaseolina</em> serine proteases and soybean Kunitz proteins.</li> <li><strong>Details</strong>: Evaluated in the apoplastic space at 3 days post-infection.</li> <li><strong>File</strong>: <code>fungalSerineprotease_Vs_Gmax_kunitzoutputdir.zip</code></li> </ul> </li> <li> <p><strong>Negative Validation Set</strong>:</p> <ul> <li><strong>Data</strong>: Known non-interacting pairs.</li> <li><strong>Details</strong>: Non-interacting pairs of serine proteases-chitinases that are not resolved as crystal structures</li> <li><strong>File</strong>: <code>Gmax_Serineprotease_Vs_Gmaxchitinases_Validation_outputdir.zip</code></li> </ul> </li> </ul>
Protein language model embeddings and predictions of the human proteome
<p>Residue and sequence embeddings of the human proteome (SwissProt for organism Human, downloaded on 2021.06.09) computed using bio_embeddings (bioembeddings.com) using the ProtT5 embedder at full precision (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3).</p> <p>Additionally:</p> <p>- Sequence-level predictions of subcellular localization in 10 classes using LA (https://www.biorxiv.org/content/10.1101/2021.04.25.441334v1)</p> <p>- Residue-level three state secondary structure prediction (alpha, sheet or other) using models reported in the ProtTrans paper (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3)</p> <p> </p> <p>Files included:</p> <p>- human.fasta --> FASTA-formatted sequences of human from SwissProt</p> <p>- DSSP3_human_ProtT5Sec.fasta --> Secondary structure predictions in three states for each residue of each protein in human.fasta. "H" stands for Helix; "E" stands for Sheet; "C" stands for Other.</p> <p>- subcell_human_LA_ProtT5.csv --> Subcellular location (10 states) and memrane-boundness (2 states) for each protein in human.fasta</p> <p>- embeddings_file.h5 --> per-residue embeddings of sequences in human.fasta. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length Lx1024, with L being the length of the protein sequence. Datasets are indexed using integers. The original sequence identifier (from the FASTA header) can be accessed through the "original_id" attribute. See https://docs.bioembeddings.com/v0.2.0/notebooks/open_embedding_file.html for information on how to open the file</p> <p>- reduced_embeddings_file.h5 --> per-sequence embeddings of sequences in human.fasta (obtained by mean-pooling the residue-embeddings along the length dimension of the protein sequence). Each dataset in the .h5 file represents a protein sequence and contains a vector of size 1024 (meaning, each sequence has the same dimension).</p>
Data Sets and Results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins"
<p>Data sets and results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins"<br> <br> The file "dna_binding_protein_sequences.zip" has the training and testing sets from the paper:<br> <br> RLL - "random_<train/test>_full_1000.csv"<br> RSL - "random_<train/test>_40.csv"<br> RS&LL - "random_<train/test>_40_1000.csv"<br> RLL where included positive examples have verified DNA binding activity - "random_<train/test>_hq_1000.csv"<br> The 10 RS&LL data sets - "random_<train/test>_40_1000.csv" + "random_<train/test>_40_1000_cv_<0-8>.csv"</p> <p>The results files are named similarly. See "see_results.ipynb" in the <a href="https://doi.org/10.5281/zenodo.5153683">codebase</a> that supplement these data sets<br> The species data sets are derived from "uniprot_data_bac.tab" and "uniprot_data_not_bac.tab." See <a href="https://doi.org/10.5281/zenodo.5153683">code</a>. <br> <br> The ESM embeddings used by the XGBoost model are in "dna_binding_protein_esm.zip"</p>
Known and Predicted GPI-anchored proteins in Trypanosoma brucei
<p>This table is a list of proteins that are either known or predicted to be a GPI-anchored proteins in <em>Trypanosoma brucei</em>. </p>
Protein structure model predictions for secreted fungal proteins
<p><strong>Dataset A - Alphafold2 prediction output data for 753 secreted proteins of <em>Rhizophagus irregularis </em>DAOM197198</strong>. Gene IDs are taken from the annotation by Yildirir et al. 2021, <a href="https://doi.org/10.1111/nph.17842">doi.org/10.1111/nph.17842</a></p> <p><strong>Dataset B - Alphafold2 prediction output data for 10 fungal effectors.</strong><strong> </strong>These are nine effectors from <em>Fusarium oxysporum</em> f. sp.<em> lycopersici</em> and RiSLM from <em>Rhizophagus irregularis</em> as well as their amino acid sequences. Signal peptides and sequences preceding a predicted Kex2 processing site were removed.</p> <p><strong>Dataset C - Alphafold2 prediction output data for 454 matches of a MycFOLD-HMM search</strong> across the Mycocosm genome database (<a href="https://mycocosm.jgi.doe.gov/mycocosm/home">https://mycocosm.jgi.doe.gov/mycocosm/home</a>) and 36 Glomeromycotina fungal genomes.</p>
Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"
<p><strong>Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process files (in "PDBs.zip") and produce the dataset ("Dataset_v02.csv")</li> <li>PDBs.zip --- PDB files include filtered structures processed by three programs</li> <li>Dataset_v02.csv --- final filtered version of dataset produced in R language (by "Main_v02.R"). </li> <li>Dataset key.txt --- key to column names in Dataset_v02.csv </li> </ul> <p>The article featuring this dataset is published in:</p> <p>Journal: Journal of Chemical Information and Modeling<br>Title: "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"<br>Author(s): Hameduh, Tareq; Mokry, Michal ; Miller, Andrew ; Heger, Zbynek; Haddad, Yazan</p> <p><a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpubs.acs.org%2Fdoi%2F10.1021%2Facs.jcim.3c00134&data=05%7C01%7C%7C4876a5c33e5f4bd8daf008db7e55a28d%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C638242678496577396%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=NE%2F0dYvc4m5%2B8T0YQtqxY21yk1pA9ZpiADqzx3vEJ4Q%3D&reserved=0">https://pubs.acs.org/doi/10.1021/acs.jcim.3c00134</a></p> <p> </p>
Predicted models of S receptor kinase ectodomain (eSRK), S-locus protein 11 (SP11), and eSRK-SP11 complexes
<p>Predicted models of <em>S</em> receptor kinase ectodomain (eSRK), <em>S</em>-locus protein 11 (SP11), and their complexes using ColabFold and curated multiple sequence alignments (MSAs).</p> <p> </p>
Data underlying the article: 3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors
<p>This repository contains the datasets and results supporting the conclusions of the manuscript "<strong>3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors</strong>". </p> <p>Publicly available data is not included in this repository. The source code to generate the results gathered here can be found on GitHub (https://github.com/CDDLeiden/3ddpd). </p>
Improving AlphaFold2-based Protein Tertiary Structure Prediction with MULTICOM in CASP15
<p>Improving AlphaFold2-based Protein Tertiary Structure Prediction with MULTICOM in CASP15</p>
Automated benchmarking of combined protein structure and ligand conformation prediction
<p>The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.</p> <p>This repository contains:</p> <ol> <li> <p>all_validation_clustering_data.tsv - X-ray validation data and MMSeqs cluster identifiers at different sequence identities for over a million small molecule and ion-binding pockets in the PDB. </p> </li> <li> <p>hqr_dataset.tsv - PDB IDs and ligand information for the high quality representative (HQR) dataset described in the manuscript</p> </li> <li> <p>score_files.tar.gz - Full docking results for all detected pockets for the PDBBind time-split test-set, the HQR dataset, and the subsets of AF models created for both datasets. One file per tool benchmarked with the following columns: Tool, Complex, Pocket, Rank, lDDT-PLI, lDDT-LP, BiSyRMSD, Reference_Ligand, Tool-generated Score</p> </li> <li> <p>errors_all_sets.csv - Report of failures running the pipeline with the following columns: Process, Complex/Ligand/Receptor, Problem</p> </li> </ol>
A dataset for predicting protein-protein interactions in humans
Open the record for dataset details and reuse information.
Supporting data for: A close look at protein function prediction evaluation protocols
<p>Data files and predictions associated with the paper: A close look at protein function prediction evaluation protocols.</p>
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