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14 results for “Enzyme prediction”
Enzymes from the BRENDA and CAZy databases annotated with organism growth temperatures and predicted Topt
<p>This repo is an updated version of repo <strong>Gang Li, & Martin KM Engqvist. (2019). Enzymes from the BRENDA database annotated with organism growth temperatures and predicted <em>T</em><sub>opt</sub> (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2539114. </strong></p> <p>Experimental as well as predicted organism growth temperatures were used to annotate enzymes from the BRENDA database (doi: 10.1093/nar/gky1048, https://www.brenda-enzymes.org) version 2018.2 (July 2018) and CAZy database (http://www.cazy.org/). </p> <p>An updated machine learning model was applied to predict the optimal functional temperature of enzymes from BRENDA and CAZy. </p> <p>There are four files in this repo:</p> <p>1. 'annotated_brenda.tsv' is a tab-seperated file that contains the annotated enzymes from BRENDA. There are 9 columns in the file: index column; "ec", EC number; "uniprot_id", protein id in Uniprot database; "domain", the domain of life (superkingdom), either Archaea, Bacteria, or Eukarya; "organism", species name; "ogt", optimal growth temperature of the organism; "ogt_note", whether the experimental or predicted ogt is used; "topt", the optimal functional temperature of the enzyme; "topt_note", whether the experimental or predicted topt is used.</p> <p>2. 'annotated_cazy.tsv' is a tab-seperated file that contains the annotated enzymes from CAZy. There are 12 columns in the file: index column; "family", CAZy family id; "genbank", genbank id; "Protein Name", the protein name from CAZy database; "ec", EC number; "organism", strain name; "uniprot_id", protein id in Uniprot database; "PDB/3D", structure id in PDB database; "ogt", optimal growth temperature of the organism; "ogt_note", whether the experimental or predicted ogt is used; "topt", the optimal functional temperature of the enzyme; "topt_note", whether the experimental or predicted topt is used.</p> <p>3. 'brenda.sql', which is a SQLite3 database version of 'annotated_brenda.tsv', with an additional column of enzyme sequences.</p> <p>4. 'cazy.sql', which is a SQLite3 database version of 'annotated_cazy.tsv'', with an additional column of enzyme sequences.</p> <p>The SQLite3 databases are for the Tome tool (<a href="https://github.com/EngqvistLab/Tome">https://github.com/EngqvistLab/Tome</a>), version 2.0.</p>
Supplementary data for "Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily"
<p>Supplementary data for "Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily"</p>
Supplementary Dataset for Deep learning based kcat prediction enables improved enzyme constrained model reconstruction
<p>This dataset is the supplementary dataset for the paper "<strong>Deep learning based <em>k</em><sub>cat</sub> prediction enables improved enzyme constrained model reconstruction</strong>". Protein sequence fasta files, deep learning predicted <em>k</em><sub>cat</sub> values, classcial-ecGEMs, DL-ecGEMs and <em>Posterior</em>-mean-ecGEMs for 343 yeast/fungi species are available in this dataset.This repository also contains the computed results for reproducing the figures as model_build_files . The scripts can be found in Github (https://github.com/SysBioChalmers/DLKcat)</p>
Enzymes from the BRENDA database annotated with organism growth temperatures and predicted Topt
<p>Experimental as well as predicted organism growth temperatures were used to annotate enzymes from the BRENDA database (doi: 10.1093/nar/gky1048, https://www.brenda-enzymes.org) version 2018.2 (July 2018). The growth temperature annotation can be used as an estimate of the enzymes catalytic optima. In addition to this, the sequence of each enzyme was taken into account to predict catalytic optima, which are more accurate than simply using OGT as an estimate.</p> <p>The "enzyme_ogt_topt.tsv" file is a tab-separated file with the data headers: ec, uniprot_id, domain, organism, ogt, ogt_source, topt and topt_source. The ec column lists enzyme classes. The uniprot_id lists UniProt identifiers. The domain column lists the domain of life (superkingdom), either Archaea, Bacteria, or Eukarya. The organism column lists organism names, with strain designations removed and formatted to lowercase characters with an underscore _ separating the name parts. The ogt column lists, in degrees centigrade, the organism growth temperature. The ogt_source column lists whether experimental growth temperatures or predicted ones were used for the annotation. The topt column lists the enzyme catalytic optimum. Finally, the topt_source column lists whether experimental or predicted topt was used for the annotation.</p> <p>The "brenda_sequences_20180109.fasta" file follows the standard FASTA format and contains the protein sequences for all annotated enzymes. UniProt identifiers are used as a header for each of the sequences.</p> <p> </p>
Accurate Prediction of Enzyme Thermostabilization with Rosetta using AlphaFold Ensembles
<p>DT<sub>M</sub> vs DG<sub>f,mut</sub> values for scoring LovD, LipA, <em>p</em>-nitrobenzyl esterase, xylanase A and tryptophan 6-halogenase variants (<em>DTM_vs_DDGf_mut.xlsx</em>).</p> <p>AlphaFold predicted structures in PDB and Pymol sessions formats for top scoring LovD, LovD6, LovD9, LipA WT, LipA 6B, <em>p</em>-nitrobenzyl esterase WT, xylanase A WT and tryptophan 6-halogenase WT decoys (<em>mAF-min_ensembles.zip</em>).</p> <p>Rosetta energies for all calculations (<em>Rosetta_scores.zip</em>).</p>
Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction
<p>Dataset produced for the Project PRELUDIUM19 2020/37/N/NZ2/00967 entitled: "Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction"</p> <p>The dataset counts with the three families of enzymes used: dehalogenase, aldehyde reductase and nitrilase.</p> <p>For each enzyme, the docked structures, docked parameters and scripts to analyze them further are present. Moreover, the protocol that derives geometric descriptors from docked structures is also present.</p> <p>This work was supported by the National Science Centre, Poland (grant no. 2020/37/N/NZ2/00967)</p>
Machine learning prediction of enzyme optimal pH
<p>- Enzyme catalytic optimum pH dataset (pHopt, 9855 proteins)</p> <p>- Secreted bacterial optimum environment pH dataset (pHenv, 1.9 million proteins)</p> <p>- Model for predicting pHopt of enzymes (EpHod)</p> <p>- Code for using the EpHod model are in <a href="https://github.com/beckham-lab/EpHod">GitHub</a>. Paper in <a href="https://doi.org/10.1101/2023.06.22.544776">BioRxiv</a></p> <p> </p>
Reactzyme: A Benchmark for Enzyme-Reaction Prediction
<h3>Official dataset of <span>Reactzyme -</span> <span>Reactzyme: A Benchmark for</span><span>Enzyme-Reaction Prediction.</span></h3> <p><span>Our study utilizes a comprehensive dataset compiled from the SwissProt and Rhea databases. SwissProt, a curated subset of the UniProt database, has been selected for its high-quality, human-derived functional annotations of protein sequences. This section of UniProt is particularly valuable for its expert-reviewed entries, which ensure reliable and accurate functional data, making it ideal for our analysis. Rhea is employed for its precise mapping from enzymes to specific catalyzed functions, offering detailed descriptions of biochemical reactions. </span></p> <p><span>The SwissProt and Rhea dataset are downloaded on January 8, 2024, and includes data entries up to this date, providing the most recent and comprehensive data available for our study. We selectively exclude water molecules and unspecific functional groups that could mask the true molecular structures. Conversely, we remove metal ions, gas molecules, and other small molecules because of their potential to bind to proteins, a characteristic that presents a valuable learning feature for our model. To this end, the total dataset comprises 178,463 positive enzyme-reaction pairs, including 178,327 unique enzymes and 7,726 unique reactions. </span></p>
Dataset for manuscript "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning"
<p>Dataset for the github repository containing the code for the manuscript "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning".</p>
Dynamic profiling and binding affinity prediction of NBTI antibacte-rials against DNA gyrase enzyme by multidimensional machine learning and molecular dynamics simulations
<p>The chemical libraries used in this study comprised of 199 and 133 structurally diverse novel bacterial topoisomerase inhibitors (<em>alias</em> NBTIs), with experimentally determined <em>in vitro</em> antibacterial potencies against <em>Staphylococcus aureus</em> DNA gyrase (IC<sub>50</sub>=0.007-50 µM) and <em>Escherichia coli</em> DNA gyrase (IC<sub>50</sub>=0.020-100 µM), respectively (named as NBTI<em><sub>SA</sub></em> and NBTI<em><sub>EC</sub></em>), were compiled from the literature as *.sdf file format. The chemical structures comprising both NBTI libraries were initially sketched by using ChemDraw Professional 20.1.1 suite and subsequently energetically minimized utilizing Discovery Studio’s integrated Merck Molecular Force Field (MMFF) module. Moreover 4D ligands ensembles of both libraries ready to be used for multidimensional QSAR modeling are available, as well.</p>
Paraoxonase-1 Pseudo Cholinesterase Organophosphate Toxicity Enzyme in Prediction the Severity and Outcome of Acute Organophosphate Poisoning and Its Correlation With Pseudo Cholinesterase Enzyme Leve
ClinicalTrials.gov study NCT06108557. IPD Sharing: Not stated. Countries: 0. Publications: 4.
Data from: Predicting allosteric mutants that increase activity of a major antibiotic resistance enzyme
Open the record for dataset details and reuse information.
Modeling cross-talk of RNA modification enzymes reveals tumor microenvironment associated clinical significance and immunotherapy prediction in hepatobiliary malignancy
GEO Series GSE222334. Homo sapiens. 100 samples. Type: Expression profiling by high throughput sequencing.
Predictive potential of circulating Ube2h mRNA as an E2 ubiquitin-conjugating enzyme for diagnosis or treatment of Alzheimer’s disease
GEO Series GSE147792. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
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