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31 results for “peptide design”
Machine learning designs non-hemolytic antimicrobial peptides
<p>The upload contains additional primary data associated with the publication, including raw data in the original file format whenever possible.</p> <p>Data content: HRMS, HPLC-MS, CD, MD, TEM</p>
Molecular dynamics simulation data of designed cyclic peptide (ligand-only)
<p>Trajectories of <strong>ligand-only </strong>simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. <br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Training data for "PepINVENT: Generative peptide design beyond the natural amino acids"
<p>The zipped file contains the training and the validation data used to train the PepINVENT model.</p>
Supporting data for: "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides"
<p>This repository contains supporting data and code for the paper titled "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides" by Kirill Shmilovich, Sayak Subhra Panda, Anna Stouffer, John D. Tovar, and Andrew L. Ferguson.</p>
Molecular dynamics simulation data of designed cyclic peptide - ligand 4 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 4. Due to the file size limitation, ligand 1-3 data and simulation set-up files can be found here: http://doi.org/10.5281/zenodo.3780463<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Molecular dynamics simulation data of designed cyclic peptide - ligand 1-3 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 1-3. Ligand 4 data can be found here: http://doi.org/10.5281/zenodo.3782629<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Umbrella simulation data of designed cyclic peptide as MDM2 binders
<p>Umbrella simulation data of designed cyclic peptide as MDM2 binders.</p> <p>Pulling simulation: start from bound MDM2/ligand to pull the ligand out</p> <p>Pushing simulation: start from unbound ligand to push it bind to MDM2</p> <p>Details of umbrella simulation parameters can be found in simulation set-up folders.</p>
Design of linear and cyclic peptide binders
<p>The data is compressed with zstd: https://github.com/facebook/zstd and tar. To uncompress do: </p> <p>tar --use-compress-program full/path/to/zstd -xvf file.tar.zst</p> <p>PDB files of predicted protein-peptide complexes from the design process:</p> <p>1.1G 1ssc_untargeted_cyclic_afm.tar.zst <br>1.1G 1ssc_untargeted_cyclic.tar.zst<br>1.1G 1ssc_untargeted_linear_afm.tar.zst<br>1.1G 1ssc_untargeted_linear.tar.zst</p> <p>Selected PDB files of predicted protein-peptide complexes from the design process:</p> <p>(440 KB each). 1ssc_adversarial_linear_sel, 1ssc_tp_cyclic_sel, 1ssc_tp_linear_sel</p> <p>Metrics:</p> <p>1ssc_cyclic_merged_with_solubility.csv - all cyclic design metriccs</p> <p>1ssc_linear_merged_with_solubility.csv - all linear design metriccs</p> <p>1ssc_linear_adversarial_sel.csv - adversarial selection for linear design</p> <p>1ssc_linear_tp_sel.csv - top selection for linear design</p> <p>spr_results.csv - SPR affinities (Kd)</p> <p>1ssc_cyclic_tp_sel.csv - top selection for cyclic design</p> <p>linear_top_adversarial_pae.csv - PAE scores for linear top and adversarial designs</p>
FASST Structure Database files for "Tertiary motifs as building blocks for the design of protein-binding peptides"
<p>FASST Database files for use in the <a href="https://github.com/swanss/peptide_design">peptide design</a> pipeline.</p> <p>A complete list of structures provided in the databases is provided in the supplementary information of the Protein Science article.</p> <p>Singlechain structures: <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.4322&file=pro4322-sup-0004-TableS6.txt">pro4322-sup-0004-TableS6.txt</a> (format: PDBID_CHAINID)</p> <p>Multichain structures: <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.4322&file=pro4322-sup-0005-TableS7.txt">pro4322-sup-0005-TableS7.txt</a> (format: PDBID)</p>
DESIGN, SYNTHESIS AND STUDY OF BIOLOGICAL ACTIVITY OF PEPTIDES WITH NEUROPROTECTIVE PROPERTIES
<p>The main purpose of this project is to search for novel compounds that will protect neurons against oxidative stress (6-hydroxydopamine, 6-OHDA) and glucocorticoids (corticosterone, CORT) in a human neuroblastoma cell line (SH-SY5Y) and elucidate the mechanism by which attenuated the physiological changes induced by these neurotoxins. We will explore whether this protective role of tested compounds will be involved also in the regulation of apoptosis, maintaining oxidoreductive balance, and stabilization of mitochondrial membrane potential. Furthermore, the involvement of the BDNF/TrkB-ERK-CREB/mTOR signalling pathway in neuronal survival will be examined.</p>
HLApollo: Towards designing improved cancer immunotherapy targets with a superior peptide-MHC-I presentation model
<p><strong>Based on the success of cancer immunotherapy, personalized cancer vaccines have recently emerged as the vanguard of oncology treatment. Because antigen presentation on MHC class I (MHC-I) is key to the adaptive immune response to cancerous cells, it is critical to have highly predictive computational methods to model which peptides are presented on MHC-I. Here, we introduce HLApollo, a transformer-based model with end-to-end treatment of MHC-I sequence, deconvolution of multi-allelic data, and ligand-flanking sequences. We develop negative-set switching, a novel training strategy that greatly reduces overfitting, which is key to HLApollo’s performance, leading to increases of 20.19% and 4.1% in average precision (AP) vs. next best model on MHC-I presentation and immunogenicity, respectively. Incorporating protein features derived from protein language models yielded further gains and reduced the need for gene expression measurements. We achieve excellent pan-allelic generalization, and create a framework for estimating performance on untrained alleles. This guides the clinical use of HLApollo, where rare alleles may be observed – particularly for individuals from underrepresented ancestries. Our work uses all facets of available MHC-I data to develop a highly accurate MHC-I presentation predictor that meaningfully improves immunogenicity prediction and allelic coverage, important for clinical applications of personalized neoantigen vaccines.</strong></p>
PepBench: Dataset for Protein-Binding Peptide Design
<p>Datasets and splits of protein-peptide complexes benchmark from <a href="https://arxiv.org/abs/2402.13555">PepGLAD</a>.</p> <p>V2 Updates:</p> <ol> <li>The size of ProtFrag augmentation dataset is 70498 instead of 70645. The latest index file has deleted duplicated entries.</li> <li>Clustering results for complexes in training/validation sets are uploaded in train_valid.</li> </ol>
Dataset for Peptide binder design with inverse folding and protein structure prediction
<p>Dataset for a paper on peptide design</p> <p> </p> <p><br> mutated_peptides - results for randomly intriduced mutations in protein-peptide complexes that can be predicted at 2 Å (Figure 1)<br> pdb_peptide - variation in the number of recycles (1-10) for 96 peptides (Figure 1)<br> minibinder - results for the minibinder set (Figure 2)<br> Pfam - results for the Pfam set (Figures 4+5)<br> protein_mpnn - results on protein_mpnn test set (Figure 6)</p> <p> </p> <p> </p>
Simulation Data for Design of Peptides that Fold and Self-Assemble on Graphite
<p>This data set for the manuscript entitled "Design of Peptides that Fold and Self-Assemble on Graphite" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and trajectories in dcd format (downsampled to 10 ns per frame). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>Version: 2.0</p> <p>Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.</p> <p><br> Conventions Used in These Files<br> ===============================</p> <p>Structure Files<br> ----------------<br> - graph_*.psf or sol_*.psf (original NAMD (XPLOR?) format psf file including atom details (type, charge, mass), as well as definitions of bonds, angles, dihedrals, and impropers for each dipeptide.)</p> <p>- graph_*.pdb or sol_*.pdb (initial coordinates before equilibration)<br> - repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl)<br> - freeTop_*.pdb (same as the above pdb files, but the carbons of the lower graphene layer have been placed at a single z value and marked for restraints in NAMD)<br> - amber_*.prmtop (combined topology and parameter files for Amber force field simulations)<br> - repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)</p> <p>Force Field Parameters<br> ----------------------<br> CHARMM format parameter files:<br> - par_all36m_prot.prm (CHARMM36m FF for proteins)<br> - par_all36_cgenff_no_nbfix.prm (CGenFF v4.4 for graphene) The NBFIX parameters are commented out since they are only needed for aromatic halogens and we use only the CG2R61 type for graphene.<br> - toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included and others commented out)</p> <p>Template NAMD Configuration Files<br> ---------------------------------<br> These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory):<br> - template_min.namd (minimization)<br> - template_eq.namd (NPT equilibration with lower graphene fixed)<br> - template_abf.namd (for adaptive biasing force)</p> <p>Minimization<br> -------------<br> - namd/min_*.0.namd</p> <p>Equilibration<br> -------------<br> - namd/eq_*.0.namd</p> <p>Adaptive biasing force calculations<br> -----------------------------------<br> - namd/eabfZRest7_graph_chp1404.0.namd<br> - namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)</p> <p>Log Files<br> ---------<br> For each NAMD configuration file given in the last two sections, there is a log file with the same prefix, which gives the text output of NAMD. For instance, the output of namd/eabfZRest7_graph_chp1404.0.namd is eabfZRest7_graph_chp1404.0.log.</p> <p>Simulation Output<br> -----------------<br> The simulation output files (which match the names of the NAMD configuration files) are in the output/ directory. Files with the extensions .coor, .vel, and .xsc are coordinates in NAMD binary format, velocities in NAMD binary format, and extended system information (including cell size) in text format. Files with the extension .dcd give the trajectory of the atomic coorinates over time (and also include system cell information). Due to storage limitations, large DCD files have been omitted or replaced with new DCD files having the prefix stride50_ including only every 50 frames. The time between frames in these files is 50 * 50000 steps/frame * 4 fs/step = 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.</p> <p>Scripts<br> -------<br> Files with the .sh extension can be found throughout. These usually provide the highest level control for submission of simulations and analysis. Look to these as a guide to what is happening. If there are scripts with step1_*.sh and step2_*.sh, they are intended to be run in order, with step1_*.sh first.</p> <p><br> CONTENTS<br> ========</p> <p>The directory contents are as follows. The directories Sim_Figure-1 and Sim_Figure-8 include README.txt files that describe the files and naming conventions used throughout this data set.</p> <p>Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.</p> <p>Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.</p> <p>Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.</p> <p>Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.</p> <p>Sim_Figure-5_replica: Temperature replica exchange molecular dynamics simulations for the peptide cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) with 20 replicas for temperatures from 295 to 454 K.</p> <p>Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).</p> <p>Sim_Figure-7: Free energy calculations for folding, adsorption, and pairing for the peptide CHP1404 (sequence: cyc(GTGSGTG-GPGG-GCGTGTG-SGPG)). For folding, we calculate the PMF as function of RMSD by replica-exchange umbrella sampling (in the subdirectory Folding_CHP1404_Graphene/). We make the same calculation in solution, which required 3 seperate replica-exchange umbrella sampling calculations (in the subdirectory Folding_CHP1404_Solution/). Both PMF of RMSD calculations for the scrambled peptide are in Folding_scram1404/. For adsorption, calculation of the PMF for the orientational restraints and the calculation of the PMF along z (the distance between the graphene sheet and the center of mass of the peptide) are in Adsorption_CHP1404/ and Adsorption_scram1404/. The actual calculation of the free energy is done by a shell script ("doRestraintEnergyError.sh") in the 1_free_energy/ subsubdirectory. Processing of the PMFs must be done first in the 0_pmf/ subsubdirectory. Finally, files for free energy calculations of pair formation for CHP1404 are found in the Pair/ subdirectory.</p> <p>Sim_Figure-8: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) where the peptides are far above the graphene–water interface in the initial configuration.</p> <p>Sim_Figure-9: Two replicates of a simulation of nine peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-9_scrambled: Two replicates of a simulation of nine peptide molecules with the control sequence cyc(GGTPTTGGGGGGSGGPSGTGGC) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-10: Adaptive biasing for calculation of the free energy of the folded peptide as a function of the angle between its long axis and the zigzag directions of the underlying graphene sheet.</p> <p> </p>
Data associate with the publication "De novo designed peptides for cellular delivery and subcellular localisation"
<p>All raw data required to reproduce the findings in the manuscript "De novo designed peptides for cellular delivery and subcellular localisation"</p>
Datasets for Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
<p>PepPC-F and PepPC Datasets for Target-Specific De Novo Peptide Binder Design with DiffPepBuilder</p>
Molecular dynamics simulation data of designed cyclic peptide (ligand-binding)
<p>Trajectories of ligand binding simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. <br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Design and functional characterization of Salmo salar TLR5 agonist peptides derived from High Mobility Group B1 acidic tail.
<p>Based on the structural knowledge of TLR5 surface using blind docking platforms, a series of peptides derived from HMGB1 truncated acidic tail from Salmo salar was designed TLR5 agonistic. Also, a template peptide with the wild type C-terminal acidic tail sequence as reference was included (SsOri). Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of complex.</p>
Supplementary material 1 from: Al Tall Y, Al-Nassar B, Abualhaijaa A, Sabi SH, Almaaytah A (2023) The design and functional characterization of a novel hybrid antimicrobial peptide from Esculentin-1a and melittin. Pharmacia 70(1): 161-170. https://doi.org/10.3897/pharmacia.70.e97116
RP-HPLC analysis Chromatogram of BKR1 peptide
Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings
<p>The Tables S3 - S5 are a supplement for the article "Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings" uploaded as a Preprint in ChemRxiv DOI: 10.26434/chemrxiv-2023-1kqm3</p>
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