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15 results for “Macromolecules”
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
Simultaneous X-ray diffraction from multiple single crystals of macromolecules
<p>X-ray diffraction datasets from the<br /> publication: K. S. Paithankar, H. O. Sørensen, J. P. Wright, S. Schmidt, H. F. Poulsen and E. F. Garman*</p> <p>Acta Cryst. (2011). D67, 608-618 doi:10.1107/S0907444911015617</p> <p>The potential in macromolecular crystallography for using multiple crystals to collect X-ray diffraction data simultaneously from assemblies of up to seven crystals is explored. The basic features of the algorithms used to extract data and their practical implementation are described. The procedure could be useful both in relation to diffraction data obtained from intergrown crystals and to alleviate the problem of rapid diffraction decay arising from the effects of radiation damage.</p>
Dataset: Highly sensitive single molecule detection of macromolecule ion beams
<p>Figure 6 & Figure 7.zip: Figure 6 Superconducting nanowire detector (SSPD) beam profile of a mass-selected Insulin (Ins5+) ion beam at 1000 eV of impact energy.; Figure 7 Area-normalized molecule detection quantum yield. - raw data and Origin 2022 files</p><p>Voltage Shifts.zip: Calibration of deflection electrodes with linear stage - raw data and Origin 2022 files</p><p>ESI source stability.opju: 1 Hour measurement of the source stability with a TOF-MS - Origin 2022 file</p><p>Figure 2.opju: Impact energy in ESI-QMS-SSPD vs. ESI-QMS-Phosphorscreen mass spectra. - Origin 2022 file</p><p>Figure 3.opju: Signal background and complexity in ESI-QMS-SSPD vs. ESI-TOF-MCP spectra. - Origin 2022 file</p><p>Figure 4.opju: Influence of Energy/Mass/Momentum/structure on the detection mechanism. - Origin 2022 file</p><p>Figure 5.opju: Confirmation of the hot-spot model for SSPD detector D2 - Origin 2022 file</p><p>Figure S2.opju: Identification of protein mass spectra. - Origin 2022 file</p><p>Figure S3.opju: ESI-QMS-SSPD spectrum measured with SSPD. - Origin 2022 file</p><p>Figure S5.opju: Beam profile at different deflection voltages. - Origin 2022 file</p><p>Figure S9.opju: Photo-activated ESI-QMS-SSPD spectrum of Ru-complex modified Insulin. - Origin 2022 file</p><p>Linear stage position readback.txt: Position readback when lifting the Linear Stage recorded with a laser triangulometer. - raw data</p>
Data for "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks"
<p>These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks" (DOI 10.1039/D2SM00452F). Three of the directories contains subdirectories with `GSD` files dumped from HOOMD. The other contains pretrained RNN models as TorchScript binaries exported from PyTorch.</p>
2017 Gradients 2 - MGL 1704 Particulate Macromolecules
<p>Measurements of ocean surface particulate macromolecule concentrations that ware made as part of the Simons Collaboration on Ocean Processes and Ecology (SCOPE) - Gradients project during the Gradients II research cruise. Samples were collected along a meridonal transect (along -158° longitude) from 26 - 41.42 ° latitude in May-June 2017. This sampling area covers much of the transition zone between the North Pacific subtropical gyre and the North Pacific subpolar gyre. </p>
A Versatile Polymer‐Based Platform for Intracellular Delivery of Macromolecules
<p>The plasma membrane barrier greatly restricts intracellular delivery of macromolecules. Currently available methods suffer from various limitations, including low delivery efficiency, high cytotoxicity, or incompatibility with a wider range of macromolecules or cell types. To overcome these issues, stimuli‐responsive polymers such as the bio‐inspired, pH‐responsive poly(l‐lysine isophthalamide) grafted with l‐phenylalanine at a stoichiometric ratio of 50% (PP50) can be used. In mildly acidic environments, the pseudopeptidic polymer can permeabilize the plasma membrane overcoming the problem of payload entrapment in the endosomes and allowing for efficient intracellular delivery. It is demonstrated that PP50 is capable of intracellular delivery by simple co‐incubation at pH 6.5 with various macromolecules, including different‐sized Dextrans, green fluorescent protein (GFP), and an apoptotic peptide. The delivery process is fast, nontoxic, and compatible with multiple cell types, including adherent and suspension cell lines, primary human mesenchymal stem cells, and cells grown as spheroids. In addition, apoptotic peptide delivery by co‐incubation with PP50 is over three times more effective than delivery using other common methods, including poly(ethyleneimine) (PEI), cell penetrating peptides (CPPs), and electroporation. The findings suggest that payload delivery by co‐incubation with PP50 is a flexible, controllable method allowing delivery of various payloads to many different cell types in vitro.</p>
Towards genetic modification of plant-parasitic nematodes: Delivery of macromolecules to male germlines and expression of exogenous mRNA in second stage juveniles
<p>Plant-parasitic nematodes are a current and future threat to food security, causing an estimated 100 billion USD in crop losses each year. The most problematic are the obligate sedentary endoparasites (primarily root knot nematodes and cyst nematodes). Progress in understanding their biology is held back by a lack of tools for functional genetics. Forward genetics is largely restricted to studies of natural variation in populations, and reverse genetics is entirely reliant on RNA interference. There is an expectation that the development of functional genetic tools would accelerate progress in plant-parasitic nematology, and hence the development of novel control solutions. Here, we develop some of the foundational biology required to deliver a functional genetic "tool kit" in plant-parasitic nematodes. We characterise the gonads of male <em>Heterodera schachtii</em> and Meloidogyne hapla in the context of spermatogenesis. We test and optimise various methods for the delivery, expression, and/or detection of exogenous nucleic acids in plant-parasitic nematodes. We demonstrate that delivery of macromolecules to cyst and root knot nematode male germlines is difficult but possible. Similarly, we demonstrate the delivery of oligonucleotides to root knot nematode gametes. Finally, we develop a transient expression system in plant-parasitic nematodes by demonstrating the delivery and expression of exogenous mRNA encoding various reporter genes throughout the body of <em>H. schachtii</em> juveniles using lipofectamine-based transfection. We anticipate these developments to be independently useful, and, taken together, will expedite the development of genetic modification protocols for sedentary endoparasitic nematodes, and ultimately catalyze research on a group of nematodes that threaten global food security.</p>
Data for "Heuristic algorithms in Evolutionary Computations and modular organization of biological macromolecules: applications to in vitro evolution"
<p>This publication contains data for the construction of the figures and tables for the paper "Heuristic algorithms in Evolutionary Computations and modular organization of biological macromolecules: applications to in vitro evolution" accepted to publication in PLOS ONE.</p>
Macromolecule distributions input file for the OCEANFILMS parameterization
<p>This dataset provides the inputs required for the OCEANFILMS parameterization of sea spray organic matter emissions, as it has been implemented in the Energy Exascale Earth System Model version 1 (E3SMv1; Burrows et al., 2014; 2022). The file was generated as described in Burrows et al. (2014). Briefly, we used the Parallel Ocean Program (POP; Maltrud et al., 1998) to simulate the general circulation and its biogeochemical elemental cycling (BEC) routines (Moore et al., 2004) to simulate marine biogeochemistry. Both are components of the Community Earth System Model (CESM; http://www2.cesm.ucar.edu, Hurrell et al., 2013). Calculations were performed using the CESM 1.0 beta release 11.</p> <p>The macromolecular distributions contained in these files were calculated based on various fields from these CESM simulations.</p> <p>1. <strong>Lipids</strong> were calculated on the basis of the POP-simulated phytoplankton and zooplankton concentrations (Burrows et al., 2014; Eq. 11).</p> <p>2. <strong>Polysaccharides</strong> were assumed to have a concentration identical to POP-simulated semilabile dissolved organic carbon (SDOC; Burrows et al., 2014; Eq. 12).</p> <p>3. <strong>Proteins</strong> were assumed to have a concentration equal to one-third of POP-simulated SDOC (Burrows et al., 2014; Eq. 13).</p> <p>4. A <strong>processed</strong> DOC group was estimated as a residual term from these surface pools (Burrows et al., 2014; Eq. 14, and surrounding text). This field has negligible impacts on the results of the OCEANFILMS parameterization, but has been retained here for clarity.</p> <p>5. A <strong>humic</strong> compound group was estimated by assuming a fixed concentration of humic substances below the mixed layer, and estimating the effects of mixing this humic matter into the ocean mixed layer (Burrows et al., 2014; Eq. 17). As with the processed DOC group, this field's impact on the resulting predictions of the OCEANFILMS parameterization is negligible, but it has been retained here for clarity.</p> <p>All fields have the units µM C.</p> <p>The dataset is also available form the E3SM inputdata repository (last access 17 March 2022): https://web.lcrc.anl.gov/public/e3sm/inputdata/atm/cam/chem/trop_mam/marine_BGC/monthly_macromolecules_0.1deg_bilinear_latlon_year01_merge_date.nc </p>
Towards genetic modification of plant-parasitic nematodes: Delivery of macromolecules to male germlines and expression of exogenous mRNA in second stage juveniles
Open the record for dataset details and reuse information.
Supporting Data for "Ionic and Nonspherical Polymer Nanoparticles in Nonpolar Solvents" (Macromolecules, doi: 10.1021/acs.macromol.0c00121)
<p>All data are named with the monomer composition of the block copolymer (SMA, BzMA, or MOTMA) and the type of data (SAXS, DLS, or TEM).</p> <p>Small-angle X-ray scattering (SAXS) reciprocal space scattering data [Q in 1/Å, I(Q) in arb. unit, uncertainty I(Q)]</p> <p>Dynamic-light scattering (DLS) intensity-weighted size distributions [Bin location in nm, Proportion in %]</p> <p>Transmission electron microscopy (TEM) micrographs [Scale bars are all 200 nm]</p>
Data and code for, "Large language models design sequence-defined macromolecules via evolutionary optimization"
<div> <pre># Codes and data for "Large language models design sequence-defined macromolecules via evolutionary optimization"<br><br>Note this repository contains codes and data files for the manuscript. This is a snapshot of the repository, frozen at the time of submission.<br><br># Codes<br><br>## LLM codes<br>- `run_claude.py` - the routine for performing LLM-based rollouts; intended for command line execution using argparse<br>- `message_utils.py` - utilities for constructing and parsing messages for LLM I/O<br>- `model_utils.py` - lightweight utilities for retrieving formatted predictions from the RNN ensemble<br>- `target_defs.py` - defines the sequence, locations, and natural language descriptions of the target structures<br>- `ask_about_oracle.ipynb` - asks the LLM to speculate about the nature of the optimization task<br><br>## other algorithms<br>- `active_learning.ipynb` - use EI acquisition with RF surrogate to label new sequences; includes an unused tokenization scheme<br>- `evolutionary_algorithm.ipynb` - use DEAP library to perform evolutionary optimization<br>- `random_sampling.ipynb` - sample sequences randomly from all possible sequences<br><br>## postprocessing<br>- `process_aggregated_logs.py` - reads data from the raw log files and prepares them for visualization<br>- `process_sample_rollouts.py` - reads data from the raw log files and prepares individual rollouts<br><br>## visualization<br>- `figure1b.ipynb` - renders panel b of Fig. 1<br>- `figure1efg.ipynb` - renders the last row of Fig. 1 (panels e-g)<br>- `figure2.ipynb` - renders all of Fig. 2<br>- `figure_si.ipynb` - renders Figs. S1 and S2<br>- `figure_md_validation.ipynb` - renders Fig. S3<br><br># Data files<br><br>- `prompts/`<br> - `prompt-scientific-v4.4.yml` - the full text of the scientific prompt, to be read by `run_claude.py`<br> - `prompt-oracle-v4.4.yml` - the full text of the oracle prompt, to be read by `run_claude.py`<br>- `models/` - the TorchScript RNN models used to make predictions<br>- `data/`<br> - `embeddings` - calculated embeddings for a collection of sequences from our prior work<br> - `llm-logs` - the raw logs obtained from the Claude 3.5 Sonnet LLM (other algorithms made to look like the LLM logs after the fact)<br> - `llm-logs-opus` - the raw logs obtained from the Claude 3.0 Opus LLM (used in the first draft of the article, replaced by Claude 3.5 Sonnet) <br> - `all-rollouts-kltd.csv` - postprocessed logs for all the rollouts using the "top $k < d^*$" metric<br> - `all-rollouts-topkd.csv` - postprocessed logs for all the rollouts using the "mean $d$ for top $k$" metric<br> - `sample-rollout-membranes-x-3.csv` - postprocessed logs for a single rollout replica, `x` = each algorithm type<br> - `snapshots` - png snapshots of MD simulation results at different locations in the manifold</pre> </div>
Use of a Synthetic Macromolecule (Hydroxypropyl Cellulose ) and Trehalose as Additives for Oocyte Vitrification
ClinicalTrials.gov study NCT01745523. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Synthetic amorphous magnesium-substituted calcium phosphate: silent constructs that trap macromolecules for intracellular delivery
GEO Series GSE59498. Homo sapiens. 28 samples. Type: Expression profiling by array.
Developing high throughput single-cell single-nucleotide in situ pairwise sequencing for macromolecule spatail-omics
GEO Series GSE190219. Mus musculus; Rattus norvegicus. 2 samples. Type: Expression profiling by high throughput sequencing.
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