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1. Molecular Dynamics Simulation of a Designed Cyclic Peptide Bound to CTLA4

   https://doi.org/10.5281/zenodo.7186684  

   Thakkar, Ravindra; Comer, Jeffrey (October 2022, RSC medicinal chemistry)

   This data set for the manuscript entitled "Computational Design of a Cyclic Peptide that Inhibits the CTLA4 Immune Checkpoint Pathway" includes all files needed to run and analyze the simulations of a designed cyclic peptide (Peptide 16) bound to CTLA4 in the putative most stable binding configuration, which is detailed in Figure 6 of the paper. These files include molecular model structure files (NAMD psf), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, 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. These scripts and their output are also included. Version: 1.0 Conventions Used in These Files =============================== Structure Files ---------------- - ctla4_P16_wat.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.) - ctla4_P16.pdb (initial coordinates before equilibration) - repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl) - rest*.pdb (same as the above pdb files, but atoms have been marked for restraints in NAMD. These files are generated by doPrep.sh, with restraints applied to different atoms.) Force Field Parameters ---------------------- CHARMM format parameter files: - par_all36m_prot.prm (CHARMM36m FF for proteins) - toppar_water_ions_prot.str (CHARMM water and ions with NBFIX parameters needed for protein and others commented out) Template NAMD Configuration Files --------------------------------- These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory): - template_min.namd (minimization) - template_rest.namd (NPT equilibration with different parts of the protein restrained) - template_prod.namd (for the long production simulations) Minimization ------------- - namd/min_*.0.namd Restraints ------------- - namd/rest_*.0.namd (both CTLA4 binding site and peptide atoms are restrained) - namd/rest_*.1.namd (CA atoms of CTLA4 and all atoms of the peptide are restrained) - namd/rest_*.2.namd (all atoms of only the peptide are restrained) - namd/rest_*.3.namd (only CA atoms of only the peptide are restrained) - namd/rest_*.4.namd (no atoms are restrained) Production ------------- - namd/pro_*.{D,E,F}.0.namd Analysis ------------- - interaction.sh (Shell script for running analysis with VMD) - calcSeparationNearestAtom.tcl (Calculate the separation between two selections, taking the shortest distance between any pair of atoms spanning the two selections. Accounts for (orthogonal) periodic boundary conditions.) - useful.tcl (VMD Tcl script with a library of useful procs, used by the script above) - sep_*.dat (Output of the above analysis containing rows with two columns: time in nanoseconds and minimum distance in Å) Scripts ------- 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. 

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2. Data for: Predicting the contribution of single trait evolution to rescuing a plant population from demographic impacts of climate change

   https://doi.org/10.5061/dryad.ht76hdrtn  

   Campbell, Diane; Powers, John; Kipness, Justin; Laboratory, Rocky_Mountain Biological (January 2025, Dryad)

   {"Abstract":["Evolutionary adaptation can allow a population to persist in the face of a\n new environmental challenge. With many populations now threatened by\n environmental change, it is important to understand whether this process\n of evolutionary rescue is feasible under natural conditions, yet work on\n this topic has been largely theoretical. We used unique long-term data to\n parameterize deterministic and stochastic models of the contribution of\n one trait to evolutionary rescue using field estimates for the subalpine\n plant Ipomopsis aggregata and hybrids with its close relative I.\n tenuituba. In the absence of evolution or plasticity, the two studied\n populations are projected to go locally extinct due to earlier snowmelt\n under climate change, which imposes drought conditions. Phenotypic\n selection on specific leaf area (SLA) was estimated in 12 years and\n multiple populations. Those data on selection and its environmental\n sensitivity to annual snowmelt timing in the spring were combined with\n previous data on heritability of the trait, phenotypic plasticity of the\n trait, and the impact of snowmelt timing on mean absolute fitness.\n Selection favored low values of SLA (thicker leaves). The evolutionary\n response to selection on that single trait was insufficient to allow\n evolutionary rescue by itself, but in combination with phenotypic\n plasticity it promoted evolutionary rescue in one of the two populations.\n The number of years until population size would stop declining and begin\n to rise again was heavily dependent upon stochastic environmental changes\n in snowmelt timing around the trend line. Our study illustrates how field\n estimates of quantitative genetic parameters can be used to predict the\n likelihood of evolutionary rescue. Although a complete set of parameter\n estimates are generally unavailable, it may also be possible to predict\n the general likelihood of evolutionary rescue based on published ranges\n for phenotypic selection and heritability and the extent to which early\n snowmelt impacts fitness."],"Methods":["The study sites consisted of three “Poverty Gulch” sites in\n Gunnsion National Forest and one site “Vera Falls” at the Rocky Mountain\n Biological Laboratory, all in Gunnison County, CO, USA. Focal plants\n included two sets of plants. One set (data from 2009-2019) consisted of\n plants in common gardens at three sites: an I. aggregata\n site (hereafter “agg”), an I. tenuituba\n site (hereafter “ten”) and a site at the center of the natural\n hybrid zone (hereafter “hyb”). The second set consisted of plants growing\n in situ at two of the same Poverty Gulch sites (“agg” and “hyb”), and\n an I. aggregata site at Vera Falls (hereafter “VF”;\n data from 2017-2023).  The common gardens were started\n from seed in 2007 and 2008. Measurements of SLA in these gardens began\n when plants were 2 years old, either 2009 or 2010 depending upon the\n garden, as they are only small seedlings during their first summer after\n seed maturation. By 2018, all but 15 of the 4512 plants originally planted\n had died, with or without blooming, and we stopped following these\n gardens. Starting in 2017, in situ vegetative plants at the I.\n aggregata site and the hybrid site whose longest leaf exceeded\n 25 mm were marked with metal tags to facilitate\n identification.   In each year of the study, one leaf\n from each vegetative plant was collected in the field and transported on\n ice to the RMBL, 8 km distant. There each leaf was scanned with a flatbed\n scanner and analyzed using ImageJ to measure leaf area. The leaf was dried\n at 70 deg C for 2 hours and then weighed to obtain dry mass and calculate\n SLA as area/dry mass. For plants in the common gardens, SLA was measured\n on 982 leaves from 383 plants in 2009 – 2014. For in situ plants, SLA was\n measured on one leaf from each of 877 plants in 2017 – 2022. Fitness was\n estimated as the binary variable of survival to flowering. Plants that\n were still alive in 2019 in the common gardens or in 2023 at the end of\n the study were assumed to survive to flowering. These\n data were used to estimate selection differentials on SLA in each of 12\n years. We then combined this information with previous information on\n heritability and the effect of snowmelt date in the spring on mean\n absolute fitness, measured as the finite rate of population increase, from\n a previous demographic study. This information was used to parameterize\n models of evolutionary rescue that we developed. We developed two models\n that differed in how snowmelt timing changed: a Step-change model and a\n Gradual environmental change model and analyzed both deterministic and\n stochastic versions. All analysis and modeling was done in R ver\n 4.2.2.  "],"TechnicalInfo":["# Data for: Predicting the contribution of single trait evolution to\n rescuing a plant population from demographic impacts of climate change\n Dataset DOI: [10.5061/dryad.ht76hdrtn](10.5061/dryad.ht76hdrtn) ##\n Description of the data and file structure File\n "mastervegtraitsSLA2023.csv" contains data on specific leaf area\n for Ipomopsis plants in the field. Files\n "masterdemography_insitu_2023.csv" and\n "masterdemography_commongarden.csv" provide the corresponding\n information on survival to flowering. File "snowmelt.csv"\n provides dates of snowmelt in the spring. File\n "selection_vs_snowmelt.csv" provides intermediate results on\n selection intensities from analysis with the first parts of the code\n "Campbell-EvolutionLettersMay2025.Rmd". File\n "IPMresults.csv" provides estimates of the finite rate of\n increase (lambda) predicted from the publication by Campbell\n [https://doi.org/10.1073/pnas.1820096116](https://doi.org/10.1073/pnas.1820096116) File "Campbell-EvolutionLettersMay2025.Rmd" provides the R code for statistical analysis and the deterministic and stochastic models of evolutionary rescue. All data analysis and modeling was done in R ver. 4.4.2 on a Windows machine. All necessary input data files are provided. The R code is annotated to indicate which portions produce analyses and figures in the manuscript. For the multipart figures 6-9 the code needs to be manually updated to produce each part of the figure before assembling them. In those cases, each part represents a model with a unique set of parameters. ### Files and variables #### File: Data\\_files\\_for\\_EVL\\_Campbell\\_2025.zip **Description:** All data files Blank cells are indicated by "." except in "selection_vs_snowmelt.csv" where they are indicated by "NA" **File:** mastervegtraitsSLA2023.csv * meltday = first day of bare ground at the Rocky Mountain Biological Lab (RMBL) in units of days starting with January 1 * year = year * site = site. agg = site with I. aggregata. hyb = site with natural hybrids. ten = site with I. tenuituba. VF = Vera Falls site containing I. aggregata. * idtag = metal tag used to identify plant * planttype = type of plant. AA = progeny of I. aggregata x I. aggregata. AT = progeny of I. aggregata x I. tenuituba. TA = progeny of I. tenuituba x I. aggregata. TT = progeny of I. tenuituba x I. tenuituba. F2 = progeny of F1 (either AT or TA) x F1. agg = natural I. aggregata. hyb = natural hybrid. * sla = specific leaf area in units of cm2/g * uniqueid = an id used to identify the plant uniquely across all years and sites **File:** masterdemography\\_insitu\\_2023.csv * site = site. agg = site with I. aggregata. hyb = site with hybrids. VF = Vera Falls site containing I. aggregata. * idtag = metal tag used to identify plant * yeartagged = year the plant was first tagged * flrlabelxx = label for plants flowering in year 20xx * stagexxxx = stage in year xxxx. 0 = dead. 1 = single vegetative rosette. 2 = single inflorescence. 3 = multiple vegetative rosette. 4 = multiple inflorescence. * lengthxx = length of longest leaf in year 20xx in mm * leavesxx = number of leaves in rosette(s) in year 20xx **File:** masterdemography_commongarden.csv * site = site. agg = site with I. aggregata. hyb = site with natural hybrids. ten = site with I. tenuituba. * IDTAG = metal tag used to identify plant * Planttype = type of plant. AA = progeny of I. aggregata x I. aggregata. AT = progeny of I. aggregata x I. tenuituba. TA = progeny of I. tenuituba x I. aggregata. TT = progeny of I. tenuituba x I. tenuituba. F2full = full-sib progeny of F1 (either AT or TA) x F1. F2non = non full-sib progeny of F1 x F1. * stagexx = stage of plant in year 20xx. 0 = dead. 1 = single vegetative rosette. 2 = single inflorescence. 3 = multiple vegetative rosette. 4 = multiple inflorescence. * lengthxx = length of longest leaf in year 20xx in mm. * leavesxx = number of leaves in rosette(s) in year 20xx. **File:** snowmelt.csv * Year = year * Snowmelt = day of first bare ground at the RMBL in units of day starting with January 1. Values prior to 1975 were estimated. **File:** selection*vs*snowmelt.csv * meltday = day of first bare ground at the RMBL in units of day starting with January 1. * year = year * Sbyyearwithsite = standardized selection differential on SLA in model that includes site. These values are reproduced with standard errors in Table 1. * bwithsite = regression coefficient for raw survival on raw SLA in model that includes site. * meansurv = mean survival * covwsla = raw selection differential on SLA * bwithsitehyb = regression coefficient for raw survival on SLA at site hyb * meansurvhyb = mean survival at site hyb * covwslahyb = raw selection differential on SLA at site hyb used in the Gradual environmental change model * covwslaagg = raw selection differential on SLA at site agg used in the Gradual environmental change model * meansurvagg = mean survival at site agg * melthyb = estimated date of bare ground at site hyb * meltagg = estimated date of bare ground at site agg **File:** IPMresults.csv * site = site. agg = site with I. aggregata. hyb = site with natural hybrids. * day = predicted day of snowmelt (all predictions are from Campbell, D. R. 2019. Early snowmelt projected to cause population decline in a subalpine plant. PNAS (USA) 116(26) 1290-12906.) Units are days starting with January 1. * lambda = predicted finite rate of increase **File:** Campbell-EvolutionLettersMay2025.Rmd Contains R code for data analysis and modeling. All analysis and modeling was done in R ver 4.2.2."]} 

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3. ToPoRg-18k: dataset of single-chain radii of gyration distribution for 18,450 architecturally diverse and chemically patterned coarse-grained polymers

   https://doi.org/10.5281/ZENODO.13619700  

   Jiang, Shengli; Michael_A, Webb (January 2025, Zenodo)

   Revision: This revision includes four independent trajectory values of the ensemble averages of the mean squared radii of gyration and their standard deviations, which can be used to compute statistical measures such as the standard error. This distribution provides access to 18,450 configurations of coarse-grained polymers. The data is provided as a serialized object using the `pickle' Python module and in csv format. The data was compiled using Python version 3.8.  ReferencesThe specific applications and analyses of the data are described in 1.  Jiang, S.; Webb, M.A. "Physics-Guided Neural Networks for Transferable Prediction of Polymer Properties" DataThere are seven .pickle files that contain serialized Python objects. pattern_graph_data_*_*_rg_new.pickle: squared radii of gyration distribution from MD simulation. The number indicates the molecular weight range. rg2_baseline_*_new.pickle: squared radii of gyration distribution from Gaussian chain theoretical prediction. delta_data_v0314.pickle: torch_geometric training data. UsageTo access the data in the .pickle file, users can execute the following: # LOAD SIMULATION DATADATA_DIR = "your/custom/dir/"mw = 40 # or 90, 190 MWs filename = os.path.join(DATA_DIR, f"pattern_graph_data_{mw}_{mw+20}_rg_new.pickle")with open(filename, "rb") as handle:    graph = pickle.load(handle)    label = pickle.load(handle)    desc  = pickle.load(handle)    meta  = pickle.load(handle)    mode  = pickle.load(handle)    rg2_mean   = pickle.load(handle)    rg2_std    = pickle.load(handle) ** 0.5 # var # combine asymmetric and symmetric star polymerslabel[label == 'stara'] = 'star'# combine bottlebrush and other comb polymerslabel[label == 'bottlebrush'] = 'comb'  # LOAD GAUSSIAN CHAIN THEORETICAL DATAwith open(os.path.join(DATA_DIR, f"rg2_baseline_{mw}_new.pickle"), "rb") as handle:    rg2_mean_theo = pickle.load(handle)[:, 0]    rg2_std_theo = pickle.load(handle)[:, 0] graph: NetworkX graph representations of polymers. label: Architectural classes of polymers (e.g., linear, cyclic, star, branch, comb, dendrimer). desc: Topological descriptors (optional). meta: Identifiers for unique architectures (optional). mode: Identifiers for unique chemical patterns (optional). rg2_mean: Mean squared radii of gyration from simulations. rg2_std: Corresponding standard deviation from simulations. rg2_mean_theo: Mean squared radii of gyration from theoretical models. rg2_std_theo: Corresponding standard deviation from theoretical models. Help, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu GitHubAdditional data and code relevant for this study is additionally accessible at https://github.com/webbtheosim/gcgnn 

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4. The Absolute Age of NGC3021

   https://doi.org/10.5281/zenodo.10729300  

   Ying, Martin; Chaboyer, Brian; Du, Wenxin (January 2024, Zenodo)

   Differential reddening corrected HST ACS photometry, Monte Carlo Parameters and Monte Carlo isochrones used to estimate the absolute age of NGC3201. The columns in the NGC3201_fitstars_DRCR.dat  file are:x   = star x position on the master framey   = star y position on the master framev   = F606W VEGA magi    = F814W VEGA magvi  = V-I VEGAmag Detailed instruction to read MC parameters and MC isochrones can be found in the notebook: Instruction on reading NGC3201 isochrones.ipynb 

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5. COAWST model simulations of warm and cool nearshore rip-current plumes

   https://doi.org/10.5281/zenodo.4272080  

   Moulton, Melissa (January 2020, Zenodo)

   This archive contains COAWST model input, grids and initial conditions, and output used to produce the results in a submitted manuscript. The files are:</p> model_input.zip: input files for simulations presented in this paper   ocean_rip_current.in: ROMS ocean model input file   swan_rip_current.in: SWAN wave model input file (example with Hs=1m)   coupling_rip_current.in: model coupling file   rip_current.h: model header file    model_grids_forcing.zip: bathymetry and initial condition files      hbeach_grid_isbathy_2m.nc: ROMS bathymetry input file      hbeach_grid_isbathy_2m.bot: SWAN bathymetry input file      hbeach_grid_isbathy_2m.grd: SWAN grid input file      hbeach_init_isbathy_14_18_17.nc: Initial temperature, cool surf zone dT=-1C case      hbeach_init_isbathy_14_18_19.nc: Initial temperature, warm surf zone dT=+1C case      hbeach_init_isbathy_14_18_16.nc: Initial temperature, cool surf zone dT=-2C case      hbeach_init_isbathy_14_18_20.nc: Initial temperature, warm surf zone dT=+2C case      hbeach_init_isbathy_14_18_17p5.nc: Initial temperature, cool surf zone dT=-0.5C case      hbeach_init_isbathy_14_18_18p5.nc: Initial temperature, warm surf zone dT=+0.5C case</p> model_output files: model output used to produce the figures      netcdf files, zipped      variables included:           x_rho (cross-shore coordinate, m)           y_rho (alongshore coordinate, m)           z_rho (vertical coordinate, m)           ocean_time (time since initialization, s, output every 5 mins)           h (bathymetry, m)           temp (temperature, Celsius)           dye_02 (surfzone-released dye)           Hwave (wave height, m)           Dissip_break (wave dissipation W/m2)            ubar (cross-shore depth-average velocity, m/s, interpolated to rho-points)      Case_141817.nc: cool surf zone dT=-1C Hs=1m      Case_141819.nc: warm surf zone dT=+1C Hs=1m      Case_141816.nc: cool surf zone dT=-2C Hs=1m      Case_141820.nc: warm surf zone dT=-2C Hs=1m      Case_141817p5.nc: cool surf zone dT=-0.5C Hs=1m      Case_141818p5.nc: warm surf zone dT=+0.5C Hs=1m      Case_141817_Hp5.nc: cool surf zone dT=-1C Hs=0.5m      Case_141819_Hp5.nc: warm surf zone dT=+1C Hs=0.5m      Case_141817_Hp75.nc: cool surf zone dT=-1C Hs=0.75m      Case_141819_Hp75.nc: warm surf zone dT=+1C Hs=0.75m</p> COAWST is an open source code and can be download at https://coawstmodel-trac.sourcerepo.com/coawstmodel_COAWST/. Descriptions of the input and output files can be found in the manual distributed with the model code and in the glossary at the end of the ocean.in file.</p> Corresponding author: Melissa Moulton, mmoulton@uw.edu</p> 

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