This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
This repository contains the raw photon-by-photon single-molecule FRET (smFRET) trajectories, SAXS data, and MD simulation trajectories, multi-sequence alignment, and gel images for the paper titled "Sub-Domain Dynamics Enables Chemical Chain Reactions in Nonribosomal Peptide Synthetases."
Large-eddy simulations were employed over five different sea ice patterns, with a constant ice fraction, to test if the overlying atmospheric boundary layer (ABL) dynamics and thermodynamics differs. The results of these simulations were used to determine that there were differences in vertical heat flux, momentum flux, and horizontal wind speed, and that more surface information is needed to predict the ABL over the sea ice surface. To see what other surface information is needed, twenty-two landscape metrics were calculated over forty-four different maps at differing resolutions, using the FRAGSTATs program. The results of that analysis are available in a .csv file in this dataset.
O'Neill, Eric; Lark, Tyler; Xie, Yanhua; Basso, Bruno
Abstract:
Collection of the underlying spatially explicit data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison. Includes raw biomass yield data and soil carbon sequestration potential data for three types of marginal land for the USA midwest at the field level including field areas. Collection also includes raw land rasters for the three types of marginal land, model parameters for the MILP model used in the study, and results used to generate the figures in the paper.
Numerical data is tabulated for all plots (Figures 2, 3a-b, 4-89, S1, S4a-b,d, S5a-b,d, S6-S156) and included as separate spreadsheets categorized by figure in a .zip file in the Supplementary Material. Error bars in Figure 4 show the spread of data observed for 4 and 5 trials on independent samples for MIL-101 and MOF-235, respectively. Figure 6a shows the average of triplicate filtrate test conversions with error propagated based on this spread. Figures 6b and S165 error bars on rate constants are determined based on propagated conversion uncertainty for independent trials and extracted standard deviations of pseudo-first order rate constants from linearized plots. Error bars on other plots represent propagation of experimental uncertainty on single trials.
Chronic hepatitis B (CHB), caused by hepatitis B virus (HBV), remains a major medical problem. HBV has a high propensity for progressing to chronicity and can result in severe liver disease, including fibrosis, cirrhosis and hepatocellular carcinoma. CHB patients frequently present with viral coinfection, including HIV and hepatitis delta virus. About 10% of chronic HIV carriers are also persistently infected with HBV which can result in more exacerbated liver disease. Mechanistic studies of HBV-induced immune responses and pathogenesis, which could be significantly influenced by HIV infection, have been hampered by the scarcity of immunocompetent animal models. Here, we demonstrate that humanized mice dually engrafted with components of a human immune system and a human liver supported HBV infection, which was partially controlled by human immune cells, as evidenced by lower levels of serum viremia and HBV replication intermediates in the liver. HBV infection resulted in priming and expansion of human HLA-restricted CD8+ T cells, which acquired an activated phenotype. Notably, our dually humanized mice support persistent coinfections with HBV and HIV which opens opportunities for analyzing immune dysregulation during HBV and HIV coinfection and preclinical testing of novel immunotherapeutics.
Webb, Michael; Jacobs, William; An, Yaxin; Oliver, Wesley
Abstract:
This distribution compiles thermodynamic and (where available) dynamic properties of short protein sequences as obtained from coarse-grained molecular dynamics simulations. The dataset features 2114 protein sequences with sequence lengths ranging from N=20 up to N=50 amino acids. The simulation and analysis of these sequences is described in "Active learning of the thermodynamics--dynamics tradeoff in protein condensates'' by Yaxin An, Michael A. Webb*, and William M. Jacobs* (https://doi.org/10.48550/arXiv.2306.03696). Of the 2114 protein sequences, 80 are homomeric polypeptides (replicating a single amino acid for N = 20, 30, 40, and 50), 1266 are sourced from version 9.0 of the DisProt database, and the remaining 768 sequences are novel sequences generated during an active learning campaign described in the aforementioned manuscript. The simulations were performed using the LAMMPS molecular dynamics engine. The interactions used for simulation are obtained from R. M. Regy , J. Thompson , Y. C. Kim and J. Mittal , Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins, Protein Sci., 2021, 1371 —1379. Properties included in this distribution include second virial coefficients, pressure-density data, expectation for phase behavior at 300 K, estimated condensed-phase densities at 300 K (if exist), and condensed-phase self-diffusion coefficients at 300 K (if exist).
This item provides access to all configurations of single-chain nanoparticles analyzed in the manuscript "Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning" by Roshan A. Patel, Sophia Colmenares, and Michael A. Webb (DOI: 10.1021/acspolymersau.3c00007). The single-chain nanoparticles derive from 320 unique precursor chains that are distinguished by the fraction of linker beads that decorate a fixed-length polymer backbone and the distribution or blockiness of those linker beads. The data is provided in the form of serialized object using the `pickle' python module. The data was compiled using Python version 3.8.8 and Clang 10.0.0. The Python object loaded from the .pkl file is a nested list, with the first dimension having 7,680 entries for the 7,680 unique single-chain nanoparticles produced in the aforementioned paper. Each of those 7,680 entries is itself a list with 20 entries, representing the 20 different simulation snapshots of the given single-chain nanoparticle. Each of the 20 entries is another list with two entries, with the first being a numpy.ndarray containing the x,y,z coordinates of all the beads comprising the single-chain nanoparticle and the second being a numpy.ndarray with a numerical encoding to indicate whether the beads are backbone (indicated as '0') or linker beads (indicated as '1'). Altogether, this provides 153,600 configurations of single-chain nanoparticles.
