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12. Chiral Tetramer Molecular Model

Author(s):
Petsev, Nikolai D.; Stillinger, Frank H.; Debenedetti, Pablo G.
Abstract:
Source code for our energy-conserving reformulation of the 4-site molecular model for chiral phenomena originally introduced by Latinwo et al. [F. Latinwo, F. H. Stillinger, and P. G. Debenedetti, Molecular Model for Chirality Phenomena, J. Chem. Phys. 145, 154503 (2016)]. The reformulation includes an additional 8-body force that arises from an explicit configuration-dependent term in the potential energy function, resulting in a coarse-grained energy-conserving force field for molecular dynamics simulations of chirality phenomena. In this model, the coarse-grained interaction energy between two tetramers depends on their respective chiralities, and is controlled by a parameter λ, where favors local configurations involving tetramers of opposite chirality, and gives energetic preference to configurations involving tetramers of the same chirality. The source code is for use with the LAMMPS simulation package.
Type:
Software
Issue Date:
3 August 2021

13. Genetic Algorithm Crystal Optimization in a Chiral Tetramer Model System

Author(s):
Petsev, Nikolai D.; Nikoubashman, Arash; Latinwo, Folarin
Abstract:
Source code for our genetic algorithm optimization investigation of conglomerate and racemic chiral crystals. In this work, we address challenges in determining the stable structures formed by chiral molecules by applying the framework of genetic algorithms to predict the ground state crystal lattices formed by a chiral tetramer model. Using this code, we explore the relative stability and structures of the model’s conglomerate and racemic crystals, and extract a structural phase diagram for the stable Bravais crystal types in the zero-temperature limit.
Type:
Software
Issue Date:
8 August 2022

15. Data for "Stability and Kinetics of Iron-Terephthalate MOFs with Diverse Structures in Aqueous Pollutant Degradation"

Author(s):
Sarazen, Michele; Moore, Samuel
Abstract:
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.
Type:
Dataset
Issue Date:
19 June 2023

16. Data for Coarse-grained Intrinsically Disordered Proteins

Author(s):
Webb, Michael; Patel, Roshan; Borca, Carlos
Abstract:
This distribution compiles numerous physical properties for 2,585 intrinsically disordered proteins (IDPs) obtained by coarse-grained molecular dynamics simulation. This combination comprises "Dataset A" as reported in "Featurization strategies for polymer sequence or composition design by machine learning" by Roshan A. Patel, Carlos H. Borca, and Michael A. Webb (DOI: 10.1039/D1ME00160D). The specific IDP sequences are sourced from version 9.0 of the DisProt database. 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.
Type:
Dataset
Issue Date:
6 May 2022

17. Data for Configurations and Characteristics of Simulated Single-Chain Nanoparticles

Author(s):
Webb, Michael; Patel, Roshan; Colmenares, Sophia
Abstract:
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.
Type:
Dataset
Issue Date:
2 June 2023

18. Data on Enzyme Activity Retention in glucose oxidase, lipase, and horseradish peroxidase

Author(s):
Webb, Michael; Patel, Roshan; Gormley, Adam; Tamasi, Matt; Borca, Carlos; Kosuri, Shashank
Abstract:
This distribution contains experimentally measured data for the extent of retained enzyme activity post thermal stressing for three distinct enzymes: glucose oxidase, lipase, and horseradish peroxidase. The data is used to form conclusions and develop machine learning models as reported in the publication "Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids" by Matthew Tamasi, Roshan Patel, Carlos Borca, Shashank Kosuri, Heloise Mugnier, Rahul Upadhya, N. Sanjeeva Murthy, Michael Webb*, and Adam Gormley. Details regarding the experimental protocols are reported in the aforementioned paper but are briefly discussed in the README.
Type:
Dataset
Issue Date:
17 May 2022

19. Thermodynamic and Dynamics Data for Coarse-grained Intrinsically Disordered Proteins Generated by Active Learning

Author(s):
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).
Type:
Dataset
Issue Date:
6 June 2023

20. Molecular Dynamics Trajectories and Analysis Code Demonstrating Liquid-Liquid Criticality in the WAIL Water Model

Author(s):
Weis, Jack
Abstract:
These GROMACS trajectories show the existence of a critical point in deeply supercooled WAIL water. Also included is the code necessary to reproduce the figures in the corresponding paper from these trajectories. From this data the critical temperature, pressure, and density of the model can be found, and critical fluctuations in the deeply supercooled liquid can be directly observed (in a computer-simulation sense).
Type:
Dataset
Issue Date:
13 May 2022

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