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Glassy dynamics and memory effects in an intrinsically disordered protein construct

Citation

Morgan, Ian et al. (2021), Glassy dynamics and memory effects in an intrinsically disordered protein construct, Dryad, Dataset, https://doi.org/10.25349/D9RC86

Abstract

Glassy, nonexponential relaxations in globular proteins are typically attributed to conformational behaviors that are missing from intrinsically disordered proteins. Yet, we show that single molecules of a disordered-protein construct display two signatures of glassy dynamics, logarithmic relaxations and a Kovacs memory effect, in response to changes in applied tension. We attribute this to the presence of multiple independent local structures in the chain, which we corroborate with a model that correctly predicts the force-dependence of the relaxation. The mechanism established here likely applies to other disordered proteins.

Methods

The data were collected using a custom-built magnetic tweezer as described in Ribeck et al. (2008) https://doi.org/10.1063/1.2981687. The force on each polymer was determined as described in Lansdorp et al. (2012) https://doi.org/10.1063/1.3687431.

Usage Notes

GENERAL INFORMATION

1. Title of Dataset: Glassy dynamics in an IDP constuct dataset

2. Author Information
    A. Researcher Contact Information
        Name: Ian L. Morgan
        Institution: University of California, Santa Barbara
        Email: ilmorgan@ucsb.edu

    B. Principal Investigator Contact Information
        Name: Omar A. Saleh
        Institution: University of California, Santa Barbara 
        Email: saleh@ucsb.edu

3. Information about funding sources that supported the collection of the data: 
    This work was supported by the National Science Foundation under Award 1715627. 

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data: 
    The data were collected using a custom-built magnetic tweezer as described in Ribeck et al. (2008) https://doi.org/10.1063/1.2981687. The force on each polymer was determined as described in Lansdorp et al. (2012) https://doi.org/10.1063/1.3687431.

2. Instrument- or software-specific information needed to interpret the data: 
    Data were analyzed using python 3.7 with the following packages:
        numpy
        scipy
        pandas
        matplotlib
        pathlib
        uncertainties
        re
        os
        datetime

3. Environmental/experimental conditions: 
    All data were collected at T = 20 C in a 20mM pH 7 MES buffer with 10mM NaCl and 0.05% Tween-20.    

FOLDERS/FILES
    data 
        Relaxationddata_04_20_20
        supp_kovacs_data
        Fig1cdata.csv
        Fig2bdata.csv
        Relaxationfits_04-20-2020.csv
        Fig3data_04-22-2020.csv
        Fig3_bootstrap_results_04-20-2020
        WLC_FJC_elasticity_fits_04-27-2020
    analysis
        Fig3_bootstrap.py
        Fig3data.py
        fit_relaxations.py
        WLC-FJC_elasticity_fits.py 
    functions 
        __init__.py
        fitting.py
        utilities.py
    plotting
        __init__.py
        default.mplstyle
        Fig1c_plot.py
        Fig2b_plot.py
        Fig3_plot.py
        Fig3_inset_plot.py
        SIFig7_scaling_plot.py

DATA-SPECIFIC INFORMATION FOR: Relaxationdata_04_20_20
    Files:
        NFL_logrelax_polyx.csv
        where x denotes the polymer number from 0-15

    Description:
        Relaxation data for 16 polymers at high force (f1) and low force (f2)

    Variables:
        relaxation - index marking each seperate trace
        time_s - time in seconds since reaching constant force
        mp_mm - magnetic position in mms 
        length_nm - polymer length in nms
        f_pN - force in pN on polymer/bead

DATA-SPECIFIC INFORMATION FOR: supp_kovacs_data
    Files:
        NFL_kovacs_forces.csv
        NFL_kovacs_polyx.csv
        where x denotes the polymer number from 0-7

    Description:
        Supplementary kovacs data for 8 polymers at intermediate force (f3)

    Variables:
        NFL_kovacs_forces.csv
            polymer - index indicating the polymer
            f1_pN - high force (f1) value
            f2_pN - low force (f2) value
            f3_pN - intermediate force (f2) value 

        NFL_kovacs_polyx.csv
            time_s - time in seconds since reaching constant force
            length_nm - polymer length in nms

DATA-SPECIFIC INFORMATION FOR: Fig1cdata.csv
    Description:
        Example relaxation data at various quench (f2) forces

    Variables:
        time_s - time in seconds since reaching constant force
        length_um - polymer length in microns

DATA-SPECIFIC INFORMATION FOR: Fig2bdata.csv
    Description:
        Example kovacs data at intermediate (f3) force

    Variables:
        time_s - time in seconds since reaching constant force
        length_um - polymer length in microns

FILE-SPECIFIC INFORMATION FOR: Fig3_bootstrap.py
    Description:
        Bootstraps fits for Fig3 data (normalized w/ worm-like chain elasticity)
        by polymer
        
FILE-SPECIFIC INFORMATION FOR: Fig3data.py
    Description:
        Loads logarithmic fits of relaxation data and calculates information
        for Fig. 3 data, e.g., fbar and bbar. Outputs Fig3data_x.csv file
        with x as the current date
        
FILE-SPECIFIC INFORMATION FOR: fit_relaxations.py
    Description:
        Performs logarithmic fits of relaxation data and outputs
        Relaxationfits_x.csv with x as current date.

FILE-SPECIFIC INFORMATION FOR: WLC-FJC_elasticity_fits.py
    Description:
        Performs bbar normalization with a more nuanced model that accounts for
        the elasticity of both the coil and structured state.
        Outputs WLC_FJC_elasticity_fits_x.csv with x as current date.

FOLDER-SPECIFIC INFORMATION FOR: fitting
    Description:
        Includes the scripts and style file to produce the major plots in the paper.

Funding

National Science Foundation, Award: 1715627