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Glassy dynamics and memory effects in an intrinsically disordered protein construct

Cite this dataset

Morgan, Ian et al. (2021). Glassy dynamics and memory effects in an intrinsically disordered protein construct [Dataset]. Dryad. 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