skip to main content

Explore Research Products in the NSF-PAR

Title: Protein unfolding by SDS: the microscopic mechanisms and the properties of the SDS-protein assembly
The effects of detergent sodium dodecyl sulfate (SDS) on protein structure and dynamics are fundamental to the most common laboratory technique used to separate proteins and determine their molecular weights: polyacrylamide gel electrophoresis. However, the mechanism by which SDS induces protein unfolding and the microstructure of protein–SDS complexes remain largely unknown. Here, we report a detailed account of SDS-induced unfolding of two proteins—I27 domain of titin and β-amylase—obtained through all-atom molecular dynamics simulations. Both proteins were found to spontaneously unfold in the presence of SDS at boiling water temperature on the time scale of several microseconds. The protein unfolding was found to occur via two distinct mechanisms in which specific interactions of individual SDS molecules disrupt the protein's secondary structure. In the final state of the unfolding process, the proteins are found to wrap around SDS micelles in a fluid necklace-and-beads configuration, where the number and location of bound micelles changes dynamically. The global conformation of the protein was found to correlate with the number of SDS micelles bound to it, whereas the number of SDS molecules directly bound to the protein was found to define the relaxation time scale of the unfolded protein. Our microscopic characterization of SDS–protein interactions sets the stage for future refinement of SDS–enabled protein characterization methods, including protein fingerprinting and sequencing using a solid-state nanopore.  more » « less
Award ID(s):
1430124 1827346
NSF-PAR ID:
10170291
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Nanoscale
Volume:
12
Issue:
9
ISSN:
2040-3364
Page Range / eLocation ID:
5422 to 5434
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ( , Protein Science)
    Abstract

    Structural information of protein–protein interactions is essential for characterization of life processes at the molecular level. While a small fraction of known protein interactions has experimentally determined structures, computational modeling of protein complexes (protein docking) has to fill the gap. TheDockgroundresource (http://dockground.compbio.ku.edu) provides a collection of datasets for the development and testing of protein docking techniques. Currently,Dockgroundcontains datasets for the bound and the unbound (experimentally determined and simulated) protein structures, model–model complexes, docking decoys of experimentally determined and modeled proteins, and templates for comparative docking. TheDockgroundbound proteins dataset is a core set, from which otherDockgrounddatasets are generated. It is devised as a relational PostgreSQL database containing information on experimentally determined protein–protein complexes. This report on theDockgroundresource describes current status of the datasets, new automated update procedures and further development of the core datasets. We also present a newDockgroundinteractive web interface, which allows search by various parameters, such as release date, multimeric state, complex type, structure resolution, and so on, visualization of the search results with a number of customizable parameters, as well as downloadable datasets with predefined levels of sequence and structure redundancy.

     
    more » « less
  2. ; ; ; ; ; ( , Frontiers in Molecular Biosciences)
    The outer membrane of Gram-negative bacteria acts as an additional diffusion barrier for solutes and nutrients. It is perforated by outer membrane proteins (OMPs) that function most often as diffusion pores, but sometimes also as parts of larger cellular transport complexes, structural components of the cell wall, or even as enzymes. These OMPs often have large loops that protrude into the extracellular environment, which have promise for biotechnological applications and as therapeutic targets. Thus, understanding how modifications to these loops affect OMP stability and folding is critical for their efficient application. In this work, the small outer membrane protein OmpX was used as a model system to quantify the effects of loop insertions on OMP folding and stability. The insertions were varied according to both hydrophobicity and size, and their effects were determined by assaying folding into detergent micelles in vitro by SDS-PAGE and in vivo by isolating the outer membrane of cells expressing the constructs. The different insertions were also examined in molecular dynamics simulations to resolve how they affect OmpX dynamics in its native outer membrane. The results indicate that folding of OMPs is affected by both the insert length and by its hydrophobic character. Small insertions sometimes even improved the folding efficiency of OmpX, while large hydrophilic inserts reduced it. All the constructs that were found to fold in vitro could also do so in their native environment. One construct that could not fold in vitro was transported to the OM in vivo , but remained unfolded. Our results will help to improve the design and efficiency of recombinant OMPs used for surface display. 
    more » « less
  3. ; ; ; ( , The Journal of Physical Chemistry B)
    Binding-induced mechanical stabilization plays key roles in proteins involved in muscle contraction, cellular mechanotransduction, or bacterial adhesion. Because of the vector nature of force, single-molecule force spectroscopy techniques are ideal for measuring the mechanical unfolding of proteins. However, current approaches are still prone to calibration errors between experiments and geometrical variations between individual tethers. Here, we introduce a single-molecule assay based on magnetic tweezers and heterocovalent attachment, which can measure the binding of the substrate–ligand using the same protein molecule. We demonstrate this approach with protein L, a model bacterial protein which has two binding interfaces for the same region of kappa-light chain antibody ligands. Engineered molecules with eight identical domains of protein L between a HaloTag and a SpyTag were exposed to repeated unfolding–refolding cycles at forces up to 100 pN for several hours at a time. The unfolding behavior of the same protein was measured in solution buffers with different concentrations of antibody ligands. With increasing antibody concentration, an increasing number of protein L domains became more stable, indicative of ligand binding and mechanical reinforcement. Interestingly, the dissociation constant of the mechanically reinforced states coincides with that measured for the low-avidity binding interface of protein L, suggesting a physiological role for the second binding interface. The molecular approach presented here opens the road to a new type of binding experiments, where the same molecule can be exposed to different solvents or ligands. 
    more » « less
  4. ; ; ; ( , Zenodo)

    This data set for the manuscript entitled "Design of Peptides that Fold and Self-Assemble on Graphite" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and 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 or python scripts. These scripts and their output are also included.

    Version: 2.0

    Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.


    Conventions Used in These Files
    ===============================

    Structure Files
    ----------------
    - graph_*.psf or sol_*.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.)

    - graph_*.pdb or sol_*.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)
    - freeTop_*.pdb (same as the above pdb files, but the carbons of the lower graphene layer have been placed at a single z value and marked for restraints in NAMD)
    - amber_*.prmtop (combined topology and parameter files for Amber force field simulations)
    - repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)

    Force Field Parameters
    ----------------------
    CHARMM format parameter files:
    - par_all36m_prot.prm (CHARMM36m FF for proteins)
    - par_all36_cgenff_no_nbfix.prm (CGenFF v4.4 for graphene) The NBFIX parameters are commented out since they are only needed for aromatic halogens and we use only the CG2R61 type for graphene.
    - toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included 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_eq.namd (NPT equilibration with lower graphene fixed)
    - template_abf.namd (for adaptive biasing force)

    Minimization
    -------------
    - namd/min_*.0.namd

    Equilibration
    -------------
    - namd/eq_*.0.namd

    Adaptive biasing force calculations
    -----------------------------------
    - namd/eabfZRest7_graph_chp1404.0.namd
    - namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)

    Log Files
    ---------
    For each NAMD configuration file given in the last two sections, there is a log file with the same prefix, which gives the text output of NAMD. For instance, the output of namd/eabfZRest7_graph_chp1404.0.namd is eabfZRest7_graph_chp1404.0.log.

    Simulation Output
    -----------------
    The simulation output files (which match the names of the NAMD configuration files) are in the output/ directory. Files with the extensions .coor, .vel, and .xsc are coordinates in NAMD binary format, velocities in NAMD binary format, and extended system information (including cell size) in text format. Files with the extension .dcd give the trajectory of the atomic coorinates over time (and also include system cell information). Due to storage limitations, large DCD files have been omitted or replaced with new DCD files having the prefix stride50_ including only every 50 frames. The time between frames in these files is 50 * 50000 steps/frame * 4 fs/step = 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.

    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. If there are scripts with step1_*.sh and step2_*.sh, they are intended to be run in order, with step1_*.sh first.


    CONTENTS
    ========

    The directory contents are as follows. The directories Sim_Figure-1 and Sim_Figure-8 include README.txt files that describe the files and naming conventions used throughout this data set.

    Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.

    Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.

    Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.

    Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.

    Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.

    Sim_Figure-5_replica: Temperature replica exchange molecular dynamics simulations for the peptide cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) with 20 replicas for temperatures from 295 to 454 K.

    Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).

    Sim_Figure-7: Free energy calculations for folding, adsorption, and pairing for the peptide CHP1404 (sequence: cyc(GTGSGTG-GPGG-GCGTGTG-SGPG)). For folding, we calculate the PMF as function of RMSD by replica-exchange umbrella sampling (in the subdirectory Folding_CHP1404_Graphene/). We make the same calculation in solution, which required 3 seperate replica-exchange umbrella sampling calculations (in the subdirectory Folding_CHP1404_Solution/). Both PMF of RMSD calculations for the scrambled peptide are in Folding_scram1404/. For adsorption, calculation of the PMF for the orientational restraints and the calculation of the PMF along z (the distance between the graphene sheet and the center of mass of the peptide) are in Adsorption_CHP1404/ and Adsorption_scram1404/. The actual calculation of the free energy is done by a shell script ("doRestraintEnergyError.sh") in the 1_free_energy/ subsubdirectory. Processing of the PMFs must be done first in the 0_pmf/ subsubdirectory. Finally, files for free energy calculations of pair formation for CHP1404 are found in the Pair/ subdirectory.

    Sim_Figure-8: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) where the peptides are far above the graphene–water interface in the initial configuration.

    Sim_Figure-9: Two replicates of a simulation of nine peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.

    Sim_Figure-9_scrambled: Two replicates of a simulation of nine peptide molecules with the control sequence cyc(GGTPTTGGGGGGSGGPSGTGGC) at the graphite–water interface at 370 K.

    Sim_Figure-10: Adaptive biasing for calculation of the free energy of the folded peptide as a function of the angle between its long axis and the zigzag directions of the underlying graphene sheet.

     

    This material is based upon work supported by the US National Science Foundation under grant no. DMR-1945589. A majority of the computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CHE-1726332, CNS-1006860, EPS-1006860, and EPS-0919443. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562, through allocation BIO200030. 
    more » « less
  5. ; ; ; ; ; ; ( , ACS Nano)
    null (Ed.)
    ABSTRACT: The dosing of peptide and protein therapeutics is complicated by rapid clearance from the blood pool and poor cellular membrane permeability. Encapsulation into nanocarriers such as liposomes or polymersomes has long been explored to overcome these limitations, but manufacturing challenges have limited clinical translation by these approaches. Recently, inverse Flash NanoPrecipitation (iFNP) has been developed to produce highly loaded polymeric nanocarriers with the peptide or protein contained within a hydrophilic core, stabilized by a hydrophobic polymer shell. Encapsulation of proteins with higher-order structure requires understanding how processing may affect their conformational state. We demonstrate a combined experimental/simulation approach to characterize protein behavior during iFNP processing steps using the Trp-cage protein TC5b as a model. Explicit-solvent fully atomistic molecular dynamics simulations with enhanced sampling techniques are coupled with two-dimensional heteronuclear multiple-quantum coherence nuclear magnetic resonance spectroscopy (2D-HMQC NMR) and circular dichroism to determine the structure of TC5b during mixed-solvent exposure encountered in iFNP processing. The simulations involve atomistic models of mixed solvents and protein to capture the complexity of the hydrogen bonding and hydrophobic interactions between water, dimethylsulfoxide (DMSO), and the protein. The combined analyses reveal structural unfolding of the protein in 11 M DMSO but confirm complete refolding after release from the polymeric nanocarrier back into an aqueous phase. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email