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1. Simulation Data for Design of Peptides that Fold and Self-Assemble on Graphite

   https://doi.org/10.5281/zenodo.6426152  

   Comer, Jeffrey ; Thakkar, Ravindra ; Velásquez-Silva, Astrid ; Miranda-Carvajal, Ingrid ( January 2022 , 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. 

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2. Free-energy changes of bacteriorhodopsin point mutants measured by single-molecule force spectroscopy

   https://doi.org/10.1073/pnas.2020083118  

   Jacobson, David R. ; Perkins, Thomas T. ( March 2021 , Proceedings of the National Academy of Sciences)

   Single amino acid mutations provide quantitative insight into the energetics that underlie the dynamics and folding of membrane proteins. Chemical denaturation is the most widely used assay and yields the change in unfolding free energy (ΔΔG). It has been applied to >80 different residues of bacteriorhodopsin (bR), a model membrane protein. However, such experiments have several key limitations: 1) a nonnative lipid environment, 2) a denatured state with significant secondary structure, 3) error introduced by extrapolation to zero denaturant, and 4) the requirement of globally reversible refolding. We overcame these limitations by reversibly unfolding local regions of an individual protein with mechanical force using an atomic-force-microscope assay optimized for 2 μs time resolution and 1 pN force stability. In this assay, bR was unfolded from its native bilayer into a well-defined, stretched state. To measure ΔΔG, we introduced two alanine point mutations into an 8-amino-acid region at the C-terminal end of bR’s G helix. For each, we reversibly unfolded and refolded this region hundreds of times while the rest of the protein remained folded. Our single-molecule–derived ΔΔGfor mutant L223A (−2.3 ± 0.6 kcal/mol) quantitatively agreed with past chemical denaturation results while our ΔΔGfor mutant V217A was 2.2-fold larger (−2.4 ± 0.6 kcal/mol). We attribute the latter result, in part, to contact between Val²¹⁷and a natively bound squalene lipid, highlighting the contribution of membrane protein–lipid contacts not present in chemical denaturation assays. More generally, we established a platform for determining ΔΔGfor a fully folded membrane protein embedded in its native bilayer.

    

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3. Characterization of the structural forces governing the reversibility of the thermal unfolding of the human acidic fibroblast growth factor

   https://doi.org/10.1038/s41598-021-95050-2  

   Agrawal, Shilpi ; Govind Kumar, Vivek ; Gundampati, Ravi Kumar ; Moradi, Mahmoud ; Kumar, Thallapuranam Krishnaswamy Suresh ( August 2021 , Scientific Reports)

   Human acidic fibroblast growth factor (hFGF1) is an all beta-sheet protein that is involved in the regulation of key cellular processes including cell proliferation and wound healing. hFGF1 is known to aggregate when subjected to thermal unfolding. In this study, we investigate the equilibrium unfolding of hFGF1 using a wide array of biophysical and biochemical techniques. Systematic analyses of the thermal and chemical denaturation data on hFGF1 variants (Q54P, K126N, R136E, K126N/R136E, Q54P/K126N, Q54P/R136E, and Q54P/K126N/R136E) indicate that nullification of charges in the heparin-binding pocket can significantly increase the stability of wtFGF1. Triple variant (Q54P/K126N/R136E) was found to be the most stable of all the hFGF1 variants studied. With the exception of triple variant, thermal unfolding of wtFGF1 and the other variants is irreversible. Thermally unfolded triple variant refolds completely to its biologically native conformation. Microsecond-level molecular dynamic simulations reveal that a network of hydrogen bonds and salt bridges linked to Q54P, K126N, and R136E mutations, are responsible for the high stability and reversibility of thermal unfolding of the triple variant. In our opinion, the findings of the study provide valuable clues for the rational design of a stable hFGF1 variant that exhibits potent wound healing properties.

    

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4. Protein interactome of 3′,5′‐cAMP reveals its role in regulating the actin cytoskeleton

   https://doi.org/10.1111/tpj.16313  

   Figueroa, Nicolás E. ; Franz, Peter ; Luzarowski, Marcin ; Martinez‐Seidel, Federico ; Moreno, Juan C. ; Childs, Dorothee ; Ziemblicka, Aleksandra ; Sampathkumar, Arun ; Andersen, Tonni Grube ; Tsiavaliaris, Georgios ; et al ( June 2023 , The Plant Journal)

   Identification of protein interactors is ideally suited for the functional characterization of small molecules. 3′,5′‐cAMP is an evolutionary ancient signaling metabolite largely uncharacterized in plants. To tap into the physiological roles of 3′,5′‐cAMP, we used a chemo‐proteomics approach, thermal proteome profiling (TPP), for the unbiased identification of 3′,5′‐cAMP protein targets. TPP measures shifts in the protein thermal stability upon ligand binding. Comprehensive proteomics analysis yielded a list of 51 proteins significantly altered in their thermal stability upon incubation with 3′,5′‐cAMP. The list contained metabolic enzymes, ribosomal subunits, translation initiation factors, and proteins associated with the regulation of plant growth such as CELL DIVISION CYCLE 48. To functionally validate obtained results, we focused on the role of 3′,5′‐cAMP in regulating the actin cytoskeleton suggested by the presence of actin among the 51 identified proteins. 3′,5′‐cAMP supplementation affected actin organization by inducing actin‐bundling. Consistent with these results, the increase in 3′,5′‐cAMP levels, obtained either by feeding or by chemical modulation of 3′,5′‐cAMP metabolism, was sufficient to partially rescue the short hypocotyl phenotype of theactin2 actin7mutant, severely compromised in actin level. The observed rescue was specific to 3′,5′‐cAMP, as demonstrated using a positional isomer 2′,3′‐cAMP, and true for the nanomolar 3′,5′‐cAMP concentrations reported for plant cells.In vitrocharacterization of the 3′,5′‐cAMP–actin pairing argues against a direct interaction between actin and 3′,5′‐cAMP. Alternative mechanisms by which 3′,5′‐cAMP would affect actin dynamics, such as by interfering with calcium signaling, are discussed. In summary, our work provides a specific resource, 3′,5′‐cAMP interactome, as well as functional insight into 3′,5′‐cAMP‐mediated regulation in plants.

    

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5. Protein unfolding by SDS: the microscopic mechanisms and the properties of the SDS-protein assembly

   https://doi.org/10.1039/c9nr09135a  

   Winogradoff, David ; John, Shalini ; Aksimentiev, Aleksei ( March 2020 , Nanoscale)

   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. 

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