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1. Rapid and label-free Listeria monocytogenes detection based on stimuli-responsive alginate-platinum thiomer nanobrushes

   https://doi.org/10.1038/s41598-022-25753-7  

   Oliveira, Daniela A. ; McLamore, Eric S. ; Gomes, Carmen L. ( December 2022 , Scientific Reports)

   In this work, we demonstrate the development of a rapid and label-free electrochemical biosensor to detectListeria monocytogenesusing a novel stimulus–response thiomer nanobrush material. Nanobrushes were developed via one-step simultaneous co-deposition of nanoplatinum (Pt) and alginate thiomers (ALG-thiomer). ALG-thiomer/Pt nanobrush platform significantly increased the average electroactive surface area of electrodes by 7 folds and maintained the actuation properties (pH-stimulated osmotic swelling) of the alginate. Dielectric behavior during brush actuation was characterized with positively, neutral, and negatively charged redox probes above and below the isoelectric point of alginate, indicating ALG-thiomer surface charge plays an important role in signal acquisition. The ALG-thiomer platform was biofunctionalized with an aptamer selective for the internalin A protein onListeriafor biosensing applications. Aptamer loading was optimized and various cell capture strategies were investigated (brush extended versus collapsed). Maximum cell capture occurs when the ALG-thiomer/aptamer is in the extended conformation (pH > 3.5), followed by impedance measurement in the collapsed conformation (pH < 3.5). Low concentrations of bacteria (5 CFU mL⁻¹) were sensed from a complex food matrix (chicken broth) and selectivity testing against other Gram-positive bacteria (Staphylococcus aureus) indicate the aptamer affinity is maintained, even at these pH values. The new hybrid soft material is among the most efficient and fastest (17 min) forL.more »monocytogenesbiosensing to date, and does not require sample pretreatment, constituting a promising new material platform for sensing small molecules or cells.

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2. Fragile protein folds: sequence and environmental factors affecting the equilibrium of two interconverting, stably folded protein conformations

   https://doi.org/10.5194/mr-2-63-2021  

   Xu, Xingjian ; Dikiy, Igor ; Evans, Matthew R. ; Marcelino, Leandro P. ; Gardner, Kevin H. ( January 2021 , Magnetic Resonance)

   Abstract. Recent research on fold-switching metamorphic proteins has revealed some notable exceptions to Anfinsen's hypothesis of protein folding. We have previously described how a single point mutation can enable a well-folded protein domain, one of the two PAS (Per-ARNT-Sim) domains of the human ARNT (aryl hydrocarbon receptor nuclear translocator) protein, to interconvert between two conformers related by a slip of an internal β strand. Using this protein as a test case, we advance the concept of a “fragile fold”, a protein fold that can reversibly rearrange into another fold that differs by a substantial number of hydrogen bonds, entailing reorganization of single secondary structure elements to more drastic changes seen in metamorphic proteins. Here we use a battery of biophysical tests to examine several factors affecting the equilibrium between the two conformations of the switching ARNT PAS-B Y456T protein. Of note is that we find that factors which impact the HI loop preceding the shifted Iβ strand affect both the equilibrium levels of the two conformers and the denatured state which links them in the interconversion process. Finally, we describe small molecules that selectively bind to and stabilize the wild-type conformation of ARNT PAS-B. These studies form a toolkit for studying fragile proteinmore »folds and could enable ways to modulate the biological functions of such fragile folds, both in natural and engineered proteins.« less
3. Vectorial folding of telomere overhang promotes higher accessibility

   https://doi.org/10.1093/nar/gkac401  

   Paul, Tapas ; Opresko, Patricia L. ; Ha, Taekjip ; Myong, Sua ( June 2022 , Nucleic Acids Research)

   Human telomere overhang composed of tandem repeats of TTAGGG folds into G-quadruplex (G4). Unlike in an experimental setting in the test tube in which the entire length is allowed to fold at once, inside the cell, the overhang is expected to fold as it is synthesized directionally (5′ to 3′) and released segmentally by a specialized enzyme, the telomerase. To mimic such vectorial G4 folding process, we employed a superhelicase, Rep-X which can unwind DNA to release the TTAGGG repeats in 5′ to 3′ direction. We demonstrate that the folded conformation achieved by the refolding of full sequence is significantly different from that of the vectorial folding for two to eight TTAGGG repeats. Strikingly, the vectorially folded state leads to a remarkably higher accessibility to complementary C-rich strand and the telomere binding protein POT1, reflecting a less stably folded state resulting from the vectorial folding. Importantly, our study points to an inherent difference between the co-polymerizing and post-polymerized folding of telomere overhang that can impact telomere architecture and downstream processes.
4. 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 )

   Abstract

   <p>This data set for the manuscript entitled &#34;Design of Peptides that Fold and Self-Assemble on Graphite&#34; 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.</p> <p>Version: 2.0</p> <p>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.</p> <p><br /> Conventions Used in These Files<br /> &#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;</p> <p>Structure Files<br /> ----------------<br /> - 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.)</p> <p>- graph_*.pdb or sol_*.pdb (initial coordinates before equilibration)<br /> - repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl)<br /> - 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)<br /> - amber_*.prmtop (combined topology and parameter files for Amber force field simulations)<br /> - repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)</p> <p>Force Field Parameters<br /> ----------------------<br /> CHARMM format parameter files:<br /> - par_all36m_prot.prm (CHARMM36m FF for proteins)<br /> - 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.<br /> - toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included and others commented out)</p> <p>Template NAMD Configuration Files<br /> ---------------------------------<br /> These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory):<br /> - template_min.namd (minimization)<br /> - template_eq.namd (NPT equilibration with lower graphene fixed)<br /> - template_abf.namd (for adaptive biasing force)</p> <p>Minimization<br /> -------------<br /> - namd/min_*.0.namd</p> <p>Equilibration<br /> -------------<br /> - namd/eq_*.0.namd</p> <p>Adaptive biasing force calculations<br /> -----------------------------------<br /> - namd/eabfZRest7_graph_chp1404.0.namd<br /> - namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)</p> <p>Log Files<br /> ---------<br /> 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.</p> <p>Simulation Output<br /> -----------------<br /> 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 &#61; 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.</p> <p>Scripts<br /> -------<br /> 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.</p> <p><br /> CONTENTS<br /> &#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;</p> <p>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.</p> <p>Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.</p> <p>Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.</p> <p>Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.</p> <p>Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.</p> <p>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.</p> <p>Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).</p> <p>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 (&#34;doRestraintEnergyError.sh&#34;) 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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p> </p>
   

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   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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5. Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions

   https://doi.org/10.1038/s41467-021-25316-w  

   Mortuza, S. M. ; Zheng, Wei ; Zhang, Chengxin ; Li, Yang ; Pearce, Robin ; Zhang, Yang ( August 2021 , Nature Communications)

   Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions.