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1. Engineering Next Generation Cyclized Peptide Ligands for Light‐Controlled Capture and Release of Therapeutic Proteins

   https://doi.org/10.1002/adfm.202101410  

   Prodromou, Raphael ; Day, Kevin N. ; Saberi‐Bosari, Sahand ; Schneible, John D. ; Mabe, Matthew D. ; San Miguel, Adriana ; Daniele, Michael A. ; Pozdin, Vladimir ; Menegatti, Stefano ( May 2021 , Advanced Functional Materials)

   Photo‐affinity adsorbents (i.e., translucent matrices functionalized with ligands featuring light‐controlled biorecognition) represent a futuristic technology for purifying labile biologics. In this study, a framework for prototyping photo‐affinity adsorbents comprising azobenzene‐cyclized peptides (ACPs) conjugated to translucent porous beads (ChemMatrix) is presented. This approach combines computational and experimental tools for designing ACPs and investigating their light‐controlled isomerization kinetics and protein biorecognition. First, a modular design for tailoring ACP's conformation, facilitating sequencing, and streamlining the in silico modeling of cis/trans isomers and their differential protein binding is introduced. Then, a spectroscopic system for measuring the photo‐isomerization kinetics of ACPs on ChemMatrix beads is reported; using this device, it is demonstrated that the isomerization at different light intensities is correlated to the cyclization geometry, specifically the energy difference of trans versus cis isomers as calculated in silico. Also, a microfluidic device for sorting ACP‐ChemMatrix beads to select and validate photo‐affinity ligands using Vascular Cell Adhesion Molecule 1 (VCAM‐1) as target protein and cyclo_AZOB[GVHAKQHRN‐K*]‐G‐ChemMatrix as model photo‐affinity adsorbent is presented. The proposed ACPs exhibit rapid and defined light‐controlled isomerization and biorecognition. Controlling the adsorption and release of VCAM‐1 using light demonstrates the potential of photo‐affinity adsorbents for targets whose biochemical liability poses challenges to its purification.

    

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2. Molecular Engineering of Cyclic Azobenzene‐Peptide Hybrid Ligands for the Purification of Human Blood Factor VIII via Photo‐Affinity Chromatography

   https://doi.org/10.1002/adfm.202213881  

   Prodromou, Raphael ; Moore, Brandyn David ; Chu, Wenning ; Deal, Halston ; San Miguel, Adriana ; Brown, Ashley Carson ; Daniele, Michael Angelo‐Anthony ; Pozdin, Vladimir Aleksandrovich ; Menegatti, Stefano ( January 2023 , Advanced Functional Materials)

   The use of benign stimuli to control the binding and release of labile biologics for their isolation from complex feedstocks is a key goal of modern biopharmaceutical technology. This study introduces cyclic azobenzene‐peptide (CAP) ligands for the rapid and discrete photo‐responsive capture and release of blood coagulation factor VIII (FVIII). A predictive method—based on amino acid sequence and molecular architecture of CAPs—is developed to correlate the conformation ofcis/trans‐CAP photo‐isomers to FVIII binding and release. Combined in silico ‐ in vitro analysis of FVIII:peptide interactions guide the design of a rational approach to optimize isomerization kinetics and biorecognition of CAPs. A photoaffinity adsorbent, prepared by conjugating selected CAP G‐cyclo_AZOB[Lys‐YYKHLYN‐Lys]‐G on translucent chromatographic beads, features high binding capacity (>6 mg of FVIII per mL of resin) and rapid photo‐isomerization kinetics (τ < 30 s) when exposed to 420–450 nm light at the intensity of 0.1 W cm⁻². The adsorbent purifies FVIII from a recombinant harvest using a single mobile phase, affording high product yield (>90%), purity (>95%), and blood clotting activity. The CAPs introduced in this report demonstrate a novel route integrating gentle operational conditions in a rapid and efficient bioprocess for the purification of life‐saving biotherapeutics.

    

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3. Click Conjugation of Boron Dipyrromethene (BODIPY) Fluorophores to EGFR-Targeting Linear and Cyclic Peptides

   https://doi.org/10.3390/molecules26030593  

   Williams, Tyrslai M. ; Kaufman, Nichole E. ; Zhou, Zehua ; Singh, Sitanshu S. ; Jois, Seetharama D. ; Vicente, Maria da ( February 2021 , Molecules)

   null (Ed.)

   Through a simple 1,3-cycloaddition reaction, three BODIPY-peptide conjugates that target the extracellular domain of the epidermal growth factor receptor (EGFR) were prepared and their ability for binding to EGFR was investigated. The peptide ligands K(N3)LARLLT and its cyclic analog cyclo(K(N3)larllt, previously shown to have high affinity for binding to the extracellular domain of EGFR, were conjugated to alkynyl-functionalized BODIPY dyes 1 and 2 via a copper-catalyzed click reaction. This reaction produced conjugates 3, 4, and 5 in high yields (70–82%). In vitro studies using human carcinoma HEp2 cells that overexpress EGFR demonstrated high cellular uptake, particularly for the cyclic peptide conjugate 5, and low cytotoxicity in light (~1 J·cm−2) and darkness. Surface plasmon resonance (SPR) results show binding affinity of the three BODIPY-peptide conjugates for EGFR, particularly for 5 bearing the cyclic peptide. Competitive binding studies using three cell lines with different expressions of EGFR show that 5 binds specifically to EGFR-overexpressing colon cancer cells. Among the three conjugates, 5 bearing the cyclic peptide exhibited the highest affinity for binding to the EGFR protein. 

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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 , 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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5. Affibody-Binding Ligands

   https://doi.org/10.3390/ijms21113769  

   Barozzi, Annalisa ; Lavoie, R. Ashton ; Day, Kevin N. ; Prodromou, Raphael ; Menegatti, Stefano ( June 2020 , International Journal of Molecular Sciences)

   null (Ed.)

   While antibodies remain established therapeutic and diagnostic tools, other protein scaffolds are emerging as effective and safer alternatives. Affibodies in particular are a new class of small proteins marketed as bio-analytic reagents. They feature tailorable binding affinity, low immunogenicity, high tissue permeation, and high expression titer in bacterial hosts. This work presents the development of affibody-binding peptides to be utilized as ligands for their purification from bacterial lysates. Affibody-binding candidates were identified by screening a peptide library simultaneously against two model affibodies (anti-immunoglobulin G (IgG) and anti-albumin) with the aim of selecting peptides targeting the conserved domain of affibodies. An ensemble of homologous sequences identified from screening was synthesized on Toyopearl® resin and evaluated via binding studies to select sequences that afford high product binding and recovery. The affibody–peptide interaction was also evaluated by in silico docking, which corroborated the targeting of the conserved domain. Ligand IGKQRI was validated through purification of an anti-ErbB2 affibody from an Escherichia coli lysate. The values of binding capacity (~5 mg affibody per mL of resin), affinity (KD ~1 μM), recovery and purity (64–71% and 86–91%), and resin lifetime (100 cycles) demonstrate that IGKQRI can be employed as ligand in affibody purification processes. 

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