skip to main content


The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 5:00 PM ET until 11:00 PM ET on Friday, June 21 due to maintenance. We apologize for the inconvenience.

Title: Computational design of a cyclic peptide that inhibits the CTLA4 immune checkpoint
Proteins involved in immune checkpoint pathways, such as CTLA4, PD1, and PD-L1, have become important targets for cancer immunotherapy; however, development of small molecule drugs targeting these pathways has proven difficult due to the nature of their protein–protein interfaces. Here, using a hierarchy of computational techniques, we design a cyclic peptide that binds CTLA4 and follow this with experimental verification of binding and biological activity, using bio-layer interferometry, cell culture, and a mouse tumor model. Beginning from a template excised from the X-ray structure of the CTLA4:B7-2 complex, we generate several peptide sequences using flexible docking and modeling steps. These peptides are cyclized head-to-tail to improve structural and proteolytic stability and screened using molecular dynamics simulation and MM-GBSA calculation. The standard binding free energies for shortlisted peptides are then calculated in explicit-solvent simulation using a rigorous multistep technique. The most promising peptide, cyc(EIDTVLTPTGWVAKRYS), yields the standard free energy −6.6 ± 3.5 kcal mol^−1, which corresponds to a dissociation constant of ∼15 μmol L^−1. The binding affinity of this peptide for CTLA4 is measured experimentally (31 ± 4 μmol L^−1) using bio-layer interferometry. Treatment with this peptide inhibited tumor growth in a co-culture of Lewis lung carcinoma (LLC) cells and antigen primed T cells, as well as in mice with an orthotropic Lewis lung carcinoma allograft model.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
RSC Medicinal Chemistry
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Currently, there is a great interest in nanoparticle-based vaccine delivery. Recent studies suggest that nanoparticles when introduced into the biological milieu are not simply passive carriers but may also contribute immunological activity themselves or of their own accord. For example there is considerable interest in the biomedical applications of one of the physiologically-based inorganic metal oxide nanoparticle, zinc oxide (ZnO). Indeed zinc oxide (ZnO) NP are now recognized as a nanoscale chemotherapeutic or anticancer nanoparticle (ANP) and several recent reports suggest ZnO NP and/or its complexes with drug and RNA induce a potent antitumor response in immuno-competent mouse models. A variety of cell culture studies have shown that ZnO NP can induce cytokines such as IFN-γ, TNF-α, IL-2, and IL-12 which are known to regulate the tumor microenvironment. Much less work has been done on magnesium oxide (MgO), cobalt oxide (Co3O4), or nickel oxide (NiO); however, despite the fact that these physiologically-based metal oxide NP are reported to functionally load and assemble RNA and protein onto their surface and may thus also be of potential interest as nanovaccine platform. Here we initially compared in vitro immunogenicity of ZnO and Co3O4 NP and their effects on cancer-associated or tolerogenic cytokines. Based on these data we moved ZnO NP forward to testing in the ex vivo splenocyte assay relative to MgO and NiO NP and these data showed significant difference for flow cytometry sorted population for ZnO-NP, relative to NiO and MgO. These data suggesting both molecular and cellular immunogenic activity, a double-stranded anticancer RNA (ACR), polyinosinic:poly cytidylic acid (poly I:C) known to bind ZnO NP; when ZnO-poly I:C was injected into B16F10-BALB/C tumor significantly induced, IL-2 and IL-12 as shown by Cohen’s d test. LL37 is an anticancer peptide (ACP) currently in clinical trials as an intratumoral immuno-therapeutic agent against metastatic melanoma. LL37 is known to bind poly I:C where it is thought to compete for receptor binding on the surface of some immune cells, metastatic melanoma and lung cells. Molecular dynamic simulations revealed association of LL37 onto ZnO NP confirmed by gel shift assay. Thus using the well-characterized model human lung cancer model cell line (BEAS-2B), poly I:C RNA, LL37 peptide, or LL37-poly I:C complexes were loaded onto ZnO NP and delivered to BEAS-2B lung cells, and the effect on the main cancer regulating cytokine, IL-6 determined by ELISA. Surprisingly ZnO-LL37, but not ZnO-poly I:C or the more novel tricomplex (ZnO-LL37-poly I:C) significantly suppressed IL-6 by >98–99%. These data support the further evaluation of physiological metal oxide compositions, so-called physiometacomposite (PMC) materials and their formulation with anticancer peptide (ACP) and/or anticancer RNA (ACR) as a potential new class of immuno-therapeutic against melanoma and potentially lung carcinoma or other cancers. 
    more » « less
  2. Age is a leading risk factor for developing breast cancer. This may be in part to the time required for acquiring sufficient cancer mutations; however, stromal cells that accumulate in tissues and undergo senescence eventually develop a senescence-associated secretory phenotype that alters the microenvironment to promote cancer. Our focus is on mesenchymal stem cells (MSCs) – stromal cells recruited to tumors due to their natural tropism for inflammatory tissues; MSCs have been shown to enhance the metastatic potential of tumor cells through direct interactions or paracrine signaling within the tumor. In the tumor, MSCs can differentiate into carcinoma-associated fibroblasts that play a central role in tumor growth and matrix remodeling. We recently investigated the molecular and mechanical differences in pre- and post- senescent MSCs and how their interactions with MDA-MB-231 breast cancer cells contribute to malignancy. Our data show post-senescent MSCs are larger and less motile, with more homogeneous mechanical properties than pre-senescent MSCs. In-depth omics analysis revealed differentially regulated genes and peptides including factors related to inflammatory cytokines, cell adhesion to the extracellular matrix, and cytoskeletal regulation. A 3D co-culture model was used to assess the effects of pre- and post- senescent MSCs on collagen matrix remodeling. Although post-senescent MSCs were far less motile than pre-senescent MSCs and less contractile with the matrix, they profoundly altered matrix protein deposition and crosslinking, which resulted in local matrix stiffening effects. Post-senescent MSCs also induced an invasive breast cancer cell phenotype, characterized by increased proliferation and invasion of breast cancer cells. This invasive breast cancer cell behavior was further amplified when MDA-MB-231 was co-cultured with a mixture of pre- and post- senescent MSCs; this result was attributed to matrix remodeling and soluble factor secretion effects of post-senescent MSCs, which enhanced the migration of pre-senescent MSCs allowing them to form tracks in the collagen network for cancer cells to follow. Finally, molecular inhibitors targeting actomyosin contractility and adhesion were used to alter MSC interactions with breast cancer cells. Actin depolymerizing agent and focal adhesion kinase inhibitor were most efficient and completely able to block the effects of post-senescent MSCs on MDA-MB-231 invasion in collagen gels. This comprehensive approach can be used to identify molecular pathways regulating heterotypic interactions of post-senescent MSCs with other cells in the tumor. Furthermore, the local matrix stiffening effect of post-senescent MSCs may play a critical role in breast cancer progression. 
    more » « less
  3. Abstract Background Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy. Redox factor-1 (Ref-1), a redox signaling protein, regulates the conversion of several transcription factors (TFs), including HIF-1α, STAT3 and NFκB from an oxidized to reduced state leading to enhancement of their DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia. Methods scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model and validated using proteomics and qRT-PCR. The identified Ref-1’s role in mitochondrial function was confirmed using mitochondrial function assays, qRT-PCR, western blotting and NADP assay. Further, the effect of Ref-1 redox function inhibition against pancreatic cancer metabolism was assayed using 3D co-culture in vitro and xenograft studies in vivo. Results Distinct transcriptional variation in central metabolism, cell cycle, apoptosis, immune response, and genes downstream of a series of signaling pathways and transcriptional regulatory factors were identified in Ref-1 knockdown vs Scrambled control from the scRNA-seq data. Mitochondrial DEG subsets downregulated with Ref-1 knockdown were significantly reduced following Ref-1 redox inhibition and more dramatically in combination with Devimistat in vitro. Mitochondrial function assays demonstrated that Ref-1 knockdown and Ref-1 redox signaling inhibition decreased utilization of TCA cycle substrates and slowed the growth of pancreatic cancer co-culture spheroids. In Ref-1 knockdown cells, a higher flux rate of NADP + consuming reactions was observed suggesting the less availability of NADP + and a higher level of oxidative stress in these cells. In vivo xenograft studies demonstrated that tumor reduction was potent with Ref-1 redox inhibitor similar to Devimistat. Conclusion Ref-1 redox signaling inhibition conclusively alters cancer cell metabolism by causing TCA cycle dysfunction while also reducing the pancreatic tumor growth in vitro as well as in vivo. 
    more » « less
  4. The antitumor effects of a partially purified water extract from Euglena gracilis (EWE) and EWE treated by boiling (bEWE) were evaluated using orthotopic lung cancer syngeneic mouse models with Lewis lung carcinoma (LLC) cells. Daily oral administration of either EWE or bEWE started three weeks prior to the inoculation of LLC cells significantly attenuated tumor growth as compared to the phosphate buffered saline (PBS) control, and the attenuation was further enhanced by bEWE. The intestinal microbiota compositions in both extract-treated groups were more diverse than that in the PBS group. Particularly, a decrease in the ratio of Firmicutes to Bacteroidetes and significant increases in Akkermansia and Muribaculum were observed in two types of EWE-treated groups. Fecal microbiota transplantation (FMT) using bEWE-treated mouse feces attenuated tumor growth to an extent equivalent to bEWE treatment, while tumor growth attenuation by bEWE was abolished by treatment with an antibiotic cocktail. These studies strongly suggest that daily oral administration of partially purified water extracts from Euglena gracilis attenuates lung carcinoma growth via the alteration of the intestinal microbiota. 
    more » « less
  5. 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)

    - namd/min_*.0.namd

    - 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.

    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.


    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 ("") 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