Engineered receptors play increasingly important roles in transformative cell-based therapies. However, the structural mechanisms that drive differences in performance across receptor designs are often poorly understood. Recent advances in protein structural prediction tools have enabled the modeling of virtually any user-defined protein, but how these tools might build understanding of engineered receptors has yet to be fully explored. In this study, we employed structural modeling tools to perform post hoc analyses to investigate whether predicted structural features might explain observed functional variation. We selected a recently reported library of receptors derived from natural cytokine receptors as a case study, generated structural models, and from these predictions quantified a set of structural features that plausibly impact receptor performance. Encouragingly, for a subset of receptors, structural features explained considerable variation in performance, and trends were largely conserved across structurally diverse receptor sets. This work indicates potential for structure prediction-guided synthetic receptor engineering.
Get full access to this article
View all access options for this article.
References
1.
JuneCH, O’ConnorRS, KawalekarOU, et al.CAR T cell immunotherapy for human cancer. Science, 2018; 359(6382):1361–1365; doi: 10.1126/science.aar6711
2.
MitraA, BaruaA, HuangL, et al.From bench to bedside: The history and progress of CAR T cell therapy. Front Immunol, 2023; 14:1188049; doi: 10.3389/fimmu.2023.1188049
3.
ChungJB, BrudnoJN, BorieD, et al.Chimeric antigen receptor T cell therapy for autoimmune disease. Nat Rev Immunol, 2024; 24(11):830–845; doi: 10.1038/s41577-024-01035-3
4.
BlacheU, TretbarS, KoehlU, et al.CAR T cells for treating autoimmune diseases. RMD Open, 2023; 9(4); doi: 10.1136/rmdopen-2022-002907
5.
ManhasJ, EdelsteinHI, LeonardJN, et al.The evolution of synthetic receptor systems. Nat Chem Biol, 2022; 18(3):244–255; doi: 10.1038/s41589-021-00926-z
6.
JavdanSB, DeansTL. Design and development of engineered receptors for cell and tissue engineering. Curr Opin Syst Biol, 2021; 28; doi: 10.1016/j.coisb.2021.100363
7.
GuedanS, CalderonH, PoseyAD, et al.Engineering and design of chimeric antigen receptors. Mol Ther Methods Clin Dev, 2019; 12:145–156; doi: 10.1016/j.omtm.2018.12.009
8.
MorsutL, RoybalKT, XiongX, et al.Engineering customized cell sensing and response behaviors using synthetic notch receptors. Cell, 2016; 164(4):780–791; doi: 10.1016/j.cell.2016.01.012
9.
RoybalKT, WilliamsJZ, MorsutL, et al.Engineering T cells with customized therapeutic response programs using synthetic notch receptors. Cell, 2016; 167(2):419–432.e16; doi: 10.1016/j.cell.2016.09.011
10.
ZhuI, LiuR, GarciaJM, et al.Modular design of synthetic receptors for programmed gene regulation in cell therapies. Cell, 2022; 185(8):1431–1443.e16; doi: 10.1016/j.cell.2022.03.023
11.
SchellerL, StrittmatterT, FuchsD, et al.Generalized extracellular molecule sensor platform for programming cellular behavior. Nat Chem Biol, 2018; 14(7):723–729; doi: 10.1038/s41589-018-0046-z
12.
KalogriopoulosNA, TeiR, YanY, et al.Synthetic GPCRs for programmable sensing and control of cell behaviour. Nature, 2025; 637(8044):230–239; doi: 10.1038/s41586-024-08282-3
13.
DaringerNM, DudekRM, SchwarzKA, et al.Modular extracellular sensor architecture for engineering mammalian cell-based devices. ACS Synth Biol, 2014; 3(12):892–902; doi: 10.1021/sb400128g
14.
SchwarzKA, DaringerNM, DolbergTB, et al.Rewiring human cellular input-output using modular extracellular sensors. Nat Chem Biol, 2017; 13(2):202–209; doi: 10.1038/nchembio.2253
15.
SinghN, FreyNV, EngelsB, et al.Antigen-independent activation enhances the efficacy of 4-1BB-costimulated CD22 CAR T cells. Nat Med, 2021; 27(5):842–850; doi: 10.1038/s41591-021-01326-5
16.
LiN, QuanA, LiD, et al.The IgG4 hinge with CD28 transmembrane domain improves VHH-based CAR T cells targeting a membrane-distal epitope of GPC1 in pancreatic cancer. Nat Commun, 2023; 14(1):1986; doi: 10.1038/s41467-023-37616-4
17.
HwangMS, MillerMS, ThirawatananondP, et al.Structural engineering of chimeric antigen receptors targeting HLA-restricted neoantigens. Nat Commun, 2021; 12(1):5271; doi: 10.1038/s41467-021-25605-4
18.
BuggeK, Lindorff‐LarsenK, KragelundBB. Understanding single‐pass transmembrane receptor signaling from a structural viewpoint—what are we missing? Febs J, 2016; 283(24):4424–4451.
CheungJ, WazirS, BellDR, et al.Crystal structure of a chimeric antigen receptor (CAR) scFv domain rearrangement forming a VL-VL dimer. Crystals, 2023; 13(4):710.
21.
NobelPrize.org. They cracked the code for proteins’ amazing structures. NobelPrize.org: Nobel Prize Outreach AB 2024; 2024.
22.
JumperJ, EvansR, PritzelA, et al.Highly accurate protein structure prediction with AlphaFold. Nature, 2021; 596(7873):583–589; doi: 10.1038/s41586-021-03819-2+
23.
AbramsonJ, AdlerJ, DungerJ, et al.Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 2024; 630(8016):493–500; doi: 10.1038/s41586-024-07487-w
24.
EvansR, O’NeillM, PritzelA, et al.Protein complex prediction with AlphaFold-Multimer. bioRxiv, 2022; doi: 10.1101/2021.10.04.463034
25.
BaekM, DiMaioF, AnishchenkoI, et al.Accurate prediction of protein structures and interactions using a three-track neural network. Science, 2021; 373(6557):871–876; doi: 10.1126/science.abj8754
26.
PogozhevaID, CherepanovS, ParkSJ, et al.Structural Modeling of Cytokine-Receptor-JAK2 Signaling Complexes Using AlphaFold Multimer. J Chem Inf Model, 2023; 63(18):5874–5895; doi: 10.1021/acs.jcim.3c00926
27.
ChangJF, LandmannJH, ChangTC, et al.Rational protein engineering to enhance MHC-independent T cell receptors. Cancer Discov, 2024; 14(11):2109–2121; doi: 10.1158/2159-8290.Cd-23-1393
28.
EdelsteinHI, CosioA, EzekielML, et al.Conversion of natural cytokine receptors into orthogonal synthetic biosensors. bioRxiv, 2024; 2024(03.23.586421); doi: 10.1101/2024.03.23.586421
29.
DolbergTB, MegerAT, BoucherJD, et al.Computation-guided optimization of split protein systems. Nat Chem Biol, 2021; 17(5):531–539; doi: 10.1038/s41589-020-00729-8
30.
MirditaM, SchutzeK, MoriwakiY, et al.ColabFold: Making protein folding accessible to all. Nat Methods, 2022; 19(6):679–682; doi: 10.1038/s41592-022-01488-1
31.
KimG, LeeS, Levy KarinE, et al.Easy and accurate protein structure prediction using ColabFold. Nat Protoc, 2024; doi: 10.1038/s41596-024-01060-5
32.
PolyanskyAA, ChugunovAO, VolynskyPE, et al.PREDDIMER: A web server for prediction of transmembrane helical dimers. Bioinformatics, 2014; 30(6):889–890; doi: 10.1093/bioinformatics/btt645
33.
PolyanskyAA, VolynskyPE, EfremovRG. Multistate organization of transmembrane helical protein dimers governed by the host membrane. J Am Chem Soc, 2012; 134(35):14390–14400; doi: 10.1021/ja303483k
34.
SarabipourS, Ballmer-HoferK, HristovaK. VEGFR-2 conformational switch in response to ligand binding. Elife, 2016; 5:e13876; doi: 10.7554/eLife.13876
35.
ChakrabortyMP, DasD, MondalP, et al.Molecular basis of VEGFR1 autoinhibition at the plasma membrane. Nat Commun, 2024; 15(1):1346; doi: 10.1038/s41467-024-45499-2
36.
YangY, XieP, OpatowskyY, et al.Direct contacts between extracellular membrane-proximal domains are required for VEGF receptor activation and cell signaling. Proc Natl Acad Sci U S A, 2010; 107(5):1906–1911; doi: 10.1073/pnas.0914052107
37.
EdelsteinHI, DonahuePS, MuldoonJJ, et al.Elucidation and refinement of synthetic receptor mechanisms. Synth Biol (Oxf), 2020; 5(1):ysaa017; doi: 10.1093/synbio/ysaa017
ChanFK, ChunHJ, ZhengL, et al.A domain in TNF receptors that mediates ligand-independent receptor assembly and signaling. Science, 2000; 288(5475):2351–2354; doi: 10.1126/science.288.5475.2351
40.
D’ArcyA, BannerDW, JanesW, et al.Crystallization and preliminary crystallographic analysis of a TNF-beta-55 kDa TNF receptor complex. J Mol Biol, 1993; 229(2):555–557; doi: 10.1006/jmbi.1993.1055
41.
WajantH, SiegmundD. TNFR1 and TNFR2 in the Control of the Life and Death Balance of Macrophages. Front Cell Dev Biol, 2019; 7:91; doi: 10.3389/fcell.2019.00091
42.
MukaiY, NakamuraT, YoshikawaM, et al.Solution of the structure of the TNF-TNFR2 complex. Sci Signal, 2010; 3(148):ra83; doi: 10.1126/scisignal.2000954
43.
RichterC, MesserschmidtS, HoleiterG, et al.The tumor necrosis factor receptor stalk regions define responsiveness to soluble versus membrane-bound ligand. Mol Cell Biol, 2012; 32(13):2515–2529; doi: 10.1128/mcb.06458-11
44.
KotenkoSV, KrauseCD, IzotovaLS, et al.Identification and functional characterization of a second chain of the interleukin-10 receptor complex. Embo J, 1997; 16(19):5894–5903; doi: 10.1093/emboj/16.19.5894
45.
DoreJJJr, EdensM, GaramszegiN, et al.Heteromeric and homomeric transforming growth factor-beta receptors show distinct signaling and endocytic responses in epithelial cells. J Biol Chem, 1998; 273(48):31770–31777; doi: 10.1074/jbc.273.48.31770
46.
Vander ArkA, CaoJ, LiX. TGF-β receptors: In and beyond TGF-β signaling. Cell Signal, 2018; 52:112–120; doi: 10.1016/j.cellsig.2018.09.002
47.
MedlerJ, KuckaK, WajantH. Tumor Necrosis Factor Receptor 2 (TNFR2): An Emerging Target in Cancer Therapy. Cancers (Basel), 2022; 14(11); doi: 10.3390/cancers14112603
48.
TengF, CuiT, ZhouL, et al.Programmable synthetic receptors: The next-generation of cell and gene therapies. Signal Transduct Target Ther, 2024; 9(1):7.
49.
ValeriJA, SoenksenLR, CollinsKM, et al.BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences. Cell Syst, 2023; 14(6):525–542. e9.
50.
ZimmermannL, StephensA, NamSZ, et al.A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core. J Mol Biol, 2018; 430(15):2237–2243; doi: 10.1016/j.jmb.2017.12.007
51.
GablerF, NamSZ, TillS, et al.Protein Sequence Analysis Using the MPI Bioinformatics Toolkit. Curr Protoc Bioinformatics, 2020; 72(1):e108; doi: 10.1002/cpbi.108
52.
MengEC, GoddardTD, PettersenEF, et al.UCSF ChimeraX: Tools for structure building and analysis. Protein Sci, 2023; 32(11):e4792; doi: 10.1002/pro.4792
53.
CorcoranWK, CosioA, EdelsteinHI, et al.Supplementary Data for “Exploring structure-function relationships in engineered receptor performance using computational structure prediction. 2024; Zenodo.org
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
