A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics

Johnson, 2009, Gene therapy with human and mouse T-cell receptors mediates cancer regression and targets normal tissues expressing cognate antigen, Blood, 114, 535, 10.1182/blood-2009-03-211714 Linette, 2013, Cardiovascular toxicity and titin cross-reactivity of affinity-enhanced T cells in myeloma and melanoma, Blood, 122, 863, 10.1182/blood-2013-03-490565 Moore, 2018, Clinical and immunologic evaluation of three metastatic melanoma patients treated with autologous melanoma-reactive TCR-transduced T cells, Cancer Immunol Immunother, 67, 311, 10.1007/s00262-017-2073-0 Morgan, 2006, Cancer regression in patients after transfer of genetically engineered lymphocytes, Science, 314, 126, 10.1126/science.1129003 Robbins, 2011, Tumor regression in patients with metastatic synovial cell sarcoma and melanoma using genetically engineered lymphocytes reactive with NY-ESO-1, J Clin Oncol, 29, 917, 10.1200/JCO.2010.32.2537 Weekes, 2010, Comparative analysis of techniques to purify plasma membrane proteins, Journal of biomolecular techniques : JBT, 21, 108 Sykulev, 1996, Evidence that a Single Peptide–MHC Complex on a Target Cell Can Elicit a Cytolytic T Cell Response, Immunity, 4, 565, 10.1016/S1074-7613(00)80483-5 He, 2019, Targeting cancers through TCR-peptide/MHC interactions, J Hematol Oncol, 12, 139, 10.1186/s13045-019-0812-8 Gartner, 2021, A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types, Nature Cancer, 2, 563, 10.1038/s43018-021-00197-6 Kosaloglu-Yalcin Z, Lanka M, Frentzen A, Logandha Ramamoorthy Premlal A, Sidney J, Vaughan K, et al. Predicting T cell recognition of MHC class I restricted neoepitopes. Oncoimmunology. 2018;7(11):e1492508. Dijkstra, 2018, Generation of Tumor-Reactive T Cells by Co-culture of Peripheral Blood Lymphocytes and Tumor Organoids, Cell, 174, 1586, 10.1016/j.cell.2018.07.009 Gros, 2016, Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients, Nat Med, 22, 433, 10.1038/nm.4051 de Greef, 2020, The naive T-cell receptor repertoire has an extremely broad distribution of clone sizes, Elife, 9, 10.7554/eLife.49900 Zarnitsyna, 2013, Estimating the diversity, completeness, and cross-reactivity of the T cell repertoire, Front Immunol, 4, 485, 10.3389/fimmu.2013.00485 Warren, 2011, Exhaustive T-cell repertoire sequencing of human peripheral blood samples reveals signatures of antigen selection and a directly measured repertoire size of at least 1 million clonotypes, Genome Res, 21, 790, 10.1101/gr.115428.110 Kunert, 2017, T-cell Receptors for Clinical Therapy. In Vitro Assessment of Toxicity Risk, Clin Cancer Res, 23, 6012, 10.1158/1078-0432.CCR-17-1012 Mensali, 2019, Preclinical assessment of transiently TCR redirected T cells for solid tumour immunotherapy, Cancer Immunol Immunother, 68, 1235, 10.1007/s00262-019-02356-2 Kersh, 1998, High- and low-potency ligands with similar affinities for the TCR: the importance of kinetics in TCR signaling, Immunity, 9, 817, 10.1016/S1074-7613(00)80647-0 van der Merwe, 2003, Molecular interactions mediating T cell antigen recognition, Annu Rev Immunol, 21, 659, 10.1146/annurev.immunol.21.120601.141036 Rudolph, 2002, The specificity of TCR/pMHC interaction, Curr Opin Immunol, 14, 52, 10.1016/S0952-7915(01)00298-9 Zhu, 2013, Insights from in situ analysis of TCR-pMHC recognition: response of an interaction network, Immunol Rev, 251, 49, 10.1111/imr.12016 Liu, 2016, DNA-based nanoparticle tension sensors reveal that T-cell receptors transmit defined pN forces to their antigens for enhanced fidelity, Proc Natl Acad Sci U S A, 113, 5610, 10.1073/pnas.1600163113 Ma, 2019, DNA probes that store mechanical information reveal transient piconewton forces applied by T cells, Proc Natl Acad Sci U S A, 116, 16949, 10.1073/pnas.1904034116 Liu, 2014, Accumulation of dynamic catch bonds between TCR and agonist peptide-MHC triggers T cell signaling, Cell, 157, 357, 10.1016/j.cell.2014.02.053 Liu, 2015, The cellular environment regulates in situ kinetics of T-cell receptor interaction with peptide major histocompatibility complex, Eur J Immunol, 45, 2099, 10.1002/eji.201445358 Kolawole, 2018, 2D Kinetic Analysis of TCR and CD8 Coreceptor for LCMV GP33 Epitopes, Front Immunol, 9, 2348, 10.3389/fimmu.2018.02348 Sibener, 2018, Isolation of a Structural Mechanism for Uncoupling T Cell Receptor Signaling from Peptide-MHC Binding, Cell, 174, 672, 10.1016/j.cell.2018.06.017 Wu, 2019, Mechano-regulation of Peptide-MHC Class I Conformations Determines TCR Antigen Recognition, Mol Cell, 73, 1015, 10.1016/j.molcel.2018.12.018 Das, 2015, Force-dependent transition in the T-cell receptor beta-subunit allosterically regulates peptide discrimination and pMHC bond lifetime, Proc Natl Acad Sci U S A, 112, 1517, 10.1073/pnas.1424829112 Robert, 2012, Kinetics and mechanics of two-dimensional interactions between T cell receptors and different activating ligands, Biophys J, 102, 248, 10.1016/j.bpj.2011.11.4018 Limozin, 2019, TCR-pMHC kinetics under force in a cell-free system show no intrinsic catch bond, but a minimal encounter duration before binding, Proc Natl Acad Sci U S A, 116, 16943, 10.1073/pnas.1902141116 Borbulevych, 2011, TCRs used in cancer gene therapy cross-react with MART-1/Melan-A tumor antigens via distinct mechanisms, J Immunol, 187, 2453, 10.4049/jimmunol.1101268 Ivanciuc, 2009, The property distance index PD predicts peptides that cross-react with IgE antibodies, Mol Immunol, 46, 873, 10.1016/j.molimm.2008.09.004 Olsson, 2011, PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions, J Chem Theory Comput, 7, 525, 10.1021/ct100578z Sondergaard, 2011, Improved Treatment of Ligands and Coupling Effects in Empirical Calculation and Rationalization of pKa Values, J Chem Theory Comput, 7, 2284, 10.1021/ct200133y Van Der Spoel, 2005, GROMACS: fast, flexible, and free, J Comput Chem, 26, 1701, 10.1002/jcc.20291 Jorgensen, 1983, Comparison of simple potential functions for simulating liquid water, J Chem Phys, 79, 926, 10.1063/1.445869 MacKerell, 1998, All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins, J Phys Chem B, 102, 3586, 10.1021/jp973084f Berendsen, 1984, Molecular dynamics with coupling to an external bath, J Chem Phys, 81, 3684, 10.1063/1.448118 Evans, 1985, The Nose-Hoover thermostat, J Chem Phys, 83, 4069, 10.1063/1.449071 Parrinello, 1981, Polymorphic transitions in single crystals: A new molecular dynamics method, J Appl Phys, 52, 7182, 10.1063/1.328693 Di Pierro, 2015, A Stochastic Algorithm for the Isobaric-Isothermal Ensemble with Ewald Summations for All Long Range Forces, J Chem Theory Comput, 11, 5624, 10.1021/acs.jctc.5b00648 Ewald PP. Die Berechnung optischer und elektrostatischer Gitterpotentiale. Annalen der Physik. 1921;369(3):253-87. Hess, 1997, LINCS: A linear constraint solver for molecular simulations, J Comput Chem, 18, 1463, 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H Rollins ZA, Faller R, George SC. T Cell Receptor Non-Equilibrium Kinetics. bioRxiv. 2021:2021.10.27.466112. Harris, 2020, Array programming with NumPy, Nature, 585, 357, 10.1038/s41586-020-2649-2 McKinney W. Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference. 2010;445. Waskom MB, Olga; O'Kane, Drew; Hobson, Paul; Lukauskas, Saulius; Gemperline, David C; Augspurger, Tom; Halchenko, Yaroslav; Cole, John B; Warmenhoven, Jordi; de Ruiter, Julian; Pye, Cameron; Hoyer, Stephan; Vanderplas, Jake; Villalba, Santi; Kunter, Gero; Quintero, Eric; Bachant, Pete; Martin, Marcel; Qalieh, Adel. mwaskom/seaborn: v0.8.1. 0.8.1 ed. Meyrin, Switzerland: Zenodo; 2017. Hunter, 2007, Matplotlib: A 2D Graphics Environment, Comput Sci Eng, 9, 90, 10.1109/MCSE.2007.55 Virtanen, 2020, SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat Methods, 17, 261, 10.1038/s41592-019-0686-2 Beckstein OD, Jan; Somogyi, Andy. GromacsWrapper: v0.3.3 (release-0.3.3). 0.3.3 ed. Meyrin, Switzerland: Zenodo; 2015. Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, et al., editors. API design for machine learning software: experiences from the scikit-learn project. European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases; 2013 2013-09-23; Prague, Czech Republichttps://hal.inria.fr/hal-00856511/document. https://hal.inria.fr/hal-00856511/file/paper.pdf. Pedregosa, 2011122825, Scikit-learn: Machine Learning in Python, J Mach Learn Res Raschka, 2018, MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack, The Journal of Open Source Software, 3, 638, 10.21105/joss.00638 Zou, 2005, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 301, 10.1111/j.1467-9868.2005.00503.x Rivoltini, 1999, A superagonist variant of peptide MART1/Melan A27–35 elicits anti-melanoma CD8+ T cells with enhanced functional characteristics: implication for more effective immunotherapy, Cancer Res, 59, 301 Hellman, 2019, Improving T Cell Receptor On-Target Specificity via Structure-Guided Design, Mol Ther, 27, 300, 10.1016/j.ymthe.2018.12.010 Solbach, 2021, Determining structure and action mechanism of LBF14 by molecular simulation, J Biomol Struct Dyn, 1–12 Hwang, 2020, The alphabetaTCR mechanosensor exploits dynamic ectodomain allostery to optimize its ligand recognition site, Proc Natl Acad Sci U S A, 117, 21336, 10.1073/pnas.2005899117 Welch, 2016, Understanding the Role of Solvation Forces on the Preferential Attachment of Nanoparticles in Liquid, ACS Nano, 10, 181, 10.1021/acsnano.5b06632 Huang, 2022, SARS-CoV-2 spike binding to ACE2 is stronger and longer ranged due to glycan interaction, Biophys J, 121, 79, 10.1016/j.bpj.2021.12.002 Xiong, 2019, Effects of N-Glycosylation on the Structure, Function, and Stability of a Plant-Made Fc-Fusion Anthrax Decoy Protein. Frontiers, Plant Sci, 10 Martínez, 2015, Automatic Identification of Mobile and Rigid Substructures in Molecular Dynamics Simulations and Fractional Structural Fluctuation Analysis, PLoS ONE, 10, e0119264, 10.1371/journal.pone.0119264 Trunk, 1979, A problem of dimensionality: a simple example, IEEE Trans Pattern Anal Mach Intell, 1, 306, 10.1109/TPAMI.1979.4766926 McLachlan, 2004, Discriminant analysis and statistical pattern recognition, xv, 526 pp. Zollanvari, 2020, A Theoretical Analysis of the Peaking Phenomenon in Classification, J Classif, 37, 421, 10.1007/s00357-019-09327-3