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VX is a highly toxic organophosphorus nerve agent that the Chemical Weapons Convention classifies as a Schedule 1. In our previous study, we developed a method for detecting organophosphorus compounds using peptide self-assembly. Nevertheless, the self-assembly mechanisms of peptides that bind organophosphorus and the roles of each peptide residue remain elusive, restricting the design and application of peptide materials. Here, we use a multi-scale computational combined with experimental approach to illustrate the self-assembly mechanism of peptide-bound VX and the roles played by residues in different peptide sequences. We calculated that the self-assembly of peptides was accelerated after adding VX, and the final size of assembled nanofibers was larger than the original one, aligning with experimental findings. The atomic scale details offered by our approach enabled us to clarify the connection between the peptide sequences and nanostructures formation, as well as the contribution of various residues in binding VX and assembly process. Our investigation revealed a tight correlation between the number of Tyrosine residues and morphology of the assembly. These results indicate a self-assembly mechanism of peptide and VX, which can be used to design functional peptides for binding and hydrolyzing other organophosphorus nerve agents for detoxification and biomedical applications.
Fan, S. Q.; Loch, A. S.; Vongsanga, K.; Dennison, G. H.; Burn, P. L.; Gentle, I. R.; Shaw, P. E. Differentiating between V- and G-series nerve agent and simulant vapours using fluorescent film responses. Small Methods 2024, 8, 2301048.
Schneider, C.; Bierwisch, A.; Koller, M.; Worek, F.; Kubik, S. Detoxification of VX and other V-type nerve agents in water at 37 °C and pH 7.4 by substituted sulfonatocalix[4]arenes. Angew. Chem., Int. Ed. 2016, 55, 12668–12672.
Disley, J.; Gil-Ramírez, G.; Gonzalez-Rodriguez, J. A review of sensing technologies for nerve agents, through the use of agent mimics in the gas phase: Future needs. TrACTrends Anal. Chem. 2023, 168, 117282.
Park, S. Y.; Sharma, R.; Lee, H. I. Thin colorimetric film array for rapid and selective detection of v-type nerve agent mimic in potentially contaminated areas. J. Hazard. Mater. 2024, 465, 133064.
Crow, B. S.; Pantazides, B. G.; Quiñones-González, J.; Garton, J. W.; Carter, M. D.; Perez, J. W.; Watson, C. M.; Tomcik, D. J.; Crenshaw, M. D.; Brewer, B. N.et al. Simultaneous measurement of tabun, sarin, soman, cyclosarin, VR, VX, and VM adducts to tyrosine in blood products by isotope dilution UHPLC-MS/MS. Anal. Chem. 2014, 86, 10397–10405.
Fu, F. Y.; Gao, R. L.; Zhang, R. H.; Zhao, P. C.; Lu, X. G.; Li, L. Q.; Wang, H. M.; Pei, C. X. Verification of soman-related nerve agents via detection of phosphonylated adducts from rabbit albumin in vitro and in vivo. Arch. Toxicol. 2019, 93, 1853–1863.
Kranawetvogl, T.; Kranawetvogl, A.; Scheidegger, L.; Wille, T.; Steinritz, D.; Worek, F.; Thiermann, H.; John, H. Evidence of nerve agent VX exposure in rat plasma by detection of albumin-adducts in vitro and in vivo. Arch. Toxicol. 2023, 97, 1873–1885.
Harshit, D.; Charmy, K.; Nrupesh, P. Organophosphorus pesticides determination by novel HPLC and spectrophotometric method. Food Chem. 2017, 230, 448–453.
Yin, X. L.; Liu, Y. Q.; Gu, H. W.; Zhang, Q.; Zhang, Z. W.; Li, H.; Li, P. W.; Zhou, Y. Multicolor enzyme-linked immunosorbent sensor for sensitive detection of organophosphorus pesticides based on TMB2+-mediated etching of gold nanorods. Microchem. J. 2021, 168, 106411.
Liu, Y. H.; Cao, X.; Liu, Z. Q.; Sun, L. L.; Fang, G. Z.; Liu, J. F.; Wang, S. Electrochemical detection of organophosphorus pesticides based on amino acids-conjugated P3TAA-modified electrodes. Analyst 2020, 145, 8068–8076.
Wang, J. Y.; Zhang, J. Y.; Wang, J.; Fang, G. Z.; Liu, J. F.; Wang, S. Fluorescent peptide probes for organophosphorus pesticides detection. J. Hazard. Mater. 2020, 389, 122074.
Liu, D. B.; Chen, W. W.; Wei, J. H.; Li, X. B.; Wang, Z.; Jiang, X. Y. A highly sensitive, dual-readout assay based on gold nanoparticles for organophosphorus and carbamate pesticides. Anal. Chem. 2012, 84, 4185–4191.
Luo, J. D.; Xie, Z. L.; Lam, J. W. Y.; Cheng, L.; Chen, H. Y.; Qiu, C. F.; Kwok, H. S.; Zhan, X. W.; Liu, Y. Q.; Zhu, D. B. et al. Aggregation-induced emission of 1-methyl-1,2,3,4,5-pentaphenylsilole. Chem. Commun. 2001, 1740–1741.
Yang, Y. Y.; Lei, X. M.; Liu, B. R.; Liu, H. C.; Chen, J. N.; Fang, G. Z.; Liu, J. F.; Wang, S. A ratiometric fluorescent sensor based on metalloenzyme mimics for detecting organophosphorus pesticides. Sens. Actuators B: Chem. 2023, 377, 133031.
Yang, Y. Y.; Hao, S. J.; Lei, X. M.; Chen, J. N.; Fang, G. Z.; Liu, J. F.; Wang, S.; He, X. X. Design of metalloenzyme mimics based on self-assembled peptides for organophosphorus pesticides detection. J. Hazard. Mater. 2022, 428, 128262.
Liu, Z. Y.; Guo, J. Q.; Qiao, Y. C.; Xu, B. Enzyme-instructed intracellular peptide assemblies. Acc. Chem. Res. 2023, 56, 3076–3088.
Li, Q.; Min, J. W.; Zhang, J. X.; Reches, M.; Shen, Y. H.; Su, R. X.; Wang, Y. F.; Qi, W. Enzyme-driven, switchable catalysis based on dynamic self-assembly of peptides. Angew. Chem., Int. Ed. 2023, 62, e202309830.
Bigley, A. N.; Xu, C. F.; Henderson, T. J.; Harvey, S. P.; Raushel, F. M. Enzymatic neutralization of the chemical warfare agent VX: Evolution of phosphotriesterase for phosphorothiolate hydrolysis. J. Am. Chem. Soc. 2013, 135, 10426–10432.
Pohorille, A.; Jarzynski, C.; Chipot, C. Good practices in free-energy calculations. J. Phys. Chem. B 2010, 114, 10235–10253.
Lu, T.; Chen, F. W. Multiwfn: A multifunctional wavefunction analyzer. J. Comput. Chem. 2012, 33, 580–592.
Lu, T.; Chen, Q. X. Independent gradient model based on Hirshfeld partition: A new method for visual study of interactions in chemical systems. J. Comput. Chem. 2022, 43, 539–555.
Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.; Frisch, M. J. Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields. J. Phys. Chem. 1994, 98, 11623–11627.
Hariharan, P. C.; Pople, J. A. Accuracy of AH n equilibrium geometries by single determinant molecular orbital theory. Mol. Phys. 1974, 27, 209–214.
Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 2010, 132, 154104.
Grimme, S.; Ehrlich, S.; Goerigk, L. Effect of the damping function in dispersion corrected density functional theory. J. Comput. Chem. 2011, 32, 1456–1465.
Petersson, G. A.; Bennett, A.; Tensfeldt, T. G.; Al-Laham, M. A.; Shirley, W. A.; Mantzaris, J. A complete basis set model chemistry. I. The total energies of closed-shell atoms and hydrides of the first-row elements. J. Chem. Phys. 1988, 89, 2193–2218.
Marenich, A. V.; Cramer, C. J.; Truhlar, D. G. Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J. Phys. Chem. B 2009, 113, 6378–6396.
Blaudeau, J. P.; McGrath, M. P.; Curtiss, L. A.; Radom, L. Extension of gaussian-2 (G2) theory to molecules containing third-row atoms K and Ca. J. Chem. Phys. 1997, 107, 5016–5021.
Páll, S.; Zhmurov, A.; Bauer, P.; Abraham, M.; Lundborg, M.; Gray, A.; Hess, B.; Lindahl, E. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. J. Chem. Phys. 2020, 153, 134110.
Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577–8593.
Zhang, J.; Lu, T. Efficient evaluation of electrostatic potential with computerized optimized code. Phys. Chem. Chem. Phys. 2021, 23, 20323–20328.
Gilson, M. K.; Zhou, H. X. Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 2007, 36, 21–42.
Pappalardo, M.; Milardi, D.; Grasso, D. M.; La Rosa, C. Free energy perturbation and molecular dynamics calculations of copper binding to azurin. J. Comput. Chem. 2003, 24, 779–785.
Yahyavi, M.; Falsafi-Zadeh, S.; Karimi, Z.; Kalatarian, G.; Galehdari, H. VMD-SS: A graphical user interface plug-in to calculate the protein secondary structure in VMD program. Bioinformation 2014, 10, 548–550.
Becke, A. D.; Edgecombe, K. E. A simple measure of electron localization in atomic and molecular systems. J. Chem. Phys. 1990, 92, 5397–5403.
Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Meng, E. C.; Couch, G. S.; Croll, T. I.; Morris, J. H.; Ferrin, T. E. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci. 2021, 30, 70–82.
Cournia, Z.; Allen, B.; Sherman, W. Relative binding free energy calculations in drug discovery: Recent advances and practical considerations. J. Chem. Inf. Model. 2017, 57, 2911–2937.
Klimovich, P. V.; Shirts, M. R.; Mobley, D. L. Guidelines for the analysis of free energy calculations. J. Comput. Aided Mol. Des. 2015, 29, 397–411.
Lee, J.; Ju, M. S.; Cho, O. H.; Kim, Y.; Nam, K. T. Tyrosine-rich peptides as a platform for assembly and material synthesis. Adv. Sci. 2019, 6, 1801255.
Reece, S. Y.; Nocera, D. G. Proton-coupled electron transfer in biology: Results from synergistic studies in natural and model systems. Annu. Rev. Biochem. 2009, 78, 673–699.
Zhang, M. T.; Irebo, T.; Johansson, O.; Hammarström, L. Proton-coupled electron transfer from tyrosine: A strong rate dependence on intramolecular proton transfer distance. J. Am. Chem. Soc. 2011, 133, 13224–13227.
Pagba, C. V.; Chi, S. H.; Perry, J.; Barry, B. A. Proton-coupled electron transfer in tyrosine and a β-hairpin maquette: Reaction dynamics on the picosecond time scale. J. Phys. Chem. B 2015, 119, 2726–2736.
Emamian, S.; Lu, T.; Kruse, H.; Emamian, H. Exploring nature and predicting strength of hydrogen bonds: A correlation analysis between atoms-in-molecules descriptors, binding energies, and energy components of symmetry-adapted perturbation theory. J. Comput. Chem. 2019, 40, 2868–2881.
Ellenbarger, J. F.; Krieger, I. V.; Huang, H. L.; Gómez-Coca, S.; Ioerger, T. R.; Sacchettini, J. C.; Wheeler, S. E.; Dunbar, K. R. Anion-π interactions in computer-aided drug design: Modeling the inhibition of malate synthase by phenyl-diketo acids. J. Chem. Inf. Model. 2018, 58, 2085–2091.
Mahadevi, A. S.; Sastry, G. N. Cation-π interaction: Its role and relevance in chemistry, biology, and material science. Chem.Rev. 2013, 113, 2100–2138.
Xiong, Q. S.; Jiang, Y. X.; Cai, X.; Yang, F. D.; Li, Z. G.; Han, W. Conformation dependence of diphenylalanine self-assembly structures and dynamics: Insights from hybrid-resolution simulations. ACS Nano 2019, 13, 4455–4468.
Ramakrishnan, M.; van Teijlingen, A.; Tuttle, T.; Ulijn, R. V. Integrating computation, experiment, and machine learning in the design of peptide-based supramolecular materials and systems. Angew. Chem., Int. Ed. 2023, 62, e202218067.
Frederix, P. W. J. M.; Ulijn, R. V.; Hunt, N. T.; Tuttle, T. Virtual screening for dipeptide aggregation: Toward predictive tools for peptide self-assembly. J. Phys. Chem. Lett. 2011, 2, 2380–2384.
Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 33–38.
Liu, Y. Y.; Wang, Y.; Tong, C. H.; Wei, G. H.; Ding, F.; Sun, Y. X. Molecular insights into the self-assembly of block copolymer suckerin polypeptides into nanoconfined β-sheets. Small 2022, 18, 2202642.
Lee, J.; Choe, I. R.; Kim, N. K.; Kim, W. J.; Jang, H. S.; Lee, Y. S.; Nam, K. T. Water-floating giant nanosheets from helical peptide pentamers. ACS Nano 2016, 10, 8263–8270.
Mannige, R. V.; Haxton, T. K.; Proulx, C.; Robertson, E. J.; Battigelli, A.; Butterfoss, G. L.; Zuckermann, R. N.; Whitelam, S. Peptoid nanosheets exhibit a new secondary-structure motif. Nature 2015, 526, 415–420.
Oliveira, V.; Cremer, D.; Kraka, E. The many facets of chalcogen bonding: Described by vibrational spectroscopy. J. Phys. Chem. A 2017, 121, 6845–6862.
