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Annual Review of Physical Chemistry

Volume 74, 2023

Gas-Phase Computational Spectroscopy: The Challenge of the Molecular Bricks of Life

Abstract

Gas-phase molecular spectroscopy is a natural playground for accurate quantum-chemical computations. However, the molecular bricks of life (e.g., DNA bases or amino acids) are challenging systems because of the unfavorable scaling of quantum-chemical models with the molecular size (active electrons) and/or the presence of large-amplitude internal motions. From the theoretical point of view, both aspects prevent the brute-force use of very accurate but very expensive state-of-the-art quantum-chemical methodologies. From the experimental point of view, both features lead to congested gas-phase spectra, whose assignment and interpretation are not at all straightforward. Based on these premises, this review focuses on the current status and perspectives of the fully a priori prediction of the spectral signatures of medium-sized molecules (containing up to two dozen atoms) in the gas phase with special reference to rotational and vibrational spectroscopies of some representative molecular bricks of life.

Keyword(s): anharmonicity, computational spectroscopy, flexibility, molecular bricks of life, rotational spectra, vibrational spectra
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2023-04-24
2024-04-26
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Literature Cited

  1. 1.
    Barone V. 2016. The virtual multifrequency spectrometer: a new paradigm for spectroscopy. WIREs Comput. Mol. Sci. 6:86–110
     [Google Scholar]
  2. 2.
    Sugiki T, Kobayashi N, Fujiwara T. 2017. Modern technologies of solution nuclear magnetic resonance spectroscopy for three-dimensional structure determination of proteins open avenues for life scientists. Comput. Struct. Biotechnol. J. 15:328–29
     [Google Scholar]
  3. 3.
    Puzzarini C, Barone V. 2018. Diving for accurate structures in the ocean of molecular systems with the help of spectroscopy and quantum chemistry. Acc. Chem. Res. 51:548–56
     [Google Scholar]
  4. 4.
    Lane JL 2018. Frontiers and Advances in Molecular Spectroscopy Amsterdam: Elsevier
  5. 5.
    Puzzarini C, Bloino J, Tasinato N, Barone V. 2019. Accuracy and interpretability: the devil and the holy grail. New routes across old boundaries in computational spectroscopy. Chem. Rev. 119:8131–91
     [Google Scholar]
  6. 6.
    Melosso M, Bizzocchi L, Gazzeh H, Tonolo F, Guillemin JC et al. 2022. Gas-phase identification of (Z)-1,2-ethenediol, a key prebiotic intermediate in the formose reaction. Chem. Commun. 58:2750–53
     [Google Scholar]
  7. 7.
    Rivilla VM, Colzi L, Jiménez-Serra I, Martín-Pintado J, Megías A et al. 2022. Precursors of the RNA world in space: detection of (Z)-1,2-ethenediol in the interstellar medium, a key intermediate in sugar formation. Astrophys. J. Lett. 929:L11
     [Google Scholar]
  8. 8.
    Gardner JP, Mather JC, Clampin M, Doyon R, Greenhouse MA et al. 2006. The James Webb Space Telescope. Space Sci. Rev. 123:485–606
     [Google Scholar]
  9. 9.
    He Y, Tang L, Wu X, Hou X, Lee Y. 2007. Spectroscopy: the best way toward green analytical chemistry?. Appl. Spectrosc. Rev. 42:119–38
     [Google Scholar]
  10. 10.
    Lindon JC, Tranter GE, Koppenaal DW, eds. 2017. Encyclopedia of Spectroscopy and Spectrometry Oxford, UK: Academic. , 3rd ed..
  11. 11.
    Martin-Drumel MA, McCarthy MC, Patterson D, McGuire BA, Crabtree KN. 2016. Automated microwave double resonance spectroscopy: a tool to identify and characterize chemical compounds. J. Chem. Phys. 144:124202
     [Google Scholar]
  12. 12.
    Lesarri A, Mata S, López J, Alonso J 2003. A laser-ablation molecular-beam Fourier-transform microwave spectrometer: the rotational spectrum of organic solids. Rev. Sci. Instrum. 74:4799–804
     [Google Scholar]
  13. 13.
    Blanco S, Lesarri A, López JC, Alonso JL. 2004. The gas-phase structure of alanine. J. Am. Chem. Soc. 126:11675–83
     [Google Scholar]
  14. 14.
    Sanz ME, Blanco S, López JC, Alonso JL. 2008. Rotational probes of six conformers of neutral cysteine. Angew. Chem. Int. Ed. 47:6216–20
     [Google Scholar]
  15. 15.
    Puzzarini C, Biczysko M, Barone V, Largo L, Peña I et al. 2014. Accurate characterization of the peptide linkage in the gas phase: a joint quantum-chemical and rotational spectroscopy study of the glycine dipeptide analogue. J. Phys. Chem. Lett. 5:534–40
     [Google Scholar]
  16. 16.
    Alonso JL, López JC 2015. Microwave spectroscopy of biomolecular building blocks. Gas-Phase IR Spectroscopy and Structure of Biological Molecules A Rijs, J Oomens 335–401. Cham, Switz: Springer
     [Google Scholar]
  17. 17.
    León I, Alonso ER, Mata S, Cabezas C, Alonso JL. 2019. Unveiling the neutral forms of glutamine. Angew. Chem. Int. Ed. 58:16002–7
     [Google Scholar]
  18. 18.
    Brown GG, Dian BC, Douglass KO, Geyer SM, Shipman ST, Pate BH. 2008. A broadband Fourier transform microwave spectrometer based on chirped pulse excitation. Rev. Sci. Instrum. 79:053103
     [Google Scholar]
  19. 19.
    Steber AL, Neill JL, Zaleski DP, Pate BH, Lesarri A et al. 2011. Structural studies of biomolecules in the gas phase by chirped-pulse Fourier transform microwave spectroscopy. Faraday Discuss. 150:227–42
     [Google Scholar]
  20. 20.
    Pérez C, Neill JL, Muckle MT, Zaleski DP, Peña I et al. 2015. Water–water and water–solute interactions in microsolvated organic complexes. Angew. Chem. Int. Ed. 54:979–82
     [Google Scholar]
  21. 21.
    Steber AL, Pérez C, Temelso B, Shields GC, Rijs AM et al. 2017. Capturing the elusive water trimer from the stepwise growth of water on the surface of the polycyclic aromatic hydrocarbon acenaphthene. J. Phys. Chem. Lett. 8:235744–50
     [Google Scholar]
  22. 22.
    Puzzarini C, Spada L, Alessandrini S, Barone V 2020. The challenge of non-covalent interactions: Theory meets experiment for reconciling accuracy and interpretation. J. Phys. Condens. Matter 32:343002
     [Google Scholar]
  23. 23.
    Xie F, Fusè M, Hazrah AS, Jaeger W, Barone V, Xu Y. 2020. Discovering the elusive global minimum in a ternary chiral cluster: rotational spectra of propylene oxide trimer. Angew. Chem. Int. Ed. 59:22427–30
     [Google Scholar]
  24. 24.
    Li X, Spada L, Alessandrini S, Zheng Y, Lengsfels KG et al. 2022. Stacked but not stuck: unveiling the role of π–π* interactions with the help of the benzofuran-formaldehyde complex. Angew. Chem. Int. Ed. 61:264–70
     [Google Scholar]
  25. 25.
    Linder R, Nispel M, Haber T, Kleinermanns K. 2005. Gas-phase FT-IR-spectra of natural amino acids. Chem. Phys. Lett. 409:260–64
     [Google Scholar]
  26. 26.
    Linder R, Seefeld K, Vavra A, Kleinermanns K. 2008. Gas phase infrared spectra of nonaromatic amino acids. Chem. Phys. Lett. 453:1–6
     [Google Scholar]
  27. 27.
    Bloino J, Biczysko M, Barone V. 2012. General perturbative approach for spectroscopy, thermodynamics and kinetics: methodological background and benchmark studies. J. Chem. Theory Comput. 8:1015–36
     [Google Scholar]
  28. 28.
    Barone V, Biczysko M, Bloino J. 2014. Fully anharmonic IR and Raman spectra of medium-size molecular systems: accuracy and interpretation. Phys. Chem. Chem. Phys. 16:1759–87
     [Google Scholar]
  29. 29.
    Balabin RM. 2010. Conformational equilibrium in glycine: experimental jet-cooled Raman spectrum. J. Phys. Chem. Lett. 1:20–23
     [Google Scholar]
  30. 30.
    Balabin RM. 2010. The identification of the two missing conformers of gas-phase alanine: a jet-cooled Raman spectroscopy study. Phys. Chem. Chem. Phys. 12:5980–82
     [Google Scholar]
  31. 31.
    Barone V. 2012. Computational Strategies for Spectroscopy: From Small Molecules to Nano Systems Hoboken, NJ: Wiley
  32. 32.
    Barone V, Alessandrini S, Biczysko M, Cheeseman JR, Clary DC et al. 2021. Computational molecular spectroscopy. Nat. Rev. Methods Primers 1:38
     [Google Scholar]
  33. 33.
    Cazzoli G, Puzzarini C, Harding ME, Gauss J. 2009. The hyperfine structure in the rotational spectrum of water: Lamb-dip technique and quantum-chemical calculations. Chem. Phys. Lett. 473:121–25
     [Google Scholar]
  34. 34.
    Puzzarini C, Cazzoli G, Harding ME, Vázquez J, Gauss J. 2009. A new experimental absolute nuclear magnetic shielding scale for oxygen based on the rotational hyperfine structure of O. J. Chem. Phys. 131:234304
     [Google Scholar]
  35. 35.
    Martin JML, Kesharwani MK. 2014. Assessment of CCSD(T)-F12 approximations and basis sets for harmonic vibrational frequencies. J. Chem. Theory Comput. 10:2085–90
     [Google Scholar]
  36. 36.
    Agbaglo D, Fortenberry RC. 2019. The performance of explicitly correlated wavefunctions [CCSD(T)-F12b] in the computation of anharmonic vibrational frequencies. Int. J. Quantum Chem. 119:e25899
     [Google Scholar]
  37. 37.
    Heim ZN, Amberger BK, Esselman BJ, Stanton JF, Woods RC, McMahon RJ. 2020. Molecular structure determination: equilibrium structure of pyrimidine (m-C4H4N2) from rotational spectroscopy () and high-level ab initio calculation (re) agree within the uncertainty of experimental measurement. J. Chem. Phys. 152:104303
     [Google Scholar]
  38. 38.
    Barone V, Puzzarini C, Mancini G. 2021. Integration of theory, simulation, artificial intelligence and virtual reality: a four-pillar approach for reconciling accuracy and interpretability in computational spectroscopy. Phys. Chem. Chem. Phys. 23:17079–96
     [Google Scholar]
  39. 39.
    Puzzarini C, Biczysko M, Barone V. 2011. Accurate anharmonic vibrational frequencies for uracil: the performance of composite schemes and hybrid CC/DFT model. J. Chem. Theory Comput. 7:3702–10
     [Google Scholar]
  40. 40.
    Davisson JL, Brinckmann NR, Polik WF. 2012. Accurate and efficient calculation of excited vibrational states from quartic potential energy surfaces. Mol. Phys. 51:2587–98
     [Google Scholar]
  41. 41.
    Barone V, Biczysko M, Bloino J, Puzzarini C. 2013. Accurate structure, thermochemistry and spectroscopic parameters from CC and CC/DFT schemes: the challenge of the conformational equilibrium in glycine. Phys. Chem. Chem. Phys. 15:10094–111
     [Google Scholar]
  42. 42.
    Puzzarini C, Barone V. 2010. Toward spectroscopic accuracy for open-shell systems: molecular structure and hyperfine coupling constants of H2CN, H2CP, NH2, and PH2 as test cases. J. Chem. Phys. 133:184301
     [Google Scholar]
  43. 43.
    Alessandrini S, Barone V, Puzzarini C. 2020. Extension of the “cheap” composite approach to noncovalent interactions: the jun-ChS scheme. J. Chem. Theory Comput. 16:988–1006
     [Google Scholar]
  44. 44.
    Lupi J, Alessandrini S, Puzzarini C, Barone V. 2021. junChS and junChS-F12 models: parameter-free efficient yet accurate composite schemes for energies and structures of noncovalent complexes. J. Chem. Theory Comput. 17:116974–92
     [Google Scholar]
  45. 45.
    Mills IM 1972. Vibration-rotation structure in asymmetric- and symmetric-top molecules. Molecular Spectroscopy: Modern Research, Vol. 1 KN Rao, CW Mathews 115–40. New York: Academic
     [Google Scholar]
  46. 46.
    Papoušek D, Aliev MR. 1982. Molecular Vibrational/Rotational Spectra Amsterdam: Elsevier
  47. 47.
    Aliev MR, Watson JKG 1985. Higher-order effects in the vibration-rotation spectra of semirigid molecules. Molecular Spectroscopy: Modern Research, Vol. 3 KN Rao 2–67. New York: Academic
     [Google Scholar]
  48. 48.
    Biczysko M, Panek P, Scalmani G, Bloino J, Barone V. 2010. Harmonic and anharmonic vibrational frequency calculations with the double-hybrid B2PLYP method: analytic second derivatives and benchmark studies. J. Chem. Theory Comput. 6:2115–25
     [Google Scholar]
  49. 49.
    Bloino J, Biczysko M, Barone V. 2015. Anharmonic effects on vibrational spectra intensities: infrared, Raman, vibrational circular dichroism and Raman optical activity. J. Phys. Chem. A 119:11862–74
     [Google Scholar]
  50. 50.
    Barone V, Ceselin G, Fusè M, Tasinato N. 2020. Accuracy meets interpretability for computational spectroscopy by means of hybrid and double-hybrid functionals. Front. Chem. 8:584203
     [Google Scholar]
  51. 51.
    Franke PR, Stanton JF, Douberly GE. 2021. How to VPT2: accurate and intuitive simulations of CH stretching infrared spectra using VPT2+K with large effective Hamiltonian resonance treatments. J. Phys. Chem. A 125:1301–24
     [Google Scholar]
  52. 52.
    Puzzarini C, Stanton JF, Gauss J. 2010. Quantum-chemical calculation of spectroscopic parameters for rotational spectroscopy. Int. Rev. Phys. Chem. 29:273–367
     [Google Scholar]
  53. 53.
    Mancini G, Fusè M, Lazzari F, Chandramouli B, Barone V. 2020. Unsupervised search of low-lying conformers with spectroscopic accuracy: a two-step algorithm rooted into the island model evolutionary algorithm. J. Chem. Phys. 153:124110
     [Google Scholar]
  54. 54.
    Puzzarini C, Biczysko M, Barone V. 2010. Accurate harmonic/anharmonic vibrational frequencies for open-shell systems: performances of the B3LYP/N07D model for semirigid free radicals benchmarked by CCSD(T) computations. J. Chem. Theory Comput. 6:828–38
     [Google Scholar]
  55. 55.
    Puzzarini C, Barone V. 2011. Extending the molecular size in accurate quantum-chemical calculations: the equilibrium structure and spectroscopic properties of uracil. Phys. Chem. Chem. Phys. 13:7189–97
     [Google Scholar]
  56. 56.
    Vainio MJ, Johnson MS. 2007. Generating conformer ensembles using a multiobjective genetic algorithm. J. Chem. Inf. Model. 47:2462–74
     [Google Scholar]
  57. 57.
    O'Boyle NM, Vandermeersch T, Flynn CJ, Maguire AR, Hutchison GR. 2011. Confab – systematic generation of diverse low-energy conformers. J. Cheminf. 3:8
     [Google Scholar]
  58. 58.
    Goto H, Takahashi T, Takata Y, Ohta K, Nagashima U. 2003. CONFLEX: conformational behaviors of polypeptides as predicted by a conformational space search. Nanotech 1:32–35
     [Google Scholar]
  59. 59.
    Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT. 2010. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J. Chem. Inf. Model. 50:572–84
     [Google Scholar]
  60. 60.
    Pracht P, Bohle F, Grimme S. 2020. Automated exploration of the low-energy chemical space with fast quantum chemical methods. Phys. Chem. Chem. Phys. 22:7169–92
     [Google Scholar]
  61. 61.
    Beusen DD, Berkley Shands E, Karasek S, Marshall GR, Dammkoehler RA 1996. Systematic search in conformational analysis. J. Mol. Struct. THEOCHEM 370:157–71
     [Google Scholar]
  62. 62.
    Rienstra CM, Tucker-Kellogg L, Jaroniec CP, Hohwy M, Reif B et al. 2002. De novo determination of peptide structure with solid-state magic-angle spinning NMR spectroscopy. PNAS 99:10260–65
     [Google Scholar]
  63. 63.
    Brownlee J. 2012. Clever Algorithms: Nature-Inspired Programming Recipes rev. 2. http://www.CleverAlgorithms.com
  64. 64.
    Watson JKG. 1967. Determination of centrifugal distortion coefficients of asymmetric-top molecules. J. Chem. Phys. 46:1935–49
     [Google Scholar]
  65. 65.
    Piccardo M, Bloino J, Barone V. 2015. Generalized vibrational perturbation theory for rotovibrational energies of linear, symmetric, and asymmetric tops: theory, approximations, and automated approaches to deal with medium-to-large molecular systems. Int. J. Quantum Chem. 115:948–82
     [Google Scholar]
  66. 66.
    Heckert M, Kállay M, Gauss J. 2005. Molecular equilibrium geometries based on coupled-cluster calculations including quadruple excitations. Mol. Phys. 103:2109–115
     [Google Scholar]
  67. 67.
    Heckert M, Kállay M, Tew DP, Klopper W, Gauss J. 2006. Basis-set extrapolation techniques for the accurate calculation of molecular equilibrium geometries using coupled-cluster theory. J. Chem. Phys. 125:044108
     [Google Scholar]
  68. 68.
    Matthews DA, Cheng L, Harding ME, Lipparini F, Stopkowicz S et al. 2020. Coupled-cluster techniques for computational chemistry: the CFOUR program package. J. Chem. Phys. 152:214108
     [Google Scholar]
  69. 69.
    Raghavachari K, Trucks GW, Pople JA, Head-Gordon M. 1989. A fifth-order perturbation comparison of electron correlation theories. Chem. Phys. Lett. 157:479–83
     [Google Scholar]
  70. 70.
    Møller C, Plesset MS. 1934. Note on an approximation treatment for many-electron systems. Phys. Rev. 46:7618–22
     [Google Scholar]
  71. 71.
    Kong L, Bischoff FA, Valeev EF. 2012. Explicitly correlated R12/F12 methods for electronic structure. Chem. Rev. 112:75–107
     [Google Scholar]
  72. 72.
    Barone V, Lupi J, Salta Z, Tasinato N. 2021. Development and validation of a parameter-free model chemistry for the computation of reliable reaction rates. J. Chem. Theory Comput. 17:4913–28
     [Google Scholar]
  73. 73.
    Knizia G, Adler TB, Werner HJ. 2009. Simplified CCSD(T)-F12 methods: theory and benchmarks. J. Chem. Phys. 130:054104
     [Google Scholar]
  74. 74.
    Papajak E, Zheng J, Xu X, Leverentz HR, Truhlar DG. 2011. Perspectives on basis sets beautiful: seasonal plantings of diffuse basis functions. J. Chem. Theory Comput. 7:3027–34
     [Google Scholar]
  75. 75.
    Peterson KA, Adler TB, Werner HJ. 2008. Systematically convergent basis sets for explicitly correlated wavefunctions: the atoms H, He, B–Ne, and Al–Ar. J. Chem. Phys. 128:084102
     [Google Scholar]
  76. 76.
    Werner HJ, Adler TB, Manby FR. 2007. General orbital invariant MP2-F12 theory. J. Chem. Phys. 126:164102
     [Google Scholar]
  77. 77.
    Spackman PR, Jayatilaka D, Karton A. 2016. Basis set convergence of CCSD(T) equilibrium geometries using a large and diverse set of molecular structures. J. Chem. Phys. 145:104101
     [Google Scholar]
  78. 78.
    Becke AD. 1988. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 38:3098–100
     [Google Scholar]
  79. 79.
    Zhao Y, Truhlar DG. 2005. Design of density functionals that are broadly accurate for thermochemistry, thermochemical kinetics, and nonbonded interactions. J. Phys. Chem. A 109:5656–67
     [Google Scholar]
  80. 80.
    Grimme S. 2006. Semiempirical hybrid density functional with perturbative second-order correlation. J. Chem. Phys. 124:034108
     [Google Scholar]
  81. 81.
    Santra G, Sylvetsky N, Martin JM. 2019. Minimally empirical double-hybrid functionals trained against the GMTKN55 database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4. J. Phys. Chem. A 123:5129–43
     [Google Scholar]
  82. 82.
    Grimme S, Antony J, Ehrlich S, Krieg H 2010. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132:154104
     [Google Scholar]
  83. 83.
    Grimme S, Ehrlich S, Goerigk L. 2011. Effect of the damping function in dispersion corrected density functional theory. J. Comput. Chem. 32:1456–65
     [Google Scholar]
  84. 84.
    Papajak E, Truhlar DG. 2011. Convergent partially augmented basis sets for post-Hartree-Fock calculations of molecular properties and reaction barrier heights. J. Chem. Theory Comput. 7:10–18
     [Google Scholar]
  85. 85.
    Yang Q, Mendolicchio M, Barone V, Bloino J. 2021. Accuracy and reliability in the simulation of vibrational spectra: a comprehensive benchmark of energies and intensities issuing from generalized vibrational perturbation theory to second order (GVPT2). Front. Astron. Space Sci. 8:665232
     [Google Scholar]
  86. 86.
    Ceselin G, Barone V, Tasinato N. 2021. Accurate biomolecular structures by the nano-LEGO approach: pick the bricks and build your geometry. J. Chem. Theory Comput. 17:7290–311
     [Google Scholar]
  87. 87.
    Melli A, Tonolo F, Barone V, Puzzarini C. 2021. Extending the applicability of the semi-experimental approach by means of “template molecule” and “linear regression” models on top of DFT computations. J. Phys. Chem. A 125:9904–16
     [Google Scholar]
  88. 88.
    Penocchio E, Piccardo M, Barone V. 2015. Semiexperimental equilibrium structures for building blocks of organic and biological molecules: the B2PLYP route. J. Chem. Theory Comput. 11:4689–707
     [Google Scholar]
  89. 89.
    Lazzari F, Salvadori A, Mancini G, Barone V. 2020. Molecular perception for visualization and computation: the Proxima library. J. Chem. Inf. Model. 60:2668–72
     [Google Scholar]
  90. 90.
    Ferro-Costas D, Mosquera-Lois I, Fernandez-Ramos A. 2021. Torsiflex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids. J. Cheminform. 13:100
     [Google Scholar]
  91. 91.
    Stewart JJP. 2007. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J. Mol. Model. 13:1173–213
     [Google Scholar]
  92. 92.
    Bannwarth C, Ehlert S, Grimme S. 2019. GFN2-xTB, an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15:1652–71
     [Google Scholar]
  93. 93.
    Boese AD, Oren M, Atasoylu O, Martin JML, Kállay M, Gauss J. 2004. W3 theory: robust computational thermochemistry in the kJ/mol accuracy range. J. Chem. Phys. 120:4129–41
     [Google Scholar]
  94. 94.
    Karton A, Rabinovich E, Martin JML, Ruscic B. 2006. W4 theory for computational thermochemistry: in pursuit of confident sub-kJ/mol predictions. J. Chem. Phys. 125:144108
     [Google Scholar]
  95. 95.
    Karton A, Martin JML. 2012. Explicitly correlated Wn theory: W1-F12 and W2-F12. J. Chem. Phys. 136:124114
     [Google Scholar]
  96. 96.
    Tajti A, Szalay PG, Császár AG, Kállay M, Gauss J et al. 2004. HEAT: high accuracy extrapolated ab initio thermochemistry. J. Chem. Phys. 121:11599–613
     [Google Scholar]
  97. 97.
    Császár AG, Allen WD, Schaefer HF III. 1998. In pursuit of the ab initio limit for conformational energy prototypes. J. Chem. Phys. 108:9751–64
     [Google Scholar]
  98. 98.
    Peterson KA, Feller D, Dixon D. 2012. Chemical accuracy in ab initio thermochemistry and spectroscopy: current strategies and future challenges. Theor. Chem. Acc. 113:1079
     [Google Scholar]
  99. 99.
    Bomble YJ, Vázquez J, Kállay M, Michauk C, Szalay PG et al. 2006. High-accuracy extrapolated ab initio thermochemistry. II. Minor improvements to the protocol and a vital simplification. J. Chem. Phys. 125:064108
     [Google Scholar]
  100. 100.
    Harding ME, Vázquez J, Ruscic B, Wilson AK, Gauss J, Stanton JF. 2008. High-accuracy extrapolated ab initio thermochemistry. III. Additional improvements and overview. J. Chem. Phys. 128:114111
     [Google Scholar]
  101. 101.
    Alessandrini S, Tonolo F, Puzzarini C. 2021. In search of phosphorus in astronomical environments: the reaction between the CP radical (X2Σ+) and methanimine. J. Chem. Phys. 154:054306
     [Google Scholar]
  102. 102.
    Tew DP, Klopper W, Heckert M, Gauss J. 2007. Basis set limit CCSD(T) harmonic vibrational frequencies. J. Phys. Chem. A 111:11242–48
     [Google Scholar]
  103. 103.
    Watrous A, Westbrook BR, Fortenberry RC. 2021. F12-TZ-cCR: a methodology for faster and still highly accurate quartic force fields. J. Phys. Chem. A 125:10532–40
     [Google Scholar]
  104. 104.
    Barone V. 2005. Anharmonic vibrational properties by a fully automated second order perturbative approach. J. Chem. Phys. 122:014108
     [Google Scholar]
  105. 105.
    Gordy W, Cook RL. 1984. Microwave Molecular Spectra New York: Wiley. , 3rd ed..
  106. 106.
    Piccardo M, Penocchio E, Puzzarini C, Biczysko M, Barone V. 2015. Semi-experimental equilibrium structure determinations by employing B3LYP/SNSD anharmonic force fields: validation and application to semirigid organic molecules. J. Phys. Chem. A 119:2058–82
     [Google Scholar]
  107. 107.
    Rosnik AM, Polik WF. 2014. VPT2+K spectroscopic constants and matrix elements of the transformed vibrational Hamiltonian of a polyatomic molecule with resonances using Van Vleck perturbation theory. Mol. Phys. 112:261–300
     [Google Scholar]
  108. 108.
    Vázquez J, Stanton JF. 2007. Treatment of Fermi resonance effects on transition moments in vibrational perturbation theory. Mol. Phys. 105:101–9
     [Google Scholar]
  109. 109.
    Mendolicchio M, Bloino J, Barone V. 2021. A general perturb-then-diagonalize model for the vibrational frequencies and intensities of molecules belonging to Abelian and non-Abelian symmetry groups. J. Chem. Theory Comput. 19:1759–87
     [Google Scholar]
  110. 110.
    Puzzarini C, Tasinato N, Bloino J, Spada L, Barone V. 2019. State-of-the-art computation of the rotational and IR spectra of the methyl-cyclopropyl cation: hints on its detection in space. Phys. Chem. Chem. Phys. 21:3615–25
     [Google Scholar]
  111. 111.
    Baiardi A, Bloino J, Barone V. 2017. Simulation of vibronic spectra of flexible systems: hybrid DVR-harmonic approaches. J. Chem. Theory Comput. 13:2804–22
     [Google Scholar]
  112. 112.
    Ceselin G, Salta Z, Bloino J, Tasinato N, Barone V. 2022. Accurate quantum chemical spectroscopic characterization of glycolic acid: a route toward its astrophysical detection. J. Phys. Chem. A 126:2373–87
     [Google Scholar]
  113. 113.
    Barone V, Fusè M, Vieira Pinto SM, Tasinato N 2021. A computational journey across nitroxide radicals: from structure to spectroscopic properties and beyond. Molecules 26:7404
     [Google Scholar]
  114. 114.
    Puzzarini C, Heckert M, Gauss J. 2008. The accuracy of rotational constants predicted by high-level quantum-chemical calculations. I. Molecules containing first-row atoms. J. Chem. Phys. 128:194108
     [Google Scholar]
  115. 115.
    Alessandrini S, Gauss J, Puzzarini C. 2018. Accuracy of rotational parameters predicted by high-level quantum-chemical calculations: case study of sulfur-containing molecules of astrochemical interest. J. Chem. Theory Comput. 14:5360–71
     [Google Scholar]
  116. 116.
    Puzzarini C, Biczysko M, Barone V, Peña I, Cabezas C, Alonso JL. 2013. Accurate molecular structure and spectroscopic properties of nucleobases: a combined computational-microwave investigation of 2-thiouracil as a case study. Phys. Chem. Chem. Phys. 15:16965–75
     [Google Scholar]
  117. 117.
    Barone V, Biczysko M, Bloino J, Cimino P, Penocchio E, Puzzarini C. 2015. CC/DFT route toward accurate structures and spectroscopic features for observed and elusive conformers of flexible molecules: pyruvic acid as a case study. J. Chem. Theory Comput. 11:4342–63
     [Google Scholar]
  118. 118.
    Barone V, Festa G, Grandi A, Rega N, Sanna N. 2004. Vibrational spectra of large molecules by density functional computations beyond the harmonic approximation. The case of uracil and 2-thiouracil. Chem. Phys. Lett. 388:279–83
     [Google Scholar]
  119. 119.
    Fornaro T, Biczysko M, Monti S, Barone V. 2014. Dispersion corrected DFT approaches for anharmonic vibrational frequency calculations: nucleobases and their dimers. Phys. Chem. Chem. Phys. 16:10112–18
     [Google Scholar]
  120. 120.
    López JC, Peña I, Sanz ME, Alonso JL. 2007. Probing thymine with laser ablation molecular beam Fourier transform microwave spectroscopy. J. Chem. Phys. 126:191103
     [Google Scholar]
  121. 121.
    Alonso JL, Peña I, López JC, Vaquero V. 2009. Rotational spectra signatures of four tautomers of guanine. Angew. Chem. Int. Ed. 48:6141–43
     [Google Scholar]
  122. 122.
    Alonso JL, Vaquero V, Peña I, López JC, Mata S, Caminati W. 2013. All five forms of cytosine revealed in the gas phase. Angew. Chem. Int. Ed. 52:2331–34
     [Google Scholar]
  123. 123.
    Choi MY, Miller RE. 2006. Four tautomers of isolated guanine from infrared laser spectroscopy in helium nanodroplets. J. Am. Chem. Soc. 128:7320–28
     [Google Scholar]
  124. 124.
    Chin W, Mons M, Piuzzi F, Tardivel B, Gorb IL, Lesczynski J. 2004. Gas phase rotamers of the nucleobase 9-methylguanine enol and its monohydrate: optical spectroscopy and quantum mechanical calculations. J. Phys. Chem. A 108:8237–43
     [Google Scholar]
  125. 125.
    Karton A. 2019. Thermochemistry of guanine tautomers re-examined by means of high-level CCSD(T) composite ab initio methods. Austr. J. Chem. 72:607–9
     [Google Scholar]
  126. 126.
    Nejad A, Meyer E, Suhm MA. 2020. Glycolic acid as a vibrational anharmonicity benchmark. J. Phys. Chem. Lett. 11:5228–33
     [Google Scholar]
  127. 127.
    Godfrey PD, Rodgers FM, Brown RD. 1997. Theory versus experiment in jet spectroscopy: glycolic acid. J. Am. Chem. Soc. 119:2232–39
     [Google Scholar]
  128. 128.
    Kisiel Z, Pszczołkowski L, Białkowska-Jaworska E, Charnley SB. 2016. Millimetre wave rotational spectrum of glycolic acid. J. Mol. Spectrosc. 321:13–22
     [Google Scholar]
  129. 129.
    Yu W, Xu X, Li H, Pang R, Fang K, Lin Z. 2009. Extensive conformational searches of 13 representative dipeptides and an efficient method for dipeptide structure determination based on amino acid conformers. J. Comput. Chem. 30:2105–21
     [Google Scholar]
  130. 130.
    Bhattacharya A, Bernstein ER. 2011. Influence of turn (or fold) and local charge in fragmentation of the peptide analogue molecule CH3CO-Gly-NH2 following single-photon VUV (118.2 nm) ionization. J. Phys. Chem. A 115:10679–88
     [Google Scholar]
  131. 131.
    Cabezas C, Varela M, Cortijo V, Jiménez AL, Peña L et al. 2013. The alanine model dipeptide Ac-Ala-NH2 exists as a mixture of C7eq and C5 conformers. Phys. Chem. Chem. Phys. 15:2580–85
     [Google Scholar]
  132. 132.
    Cabezas C, Varela M, Alonso JL. 2013. Probing the γ-turn in a short proline dipeptide chain. Chem. Phys. Chem. 14:2539–43
     [Google Scholar]
  133. 133.
    Cabezas C, Robben MAT, Rijs AM, Peña I, Alonso JL. 2015. Fourier transform microwave spectroscopy of Ac-Ser-NH2: the role of side-chain interactions in peptide folding. Phys. Chem. Chem. Phys. 17:20274–80
     [Google Scholar]
  134. 134.
    Chakraborty D, Banerjee A, Wales DJ. 2021. Side-chain polarity modulates the intrinsic conformational landscape of model dipeptides. J. Phys. Chem. B 125:5809–22
     [Google Scholar]
  135. 135.
    Grenie Y, Avignon M, Garrigou-Lagrange C. 1975. Molecular structure study of dipeptides isolated in an argon matrix by infrared spectroscopy. J. Mol. Struct. 24:293–307
     [Google Scholar]
  136. 136.
    Blanco S, Sanz ME, López JC, Alonso JL. 2007. Revealing the multiple structures of serine. PNAS 104:20183–88
     [Google Scholar]
  137. 137.
    Wilke JJ, Lind MC, Schaefer HF III, Császár AG, Allen WD 2009. Conformers of gaseous cysteine. J. Chem. Theory Comput. 5:1511–23
     [Google Scholar]
  138. 138.
    Jaeger HM, Scheafer HF III, Demaison J, Császár A, Allen WD 2010. Lowest-lying conformers of alanine: pushing theory to ascertain precise energetics and semiexperimental Re structures. J. Chem. Theory Comput. 6:3066–78
     [Google Scholar]
  139. 139.
    Nguyen HVL, Kleiner I. 2022. Understanding (coupled) large amplitude motions: the interplay of microwave spectroscopy, spectral modeling, and quantum chemistry. Phys. Sci. Rev. 7:679–726
     [Google Scholar]
  140. 140.
    Barone V, Biczysko M, Bloino J, Puzzarini C. 2013. Glycine conformers: a never-ending story?. Phys. Chem. Chem. Phys. 15:1358–63
     [Google Scholar]
  141. 141.
    Császár A. 1996. Conformers of gaseous alanine. J. Phys. Chem. 100:3541–51
     [Google Scholar]
  142. 142.
    Meinert C, Garcia AD, Topin J, Jones NC, Dieckmann M et al. 2022. Amino acid gas phase circular dichroism and implications for the origin of biomolecular asymmetry. Nat. Commun. 13:502
     [Google Scholar]
  143. 143.
    León I, Fusè M, Alonso ER, Mata S, Mancini G et al. 2022. Unbiased disentanglement of conformational baths with the help of microwave spectroscopy, quantum chemistry, and artificial intelligence: the puzzling case of homocysteine. J. Chem. Phys. 157:074107
     [Google Scholar]
  144. 144.
    Lupi J, Puzzarini C, Cavallotti C, Barone V. 2020. State-of-the-art quantum chemistry meets variable reaction coordinate transition state theory to solve the puzzling case of the H2S + Cl system. J. Chem. Theory Comput. 16:85090–104
     [Google Scholar]
  145. 145.
    Gunasekaran S, Bright A, Renuga Devi TS, Arunbalaji R, Anand G et al. 2010. Experimental and semi-empirical computations of the vibrational spectra of methionine, homocysteine and cysteine. Arch. Phys. Res. 1:12–26
     [Google Scholar]
/content/journals/10.1146/annurev-physchem-082720-103845
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Gas-Phase Computational Spectroscopy: The Challenge of the Molecular Bricks of Life
Annual Review of Physical Chemistry 74, 29 (2023); https://doi.org/10.1146/annurev-physchem-082720-103845
/content/journals/10.1146/annurev-physchem-082720-103845
/content/journals/10.1146/annurev-physchem-082720-103845
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