Assessment of MC-PDFT Excitation Energies for a Set of QM/MM Models of Rhodopsins

María Del Carmen Marín, Luca De Vico, Sijia S. Dong, Laura Gagliardi, Donald G. Truhlar, Massimo Olivucci

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

A methodology for the automatic production of quantum mechanical/molecular mechanical (QM/MM) models of retinal-binding rhodopsin proteins and subsequent prediction of their spectroscopic properties has been proposed recently by some of the authors. The technology employed for the evaluation of the excitation energies is called Automatic Rhodopsin Modeling (ARM), and it involves the use of the complete active space self-consistent field (CASSCF) method followed by a multiconfiguration second-order perturbation theory (in particular, CASPT2) calculation of external correlation energies. Although it was shown that ARM is capable of successfully reproducing and predicting spectroscopic property trends in chromophore-embedding protein sets, practical applications of such technology are limited by the high computational costs of the multiconfiguration perturbation theory calculations. In the present work we benchmark the more affordable multiconfiguration pair-density functional theory (MC-PDFT) method whose accuracy has been recently validated for retinal chromophores in the gas phase, indicating that MC-PDFT could potentially be used to analyze large (e.g., few hundreds) sets of rhodopsin proteins. Here, we test this theory for a set of rhodopsin QM/MM models whose experimental absorption maxima (λ a max) have been measured. The results indicate that MC-PDFT may be employed to calculate λ a max values for this important class of photoresponsive proteins.

Original languageEnglish (US)
Pages (from-to)1915-1923
Number of pages9
JournalJournal of Chemical Theory and Computation
Volume15
Issue number3
DOIs
StatePublished - Mar 12 2019

Bibliographical note

Funding Information:
M.d.C.M, L.D.V. and M.O. acknowledge a MIUR grant Dipartimento di Eccellenza 2018-2022. M.O. is also grateful for Grants No. NSF CHE-CLP-1710191 and NIH GM126627 01 and for a USIAS 2015 fellowship. S.S.D, L.G. and D.G.T. acknowledge the National Science Foundation, Grant No. CHE-1464536.

Funding Information:
*(L.G.) E-mail: gagliard@umn.edu. *(D.G.T.) E-mail: truhlar@umn.edu. *(M.O.) E-mail: molivuc@bgsu.edu. ORCID María del Carmen Marín: 0000-0001-6603-1692 Luca De Vico: 0000-0002-2821-5711 Sijia S. Dong: 0000-0001-8182-6522 Laura Gagliardi: 0000-0001-5227-1396 Donald G. Truhlar: 0000-0002-7742-7294 Massimo Olivucci: 0000-0002-8247-209X Funding M.d.C.M, L.D.V., and M.O. acknowledge a MIUR grant “Dipartimento di Eccellenza 2018-2022”. M.O. is also grateful for Grants No. NSF CHE-CLP-1710191 and NIH GM126627 01 and for a USIAS 2015 fellowship. S.S.D, L.G., and D.G.T. acknowledge the National Science Foundation, Grant No. CHE-1464536. Notes The authors declare no competing financial interest.

Publisher Copyright:
© 2019 American Chemical Society.

Keywords

  • Animals
  • Bacteria/chemistry
  • Bacteriorhodopsins/chemistry
  • Databases, Protein
  • Humans
  • Models, Molecular
  • Protein Conformation
  • Quantum Theory
  • Rhodopsin/chemistry
  • Thermodynamics

PubMed: MeSH publication types

  • Journal Article

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