PyFREC 2.0: Software for excitation energy transfer modeling.


Journal

Journal of computational chemistry
ISSN: 1096-987X
Titre abrégé: J Comput Chem
Pays: United States
ID NLM: 9878362

Informations de publication

Date de publication:
15 07 2022
Historique:
revised: 21 04 2022
received: 08 03 2022
accepted: 28 04 2022
pubmed: 25 5 2022
medline: 15 6 2022
entrez: 24 5 2022
Statut: ppublish

Résumé

Excitation energy transfer is a ubiquitous process of fundamental importance for understanding natural phenomena, such as photosynthesis, as well as advancing technologies ranging from photovoltaics to development of photosensitizers and fluorescent probes used to explore molecular interactions inside living cells. The current version of PyFREC 2.0 is an advancement of the previously reported software (D. Kosenkov, J. Comput. Chem. 2016, 37, 1847-1854). The current update is primarily focused on providing a computational tool based on Förster theory for bridging a gap between theoretically calculated molecular properties (e.g., electronic couplings, orientation factors, etc.) and experimentally measured emission and absorption spectra of molecules. The software is aimed to facilitate deeper understanding of photochemical mechanisms of fluorescence resonance energy transfer (FRET) in donor-acceptor pairs. Specific updates of the software include implementations of overlap integrals between donor emission and acceptor absorption spectra of FRET pairs, estimation of Strickler-Berg fluorescence lifetimes, calculation of Förster radii, energy transfer efficiency, and radiation zones that, in particular, determine applicability of the Förster theory.

Identifiants

pubmed: 35608241
doi: 10.1002/jcc.26930
doi:

Substances chimiques

Fluorescent Dyes 0

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1320-1328

Subventions

Organisme : American Chemical Society Petroleum Research Fund
ID : #58019-UR6
Organisme : National Science Foundation
ID : CHE-1955649 RUI-D3SC

Informations de copyright

© 2022 Wiley Periodicals LLC.

Références

D. Kosenkov, J. Comput. Chem. 2016, 37, 1847.
Y. Kholod, M. DeFilippo, B. Reed, D. Valdez, G. Gillan, D. Kosenkov, J. Comput. Chem. 2018, 39, 438.
Y. K. Kosenkov, D. Kosenkov, J. Chem. Phys. 2019, 151, 144101.
G. Linden, L. Zhang, F. Pieck, U. Linne, D. Kosenkov, R. Tonner, O. Vazquez, Angew. Chem. Int. Ed. 2019, 58, 12868.
Q. Qi, M. Taniguchi, J. S. Lindsey, J. Chem. Inf. Model. 2019, 59, 652.
B. W. van der Meer, in FRET−Förster Resonance Energy Transfer (FRET)−From Theory to Applications (Eds: I. Medintz, N. Hildebrandt), Wiley VCH, Weinheim 2014; Chapter 3, p. 23.
S. J. Strickler, R. A. Berg, J. Chem. Phys. 1962, 37, 814.
M. Taniguchi, H. Du, J. S. Lindsey, Photochem. Photobiol. 2018, 94, 277.
M. Taniguchi, J. S. Lindsey, Photochem. Photobiol. 2018, 94, 290.
J. C. T. Carlson, L. G. Meimetis, S. A. Hilderbrand, R. Weissleder, Angew. Chem. Int. Ed. 2013, 52, 6917.
F. Schweighöfer, L. Dworak, C. A. Hammer, H. Gustmann, M. Zastrow, K. Rück-Braun, J. Wachtveitl, Sci. Rep. 2016, 6, 28638.
T. Förster, Naturwissenschaften 1946, 33, 166.
T. Förster, Faraday Discuss. 1959, 27, 7.
T. Förster, Delocalized Excitation and Excitation Transfer, Florida State University, Tallahassee, FL 1965.
C. Curutchet, B. Mennucci, Chem. Rev. 2017, 117, 294.
V. May, O. Kühn, Charge and Energy Transfer Dynamics in Molecular Systems, 3rd ed., Wiley VCH, Weinheim 2011.
T. Renger, Photosynth. Res. 2009, 102, 471.
C. Curutchet, G. D. Scholes, B. Mennucci, R. Cammi, J. Phys. Chem. B 2007, 111, 13253.
B. Cohen, C. E. Crespo-Hernandez, B. Kohler, Faraday Discuss. 2004, 127, 137.
B. W. van der Meer, D. M. van der Meer, S. S. Vogel, FRET−Förster Resonance Energy Transfer (FRET)−From Theory to Applications. in (Eds: I. Medintz, N. Hildebrandt), Wiley VCH, Weinheim 2014; Chapter 4, p. 63.
J. S. Lindsey, M. Taniguchi, D. F. Bocian, D. Holten, Chem. Phys. Rev. 2021, 2, 011302.
The Python Standard Library Accessed March 2, 2022. https://docs.python.org/2.7/library/
NumPy Accessed March 2, 2022. https://numpy.org/
SciPy Accessed March 2, 2022. https://www.scipy.org/
D. Magde, R. Wong, P. G. Seybold, Photochem. Photobiol. 2002, 75, 327.
A. S. Kristoffersen, S. R. Erga, B. Hamre, Ø. Frette, J. Fluoresc. 2014, 24, 1015.
N. Boens, W. Qin, N. Basarić, J. Hofkens, M. Ameloot, J. Pouget, J.-P. Lefèvre, B. Valeur, E. Gratton, M. V. Ven, N. D. Silva, Y. Engelborghs, K. Willaert, A. Sillen, G. Rumbles, D. Phillips, A. J. W. G. Visser, A. van Hoek, J. R. Lakowicz, H. Malak, I. Gryczynski, A. G. Szabo, D. T. Krajcarski, N. Tamai, A. Miura, Anal. Chem. 2007, 79, 2137.
Y. Kubota, in Progress in the Science of Functional Dyes (Eds: Y. Ooyama, S. Yagi), Springer, Singapore 2021, p. 119.
R. M. Hochstrasser, D. S. King, A. B. Smith III, J. Am. Chem. Soc. 1977, 99, 3923.
D. Cao, L. Zhu, Z. Liu, W. Lin, J Photochem Photobiol C: Photochem Rev 2020, 44, 100371.

Auteurs

Dmitri Kosenkov (D)

Department of Chemistry and Physics, Monmouth University, West Long Branch, New Jersey, USA.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Cephalometry Humans Anatomic Landmarks Software Internet
Humans Algorithms Software Artificial Intelligence Computer Simulation

Classifications MeSH