TY  - JOUR
AU  - Hernandez, Altair C
AU  - Ortiz, Sebastian
AU  - Betancur, Laura I
AU  - Dojčilović, Radovan
AU  - Picco, Andrea
AU  - Kaksonen, Marko
AU  - Oliva, Baldo
AU  - Gallego, Oriol
PY  - 2024
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/13085
AB  - Structural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software programs that challenge reproducibility and limit their implementation to highly specialized researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F’s performance.
T2  - NAR Genomics and Bioinformatics
T1  - PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments
VL  - 6
IS  - 1
DO  - 10.1093/nargab/lqae027
ER  - 

@article{
author = "Hernandez, Altair C and Ortiz, Sebastian and Betancur, Laura I and Dojčilović, Radovan and Picco, Andrea and Kaksonen, Marko and Oliva, Baldo and Gallego, Oriol",
year = "2024",
abstract = "Structural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software programs that challenge reproducibility and limit their implementation to highly specialized researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F’s performance.",
journal = "NAR Genomics and Bioinformatics",
title = "PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments",
volume = "6",
number = "1",
doi = "10.1093/nargab/lqae027"
}

Hernandez, A. C., Ortiz, S., Betancur, L. I., Dojčilović, R., Picco, A., Kaksonen, M., Oliva, B.,& Gallego, O.. (2024). PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments. in NAR Genomics and Bioinformatics, 6(1).
https://doi.org/10.1093/nargab/lqae027

Hernandez AC, Ortiz S, Betancur LI, Dojčilović R, Picco A, Kaksonen M, Oliva B, Gallego O. PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments. in NAR Genomics and Bioinformatics. 2024;6(1).
doi:10.1093/nargab/lqae027 .

Hernandez, Altair C, Ortiz, Sebastian, Betancur, Laura I, Dojčilović, Radovan, Picco, Andrea, Kaksonen, Marko, Oliva, Baldo, Gallego, Oriol, "PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments" in NAR Genomics and Bioinformatics, 6, no. 1 (2024),
https://doi.org/10.1093/nargab/lqae027 . .