The FoldAffinity and MoltenProt apps

Last time updated: November 2024

Introduction

This folder contains two shiny apps developed for analyzing differential scanning fluorimetry (DSF) data. The first app, FoldAffinity, estimates binding affinities using a two-state unfolding model coupled with ligand binding. It provides one model based on fitting the isotherms and one model based on fitting the observed melting temperatures. The second app, MoltenProt, provides a user-friendly and reliable method for fitting thermal unfolding curves to different models.

The input data for both apps consists of fluorescence-based melting curves, while the output data includes the estimated parameters. Example data can be found in the 'www' folders of each app. In the case of FoldAffinity, the dissociation constant Kd is of particular interest, whereas for MoltenProt, the melting temperature and enthalpy of unfolding are the key parameters.

Getting started

To run the apps locally you need R (tested with version 4.4.0) and Python (tested with version 3.10.9). Then,

Install the required R packages (it may take a long time)

Rscript ./appFiles/install_r_packages.R

Create a Python environment

user=$(whoami) 
python3 -m venv /home/${user}/myenv

Install the required Python packages (if you prefer Conda, contact us)

/home/${user}/myenv/bin/pip install --prefer-binary --no-cache-dir -r ./appFiles/requirements.txt

Set the correct path for both apps

if [ "$(basename "$(pwd)")" = "differentialScanningFluorimetryApps" ]; then
    sed -i "0,/base_dir <- paste0/s|base_dir <- paste0.*|base_dir <- paste0('$PWD', '/appFiles/FoldAffinity/')|" appFiles/FoldAffinity/global.R
    sed -i "0,/base_dir <- paste0/s|base_dir <- paste0.*|base_dir <- paste0('$PWD', '/appFiles/MoltenProt/')|"   appFiles/MoltenProt/global.R
else
   echo "Change the working directory to differentialScanningFluorimetryApps"
fi

5a) Run FoldAffinity

cd appFiles/FoldAffinity
R -e 'shiny::runApp()'

5b) Or, run MoltenProt

cd appFiles/MoltenProt
R -e 'shiny::runApp()'

Future developments

For MoltenProt, it would be great to add support for best-model selection, e.g., to automatically tell the user if they should use a two or three states model. For FoldAffinity, to add a new feature such that users can download a PDF report.

References

Kotov, Vadim, et al. "In‐depth interrogation of protein thermal unfolding data with MoltenProt." Protein Science 30.1 (2021): 201-217.

Bai, Nan, et al. "Isothermal analysis of ThermoFluor data can readily provide quantitative binding affinities." Scientific reports 9.1 (2019): 1-15.

Niebling, Stephan, et al. "FoldAffinity: binding affinities from nDSF experiments." Scientific Reports 11.1 (2021): 9572.

Burastero, Osvaldo, et al. "eSPC: an online data-analysis platform for molecular biophysics." Acta Crystallographica Section D: Structural Biology 77.10 (2021): 1241-1250.

Acknowledgments

Packages

FoldAffinity is possible thanks to:

R language: R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

R package shiny: Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2020). shiny: Web Application Framework for R. R package version 1.4.0.2. https://CRAN.R-project.org/package=shiny

R package viridis: Simon Garnier (2018). viridis: Default Color Maps from 'matplotlib'. R package version 0.5.1. https://CRAN.R-project.org/package=viridis

R package tidyverse: Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686

R package pracma: Hans W. Borchers (2019). pracma: Practical Numerical Math Functions. R package version 2.2.9. https://CRAN.R-project.org/package=pracma

R package shinydashboard: Winston Chang and Barbara Borges Ribeiro (2018). shinydashboard: Create Dashboards with 'Shiny'. R package version 0.7.1. https://CRAN.R-project.org/package=shinydashboard

R package ggplot2: H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

R package xlsx: Adrian Dragulescu and Cole Arendt (2020). xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.3. https://CRAN.R-project.org/package=xlsx

R package reshape2: Hadley Wickham (2007). Reshaping Data with the reshape Package. Journal of Statistical Software, 21(12), 1-20. URL http://www.jstatsoft.org/v21/i12/.

R package tippy: John Coene (2018). tippy: Add Tooltips to 'R markdown' Documents or 'Shiny' Apps. R package version 0.0.1. https://CRAN.R-project.org/package=tippy

R package shinyalert: Pretty Popup Messages (Modals) in 'Shiny'. R package version 1.1. https://CRAN.R-project.org/package=shinyalert

R package plotly: C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.

R package tableHTML: Theo Boutaris, Clemens Zauchner and Dana Jomar (2019). tableHTML: A Tool to Create HTML Tables. R package version 2.0.0. https://CRAN.R-project.org/package=tableHTML

R package rhandsontable: Jonathan Owen (2018). rhandsontable: Interface to the 'Handsontable.js' Library. R package version 0.3.7. https://CRAN.R-project.org/package=rhandsontable

R package remotes: Jim Hester, Gábor Csárdi, Hadley Wickham, Winston Chang, Martin Morgan and Dan Tenenbaum (2020). remotes: R Package Installation from Remote Repositories, Including 'GitHub'. R package version 2.1.1. https://CRAN.R-project.org/package=remotes

R package devtools: Hadley Wickham, Jim Hester and Winston Chang (2020). devtools: Tools to Make Developing R Packages Easier. R package version 2.3.0. https://CRAN.R-project.org/package=devtools

R package shinyjs: Dean Attali (2020). shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds. R package version 1.1. https://CRAN.R-project.org/package=shinyjs

R package data.table: Matt Dowle and Arun Srinivasan (2019). data.table: Extension of data.frame. R package version 1.12.8. https://CRAN.R-project.org/package=data.table

R package reticulate: Kevin Ushey, JJ Allaire and Yuan Tang (2020). reticulate: Interface to 'Python'. R package version 1.16. https://CRAN.R-project.org/package=reticulate

R package shinycssloaders: Andras Sali and Dean Attali (2020). shinycssloaders: Add CSS Loading Animations to 'shiny' Outputs. R package version 0.3. https://CRAN.R-project.org/package=shinycssloaders

Python3.7 language: Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. Scotts Valley, CA: CreateSpace.

Python package numpy: Travis E, Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006). Stéfan van der Walt, S. Chris Colbert, and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37

Python package pandas: Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51-56 (2010)

Python package scipy: Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3), 261-272.

Python package xlrd: https://xlrd.readthedocs.io/en/latest/index.html

Python package natsort: https://natsort.readthedocs.io/en/master/

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MoltenProt is possible thanks to: