Maria Garcia-Alai
Project leader
This website uses cookies, and the limited processing of your personal data to function. By using the site you are agreeing to this as outlined in our Privacy Policy.
Welcome to PhotoMol! This website is free for academic use. By clicking "I agree", you agree to the Academic Software License Agreement. For non-academic use, please contact EMBLEM. Please remember to cite the PhotoMol publication. Additionally, please consider starring our GitHub repository.
Input
Mass Photometry file(s) (up to eight)
Error:
Bin width
Min. observed mass
Upper limit for the standard deviation
52000030525456585105125145165185200
Tolerance to the initial guesses
12000030121416181101121141161181200
Starting values
Baseline
Starting values (file 2)
Starting values (file 3)
Starting values (file 4)
Starting values (file 5)
Starting values (file 6)
Starting values (file 7)
Starting values (file 8)
Window range
00000 — 0
Slider left limit
Slider right limit
Automatic fitting
Option 1 - MP mass distribution histogram and Gaussian fit of the sample XX. The plot and analysis was done with the PhotoMol tool (spc.embl-hamburg.de).
Option 2 - Mass distribution fitting of sample Y ( © eSPC, spc.embl-hamburg.de).
Option 1 - MP binding and unbinding events histogram. Plot was generated using the PhotoMol tool (spc.embl-hamburg.de)
Option 2 - MP binding and unbinding events histogram ( © eSPC, spc.embl-hamburg.de).
Fitted parameters and counts
Legends
X
Show estimated masses
Show counts percentage
Plot download options
Width
Height
File type
PNG
Axis text size
Show
Simulation
Activate
Position
Std.
Amplitude
Left limit
Export H5 dataset
Input
Activate calibration
Calibration method
Calibration file
Slope * 1e6
Intercept * 1e6
MassPhotometry file
Error:
Bin width * 1e3
Initial guesses * 1e3
Known masses
Window range * 1e3
-1,0000-6000-600 — 0-1,000-900-800-700-600-500-400-300-200-1000
Slider left limit
Slider right limit
Fitted parameters and counts
Legends
X
Show estimated contrasts
Plot download options
Width
Height
File type
PNG
Axis text size
Show
PhotoMol accepts as input a '.h5' (Hierarchical Data Format) file (demo.h5). This file should have one 1D dataset called 'masses_kDa' and can be exported using the software Refeyn DiscoverMP. In the DiscoverMP version < 2.5, the file eventsFitted.h5 is saved in the folder when saving the results. In version 2.5 the events can be exported individually selecting a custom file name. For calibration, only a dataset called 'contrasts' is needed. If the dataset named 'masses_kDa' is not present, but a dataset containing calibration parameters called 'calibration' exists, the contrasts will be converted to masses.
Additionally, a (csv) (comma-separated values) file with headers can be loaded. The column 'masses_kDa' and 'contrasts' are respectively required for the mass distribution data analysis and calibration.
Firstly, we build a histogram based on the observed masses, the bin width and the window range. Secondly, we fit a multi-gaussian function to the histogram based on a user-defined number of truncated Gaussians:
counts=baseline+n∑i=1gi(mass)where n is the number of one-side (of lower tail) truncated Gaussians gi(), and baseline is defined by the user. The parameter 'Minimum observed mass' (kDa) can be used to set the limit for the truncated gaussians. We recommend to use 30 kDa, but this value depends on the instrument.
For quality check purposes, unbinding events (negative masses) can also be fitted by changing the range of analysis. Peaks that have a similar number of counts in both the binding and unbinding regions are indicative of artifacts.
The fitted means and standard deviations are shown in the 'Fitted parameters and counts' Table using as column names 'Position / kDa' and 'Sigma / kDa', respectively. The number of counts (and percentage from total) under each fitted gaussian is also presented.
If the '.h5' (Hierarchical Data Format) file only contains information about the ratiometric contrasts, it is still possible to convert them to masses by a) loading a '.h5' file with known masses (3 different species at least), or using parameters from a previous calibration. In both cases, the calibration experiment should have been done with the same buffer, at the same temperature, and using the same instrument parameters (i.e., the field of view)
a) Load the calibration file in the 'Calibration' panel, write the known masses in the 'Known masses' input box, and fill the 'Initial guesses' input box. Check the fitting in the 'Masses versus Contrast' plot, and the parameters in the 'Calibration parameters Table. In this case, the data is fitted using non-truncated gaussians.
b) Use previously known parameters obtained by fitting the function of a line to the mass versus contrast curve, i.e., contrast = f(mass) = mass*slope + intercept.
Objective
The main objective of PhotoMol is to estimate the masses of different species in a sample by analyzing mass photometry data. PhotoMol is provided by the Sample Preparation and Characterization (SPC) Facility from the European Molecular Biology Laboratory (EMBL) Hamburg Station.
The team
Stephan Niebling
Project developer
Osvaldo Burastero
Project developer
George Draper-Barr
Project advisor
Contact
Please address all email correspondence to spc@embl-hamburg.de
Citations and references
If PhotoMol has been useful for your project, please cite us and consider starring our Github. Thank you!
Niebling, Stephan, et al. "Biophysical screening pipeline for cryo-EM grid preparation of membrane proteins." Frontiers in Molecular Biosciences: 535.