Direct reading of Microcal Origin OPJ files with AFFINImeter

Direct reading of Microcal Origin OPJ files with AFFINImeter

The direct reading  Microcal Origin OPJ files has been recently implemented in AFFINImeter. Initially, only OPJ files containing a single datasheet were allowed. Our code has just been modified to make possible the reading of OPJ files with multiple datasheets. Additionally the uploaded OPJ files are now being filtered to permit just the selection of datasheets suitable for analysis with AFFINImeter (i.e. datasheets of ITC isotherms). Some minor improvements that optimize this process have also been implemented.
Soon you will also be able to directly upload Microcal Origin itc files. In collaboration with our scientific advisor Prof. Philippe Dumas, from the University of Strasbourg, we are currently implementing a procedure to automate the integration of the raw ITC data, including removal of noise and baseline correction.

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AFFINImeter Beta Version Release

AFFINImeter is the most complete software for Isothermal Titration Calorimetry data Analysis. You can easily  build your own Binding Models or Perform Global Simultaneous Analysis of several Isotherms, among other features.

Here is the list of main implementations and changes in the released Beta version of AFFINImeter:

  1. A new model family, for Isothermal Titration Calorimetry Data Analysis, that considers the independent binding of one or two (competitive) different compounds to a macromolecule has been implemented. This is a generalization of the so called “two sets of independent sites (TSIS)” models that may consider any number of different and independent sets with any number of identical and independent sites per set.
  2.  Binary origin files (with OPJ extension) coming from microcal ITCs can now be directly uploaded. This facilitates the work by avoiding intermediate files and potential errors in the format. TA experiments can also be uploaded as in the previous version.
  3.  Simulation and Fitting results can be easily shared by e-mail. AFFINImeter is the only software able to make simulations for any model. This tool is free for any registered user. Simulations are extremely useful in several situations: (i) to optimize the experimental setup of an experiment; (ii) once a system has been analyzed it allows predicting the conditions under which the distribution of chemical species meet some special requirement (for instance, the solution dominated by a given chemical species); and (iii) for didactic purposes, to illustrate how a chemical species can be displaced by another, to explain the difference between cooperative and non-cooperative processes or to explain the effect of endothermic and exothermic processes
  4. Several optimizations, including calculations and also the protection of data, have been performed. Some occasional noise was detected in the calculated fitting curve of the previous version of AFFINImeter. This is connected to the noise of the experimental data points and also to the approach employed to correct for the displacement of volume in Microcal and TA ITC instruments. A new algorithm was implemented to globaly fit all the data points, thus minimizing this kind of problems
  5. It is possible to remove data points when editing dataseries as well as when uploading them to a project. This is really useful to make several tests of the same dataseries by removing different points on the experimental curve.

Functionalities of the previous version of AFFINImeter are still active, with AFFINImeter you can:

  • Design your own sequential binding model by using our model builder tool. It is possible to design models, from an Isothermal Titration Calorimetry experiment, involving up to three different compounds with two ligands that compete with each other to bind a macromolecule. The second ligand may be in the cell or/and in the syringe. Dissociation of any order (from simple homodimers to complex hetero-oligomers) induced by dilution experiments can also be easily analyzed.
  • Analyze the presence of local minima by repeating fittings starting from different random seeds for the parameters
  • Include dynamic relationships between parameters
  • Include dynamic bounds to restrain the fitting parameters
  •  Correct for the effective concentration of any of the compounds of the experiment by fitting a scaling factor
  • Fit several curves simultaneously to minimize the statistical uncertainty of the fitting parameters
  • Manage associate accounts for your students and collaborators. Administrator account owners will have access to the full activity of their associated users
  •  Share your results by e-mail with your collaborators. They will get direct access to the measurements, fitting curve and fitted parameters of the experiments you decide to share with your colleagues.
  • Organize your projects and access them from any device