AFFINImeter new version!

During the last months, we’ve contacted you asking your opinion and experience with the software. Thanks for all your suggestions and needs we have improved the previous version of the software to make it easier, faster and more versatile.

What is new in this release?

  • In collaboration with Mestrelab Software, AFFINImeter can now analyze Nuclear Magnetic Resonance (NMR)  titration curves.

  • The Main toolbar has been simplified:

Data” & “Projects” have been merged into a single “Projects & Data*” menu

You can filter the elements to show by selecting “Projects & Data” (default), only “Projects” or only “Data“.

A new option has been included to easily pre-visualize the settings of your experiments directly from the list of data & projects.

The “Instruments” management menu has been removed.

*Data elements are represented by this icon –>  
*Projects elements are represented by this icon–> 

 

  • Now you can organize all your data and projects into folders:

 

  • Organize your data & projects as you would do on your computer.
  • Looking for your already existing KinITC data? Go to Projects & Data, and look into the folder “KINITC”.
  • A new parameter has been introduced to measure the quality of the FIT: Goodness of Fit

The GoF (Goodness of Fit) is the probability of finding the fitted value within a normal distribution with half-width equal to the uncertainty of each experimental point. The GoF for a curve is obtained as the mean GoF of all its points. The ideal value of perfect fit would be Gof=100%.

This parameter will be available when you execute a new FIT project/or re-execute an older project. If you visualize an already executed project, the previous parameter (χ 2) still will be present.

What have we improved from the previous version?

  • We have incorporated the possibility to perform blank subtraction right after raw data upload and processing.
  • Multiple minor usability improvements and performance tweaks.
  • Multiple bug fixes and issues suggested by our users have been implemented and resolved.

 

Try the new AFFINImeter here

The stoichiometric equilibria approach.

Following the last part of this course, we want to introduce the second fitting approach that can be performed with AFFINImeter:

The Stoichiometric equilibria approach.

This approach uses binding models based on equilibria between stoichiometric species and yields stoichiometric binding constants. In AFFINImeter, traditional and tailored stoichiometric binding models can be easily generated with the “model builder” tool.
Stoichiometric binding models are applicable to a great variety of complex interactions like:

  • A ligand binding to a multi-site receptor with known stoichiometry, where the sites can be independent or cooperative.
  • Competing ligands binding to the same receptor.
  • Oligomerization.

….among others.

The appropriate design and use of binding models in AFFINImeter pass through an understanding of the nomenclature that the software uses to describe the species located in the calorimetric cell and in the syringe.
In the following link you can visualize a quick video about the Nomenclature of the Model model builder:

Nomenclature of Model Builder.

The last video of this course is about how to use the stoichiometric equilibria approach to perform an individual and global analysis.

The course about ITC data analysis comes to an end so if you want to try the versatility of AFFINImeter you can register to get a free trial during 1 month:

 

Get a free trial

The course of Isothermal Titration Calorimetry data analysis: second part

In the second part of this course, we are going to show you how to perform the analysis of binding isotherms considering an independent sites approach.
This approach uses a reaction scheme based on the binding of the ligand to individual sites present in the receptor and considering that all the sites are independent; thus, it supplies site binding constants.
This approach offers a sole reaction scheme where a receptor with a certain number of sites “n” binds to the ligand.
The sites are grouped into sets to discern between sites that are non-equivalent.

If you want to know more about how to get the stoichiometry (number of sites) and site binding constants with the independent sites approach you can click here:

Stoichiometric and site constants – two approaches to analyze data with AFFINImeter

The first video tutorial presented is about how to use an independent sites approach to perform fittings:

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Into another subject, to introduce the second fitting approach that can be performed with AFFINImeter (Stoichiometric equilibria), we will describe how to use the model builder.
This original tool allows to design and apply your own binding model in an easy way. Check the following video to know the versatility of the model builder:

Finally, if you want to try the Model Builder click here:

Model Builder

 

The course of Isothermal Titration Calorimetry data analysis

Isothermal Titration Calorimetry (ITC) is widely considered the gold standard technique for the direct determination of binding thermodynamics.
Nowadays, getting kinetic information directly from the thermogram is also possible thanks to the method KinITC implemented in AFFINImeter. Besides KinITC, AFFINImeter offers a number of advanced analytical tools to take the most out of ITC data.

Aiming to show researchers how to make use of these tools, we have prepared a course on ITC data analysis, consisting of a series of video tutorials imparted by Eva Muñoz (Senior Scientist at AFFINImeter) and that we will release in three parts.

The following picture shows the scheme of the course:

The first part, we present three video tutorials dedicated to the analysis of ITC data with KinITC and the multi-temperature tools. Here we are attaching the video tutorials.

  1. Introduction-KinITC automatic processing
  2. KinITC Manual process
  3. Multi-temperature analysis

In the following parts of this course, we will be focused on the design of two binding models for ITC curve fitting with AFFINImeter:

Una spin-off de la USC, AFFINImeter, seleccionada por Google para participar en el programa Campus Remote Mentoring.

El Campus Remote Mentoring de Google potencia el crecimiento de nuevas empresas con sesiones de mentorship personalizadas, networking y acceso a nuevos mercados.

AFFINImeter es una spin-off de la Universidad de Santiago de Compostela dedicada al desarrollo de software científico y metodologías para investigación química y farmacéutica en el campo de interacciones moleculares. Entre sus clientes se encuentran diversas farmacéuticas como Bayer, Roche o la filial farmacéutica de Mitsubishi además de prestigiosas universidades de Europa, Japón y Estados Unidos.

El pasado mes de marzo,  AFFINImeter fue seleccionada para participar en el exclusivo programa “Campus Remote Mentoring” de Google, una iniciativa orientada a irradiar la influencia  del campus de Google en Madrid para el resto de start up dentro del territorio español. El programa incluye una tutorización desde la sede de Google en Dublín, y se centra en mejorar aspectos relacionados con el Marketing on-line y la comunicación digital, con el fin de potenciar la internalización de la start up.

Paralelamente AFFINImeter ha sido seleccionada en el prestigioso programa de aceleración de ViaGalicia, promovido por el Consorcio de la Zona Franca y la Xunta de Galicia (a través de la Agencia Gallega de Innovación GAIN y la sociedad de capital-riesgo XesGalicia).

MicroCal User´s day in Madrid

The next April 19th will be taking place in Madrid the MicroCal user´s day. The event will be held at the Institute of Chemical Physics Rocasolano.

The basics and applications of the ITC -Isothermal titration Calorimetry and DSC -Differential scanning calorimetry technologies will be treated. It will be an excellent event with very interesting talks and nice place for catch up of these applications.

We are pleased to announce that Dr Juan Sabín (Co-founder & Product Designer) will be giving a presentation about AFFINImeter, the software for Isothermal Titration Calorimetry data analysis.

Additionally, Dr Raúl Pacheco application specialist from Malvern Instruments, Dr Margarita Menendez from the Institute of Chemical Physics Rocasolano and Dr Javier Murciano from the University of Granada will also attend the event.

The Microcal user´s day has been organised by Malvern Instruments, CSIC-Spanish National Research Council, Institute of Chemical Physics Rocasolano and Iesmat.

For more information:

Microcal user´s day Madrid.

We will be attending the Fragments 2017 – 6th RSC-BMCS Fragment-based Drug Discovery meeting

 

On March 5th to 7th we will be attending the Fragments 2017 – 6th RSC-BMCS Fragment-based Drug Discovery meeting

AFFINImeter will be attending the congress and will have a stand and a poster for the duration of the congress

We will be there sharing ideas on new applications and challenges on Isothermal Titration Calorimetry (ITC) experiments and data analysis. We will be pleased to talk to you. Contact us.

You won´t miss this event

 

We want to thank Xunta de Galicia, IGAPE and Fondo Europeo de desenvolvemento rexional for giving us this opportunity.

AFFINImeter has been certified to run on Windows 10

AFFINImeter is supported by Software 4 Science Development on the following editions of Internet Explorer –  Explorer 11 and Microsoft Edge and is supported on all currently supported servicing branches of Windows 10.
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* AFFINImeter already runs in Linux and Mac OS X ® operating systems, using latest versions of Firefox ® and/or Chrome ® browsers. Others browsers may be supported.
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Checking the quality of the fittings in AFFINImeter

As important as obtaining a set of parameters corresponding to a model able to describe the experimental data is to ensure, with a certain statistical probability, that the analysis is correct. Often, this is a complicated task. AFFINImeter´s philosophy is to make things simple. We have developed a number of mathematical and statistical tools that are useful to assess how robust your analysis is.

The next paragraphs will explain the meaning of some parameters implemented in AFFINImeter and how to interpret them in terms of the quality of the analysis.

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What is the meaning of Standard errors in AFFINImeter?

Standard errors come from the variance-covariance matrix obtained from the partial derivatives of the objective function with respect to the fitting parameters. These are the errors commonly determined in nonlinear fitting algorithms based on derivatives, such as the Levenberg-Marquardt method that we employ in combination with the Simulated Annealing method to optimize the efficiency of the minimization process.

What is the meaning of Statistical errors in AFFINImeter?

Statistical errors are determined from the standard deviation of the parameters obtained by repeating the fitting as many times as experimental points are available in the target data series, each time ignoring one point in these extra fittings. For instance, in a binding isotherm consisting of 30 points, all of them are employed in the main minimization process (weighted by their corresponding uncertainty). The resulting parameters are used as seeds for 30 independent fittings (of 29 points each) where the data points are sequentially removed one at a time. In this case, AFFINImeter gets 30 independent solutions for each parameter (in addition to the one provided by the main fitting). The removal of the points that weight a lot in the fittings induces a significant change in the parameters that, in its turn, increases the standard deviation (the statistical error in AFFINImeter). This is the so-called  Jackknife method.

By default, the statistical uncertainties are activated in AFFINImeter but we do provide also the standard errors in order to have a useful alternative value for the uncertainty of the fitting parameters.

What is the meaning of Local Minima table in AFFINImeter?

Additional useful information to be considered in the resulting analysis with AFFINImeter is the local minima table. This table provides an estimation on how the results obtained depend on the initial values (seeds) of the fitting algorithm. AFFINImeter executes several minimizations runs (20 by default) for each analysis and it selects the values that provide the best fitting (lowest c2 value). A robust fitting is achieved when the same results are reached independently of the random initial seeds. If the local minima table shows different results for the different runs of the algorithm with similar c2 values, the analysis is probably overparameterized. In that case, it will necessary to simplify the model, thus reducing the number of fitting variables, or to include additional data series for a global fitting, thus reducing the number of degrees of freedom in the analysis.

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