AMIVA-F 0.0.3

A FLNc mutation pathogenicity predictor algorithm

AMIVA-F-test package

AMIVA-F is a machine learning based algorithm, trained to correlate single point mutations with disease in FLNc.

AMIVA-F requires additionally JAVA and PYMOL installed. You need a Java Development Kit (JDK) 8 or later: https://adoptopenjdk.net/ installed and the JAVA_HOME environment variable pointing to the installation directory. https://docs.oracle.com/cd/E19182-01/820-7851/inst_cli_jdk_javahome_t/index.html

Ideally you would download JAVA 8/11 given that future versions will cause problems with accessibility (currently not solved bug).

PYMOL: Pymol can be downloaded from https://pymol.org/2/ Select your appropriate OS version and make sure to set PATH accordingly after installation in order for your Interpreter to find PYMOL. Pymol comes with its own interpreter , so be careful to not work in between 2 interpreters.

Setup from anaconda(windows10):

First download Anaconda from https://www.anaconda.com/products/individual

Afterwards open the anaconda prompt (anaconda3) and simply type:

conda create -n amivaenv python=3.8

This creates a new virtual environment with python 3.8 named amivaenv which will be used to install AMIVA and its dependencies without polluting your local pythonspace. After creation enter:

conda activate amivaenv

Which will then activate the new environment.

Usage of AMIVA-F

AMIVA-F works fully automated and is easy to use, even in the absence of knowledge about the underlying parameters which are used as input for the neural network.

Step 1)

AMIVA-F works at the protein annotation level, therefore if you have mutations of interest in the c notation (DNA), look up the corresponding p.notation.

Once you have your mutation of interest in protein notation, enter it in the entry field location directly above the green button ("Calculate everything for me!"). The required input should look like this:

M82K             

This input would correspond to the single point mutation at position 82 in FLNc, where the wildtype amino acid (M, Methionine) is substituted by the mutated amino acid (K, Lysine). If you by any chance submit a wrong amino acid (the amino acid you specified for the wildtype position is in fact not what you submitted, e.g FLNc position 82 corresponds to methionine, but you wrote S82K, which would correspond to serine), then AMIVA-F automatically corrects you and offers you to proceed calculations with the correct amino acid in in place.

Step 2)

After you entered the mutation of interest e.g M82K into the entry field specified above, click the green button ("Calculate everything for me!") This button will then automatically grab the correct model structure where your amino acid is located and calculate all input parameters required to predict the pathogenicity of the mutation. Usually this process is really fast, you will see all entry fields filled and you should normally just check if there is anything left blank. The 2 last rows in the entryfield (Found posttranslational modification sites, and additional information) are solely there to inform you about potentially interesting sited in close proximity (8Angström cutoff) of the desired mutation spot. If you are working by any chance on posttranslational modifications or you possess information about additional binding partners, feel free to add them to the library files ( Posttranslational_modifications_and_binding_partners\Binding_partners_list.txt and Posttranslational_modifications_and_binding_partners\Posttranslational_modification_list.txt) which will be taken into account when filling out the input parameters.

Step 3)

Check if every entry field in the form is filled and every radiobutton is selected. If everything seems fine, proceed by clicking the blue button ("Generate template file"). This will prepare a specific input parameter file which will then be placed into the correct directory and can be directly used for further prediction by AMIVA-F

Step 4)

Click the red button ("Prediction on pathogenicity") and wait a couple of seconds. In the background, AMIVA-F trains itself with 10x cross validation with additional stratification (details can be seen later in the Trainingset info section of the neighbouring button). This process takes a couple of seconds but afterwards you should see the following entries: (In the examplary case of M82K input)

More information can be found at the full tutorial inside the package.