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Method Article

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

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DOI:

10.3791/67833

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June 6th, 2025

 , 
1Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Üsküdar University

In This Article

Summary

This study introduces a multiscale framework, spanning from DNA to protein function and neural behavior. It presents a novel approach for investigating predicted pathogenic mutations in the GABAA receptor subunit, hypothesizing that epileptogenic mutations and proximal mutations, predicted as pathogenic, may produce similar effects on the CA1 pyramidal neuron model.

Abstract

Understanding the effects of functionally unknown variants in epilepsy associated genes is crucial for elucidating disease pathophysiology and developing personalized therapeutics. With a multiscale framework, spanning from DNA sequence to protein function and neural behavior, we describe a novel approach for predicting and investigating pathogenic mutations, hypothesizing that epileptogenic mutations in the GABAA receptor subunit and nearby predicted mutations may produce similar effects on the CA1 pyramidal neuron model. By exploring the characteristic relationships between predicted pathogenic mutations and proximal epileptogenic mutations, the study aims to estimate the effects of predicted mutations based on the effects of epileptogenic mutations on hippocampal pyramidal neuron simulations.

The methodology begins with the collection of GABAA receptor γ2 subunit genetic data, followed by data cleaning and formatting performed in R using a custom script. Next, ensemble predictors will be applied to identify and prioritize the pathogenic missense variants of the γ2 subunit. Mapping a specific pathogenic variant (predicted) to the subunit structural domains shared by epileptogenic mutations will be illustrated, accompanied by molecular modeling of their effects and consideration of evolutionary conservation. Then, variant-specific meta-analysis and parameter normalization will be performed, followed by correlation analysis to identify any significant relationships between predicted mutations and proximal epileptogenic mutations. Using a Python-based neural simulator, multi-compartmental conductance-based neuron model, reflecting the effect of wild-type and epileptogenic mutants will be described. Simulation of neural responses generated by epileptogenic GABAA receptor subtype will be considered for the rough estimation of the predicted pathogenic variants' effect on neural response. To our knowledge, this is the first protocol exploring a multiscale framework to estimate the effects of GABAA receptor variants on neuronal behavior, crucial for epilepsy research. This protocol can serve as a foundation for enhancing predictions of cellular phenotypes caused by potentially pathogenic variants of GABAA receptors associated with epilepsy.

Introduction

For nearly all human diseases, genetic variation plays a significant role in individual susceptibility. Therefore, understanding how sequence variations relate to disease risk offers a valuable way to uncover key processes involved in disease development and identify new approaches for prevention and treatment1. This also applies to neurodevelopmental disorders, which rank among the most prevalent chronic medical conditions in pediatric primary care2. Conditions such as autism spectrum disorder, intellectual disability, and epilepsy illustrate how genetic variation significantly influences individual susceptibility during development3.

The developing brain is more susceptible to epileptic seizures than the adult brain due to genetically programmed neurodevelopmental mismatch in the critical balance between excitation and inhibition4. As GABA (gamma-aminobutyric acid), the primary inhibitory neurotransmitter in the adult brain, is excitatory during embryonic and early postnatal development, this is not favorable to the stability needed to prevent seizures in young brains. This temporary state, caused by the lack of sufficient expression of K-Cl co-transporters5, can contribute to an increased risk of seizure activity in the presence of dysfunctional GABAA receptors. GABAA receptors mediate excitatory and inhibitory actions of GABA, depending on the intracellular concentration of the Cl- ion6. Thus, as the brain matures, mutations in the GABAA receptor-encoding genes, as well as in other ion channels, distort excitability, and mutations in genes involved in neuronal metabolism, cell signaling, and synapse formation7, can cause conditions like childhood absence epilepsy8.

Clinical interventions are increasingly leveraging genetic analysis to improve precision in treating neurodevelopmental disorders2. Genetic testing in pediatric epilepsy presents potential targets for precision medicine approaches9, highlighting the significance of genetic variants in guiding treatment decisions. In addition, ~25% of epilepsy patients with de novo mutations receive genetic diagnoses that identify potential targets for precision medicine, underscoring the significant value of genetic variants in guiding treatment decisions10. This has been fueled by advancements in next-generation sequencing technologies, such as targeted gene panels, whole-exome sequencing, and whole-genome sequencing, which have dramatically accelerated genetic discoveries11. However, the increasing number of new gene discoveries comes with a challenge when results yield a variant of unknown significance (VUS), a classification that reflects conflicting evidence or insufficient information regarding the variant's molecular role in disease pathogenesis. Variants classified as VUS correspond to one category within the five-tier variant classification system proposed by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP)12.

Addressing the challenge of functionally unknown genetic variants requires efforts across two key dimensions: clinical practice and research. Clinically, the uncertainty surrounding VUS can complicate patient management and decision-making13. From a scientific research perspective, identifying pathogenic variants among the increasing number of variants of uncertain significance and determining their roles in disease pathophysiology and phenotypic effects are crucial1. One ideal scenario would involve accurately predicting the molecular, neuronal, and network-level effects of all functionally uncharacterized variants, thereby minimizing the resources, time, and effort required for laboratory-based investigations. These aspects underscore the importance of accurately classifying genetic variants to enable precise diagnosis of genetic epilepsies, support personalized treatment, and facilitate the discovery of potential pharmacological targets. Current predictive tools14,15,16,17 are relatively accurate but typically provide only binary classifications (pathogenic vs. benign) and lack disease-specific insights into molecular pathophysiology, phenotypic consequences, and underlying mechanisms. Focusing on the unknown missense variants of selected GABAA receptor subunit-encoding genes, this paper presents a framework aimed at enhancing research guidance by incorporating contextual factors of variants such as molecular, evolutionary, and structural aspects, as well as simulations of neural pathology derived from in vitro biophysical data of epilepsy-associated mutations. Our methodology addresses the identification of unknown pathogenic variants of the γ2 subunit of the GABAA receptor, a key subunit involved in the pathophysiology of epilepsy18,19,20. This is followed by the exploration of position-specific matching of these predicted variants with the epilepsy-associated mutations characterized by structural and electrophysiological data. These data are then used to estimate the variant effect on a model of hippocampal pyramidal neuron expressing a GABAA receptor subtype, composed of γ2, α1, and β3 subunits (γ2-GABAA receptors), responsible for fast synaptic inhibition6. It is important to note that GABAA receptors assemble from a large subunit pool (α1-α6, β1-β3, γ1-γ3, δ, Ε, θ, π, and ρ1-ρ3) and depending on the subunit composition, GABAA receptors differ in their modulation, biophysical characteristics, as well as regional, cellular, and subcellular expression patterns coupled with specific functions6,21,22,23,24,25. Thus, the present study focuses on the γ2-GABAA receptors or γ2-containing GABAA receptors only.

GABAA receptor subunits are composed of characteristic structural features-a long N-terminal extracellular domain (ECD), four transmembrane spanning domains (TM1 to TM4), an intracellular linker connecting the TM1 and TM2, an extracellular linker connecting the TM2 and TM3, a large intracellular loop between TM3 and TM4 (TM3-TM4 loop), and a short extracellular C terminus6,26,27. It is suggested that the GABAA receptor functions via a complex "lock and pull" mechanism, where GABA binding locks the β and α subunits, causing them to pull on the extracellular domains (ECDs) of the subunits, rotating them counterclockwise27. This movement bends the transmembrane domains (TMDs), thereby opening the ion channel27. Thus, the channel activity appears to be coordinated together with structural cassettes within the GABAA receptors. It turns out that epilepsy mutations cause dysfunction in channel activity via distortion of these structural cassettes28. Consequently, our study is based on the idea that predicted pathogenic variants in proximity to functionally identified epileptogenic mutations in the specific structural cassettes of the GABAA receptor subunits may exhibit similar patterns of electrophysiological or biophysical distortion in channel function, as observed in cases of these epileptogenic mutations. While the presence of epileptogenic structural cassettes in the GABAA receptor subunits28 indirectly supports this notion, our study demonstrates the complexity and challenge of correlating biophysical parameters of epileptogenic mutations with those of predicted pathogenic mutations. To unmask these complex relationships, our framework is significant as it highlights a multiscale approach ranging from DNA to protein function and neural behavior critical for epilepsy research. This approach integrates computational genetics with molecular modeling and neural simulations while also emphasizing the importance of complementary methods, such as machine learning trained on large datasets, that could capture the effects of mutations on channel structure, activity, and neural excitability. In addition, the simulation of epileptogenic γ2-GABAA receptor activity on the hippocampal pyramidal neuron model allows the replication of in vitro cellular phenotype associated with GABAA receptor channelopathy and the demonstration of altered single-neuron responses at the center of network dysfunction.

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Protocol

1. In silico prediction o​f pathogenic variants

  1. Variant data collection
    1. Using the ClinVar database29, search for variants of uncertain significance (VUS) in the coding region of the gene of interest via the website: https://www.ncbi.nlm.nih.gov/clinvar/. Enter the gene symbol (e.g., GABRG2) in the search bar and filter the results to include only the desired types of variants, such as single-nucleotide, missense variants with uncertain significance. Download and save the data as data.xlxs (Supplementary File 4: Supplementary Table S1). Record the date of the downloaded data.
      NOTE: In the present protocol, human γ2 subunit of GABAA receptor, specifically Homo sapiens gamma-aminobutyric acid type A receptor subunit gamma2 (GABRG2), transcript variant 1, mRNA (NCBI Ref. seq.: NM_198904.4), also known as γ2L, will be analyzed. It is important to record the reference transcript of the gene of interest as well as other corresponding identifiers across different databases (UniProt, ENSEMBL, PDB) since different computational methods may require different identifiers (Supplementary File 4: Supplementary Table S2). In case the database or computational tool does not recognize the version numbers of the sequence identifiers, try both the ID with the version number (NM_198904.4) and without the version number (NM_198904).
    2. Reference protein basic information
      1. In the NCBI database https://www.ncbi.nlm.nih.gov/, select Nucleotide in the search options and enter the NCBI Ref. seq. ID of the gene of interest (NM_198904.4). Then, by scrolling down on the right column, click on the Protein under the category Related information to find the protein (NP_944494.1) encoded by the transcript NM_198904.4. Using the information given for the protein NP_944494.1, record the sequence positions of the specific regions in the form of a table (Supplementary File 4: Supplementary Table S3).
        NOTE: It is important to determine the preliminary known information for sequence position of functionally and structurally critical regions, motifs, or residues such as protein domains, phosphorylation sites, ligand binding sites, and molecular interaction interfaces. This can be achieved by combining database (NCBI, ENSEMBL, UniProt...) and literature searches.
  2. Variant data organization
    1. Organize the data to meet the input requirements for the chosen predictors. Ensure that the format of the data retrieved is organized to match the requirements of the dbNSFP server http://database.liulab.science/dbNSFP. To do this, remove unnecessary columns from the data.xlsx file (Supplementary File 4: Supplementary Table S1 from step 1.1.1), keeping only the following columns in the specified order:
      ​"GRCh38Chromosome", "GRCh38Location", "Name", "Protein change".
    2. Save the file under a new file name: "data1.xlsx" (Supplementary Table S4). Format the data1.xlsx file in R by running the code (Supplementary File 1: Data_GABAA.R), which will save the formatted data as data1_output.xlsx (Supplementary File 4: Supplementary Table S5) in the working directory relevant to the R project.
      NOTE: Different computational methods require different data types and formats. Collecting and organizing data according to specific format requirements, even for a dozen variants, can be prone to errors and time-consuming, so this step is important unless the variant pool is made up of only a few variants. Then, manual data organization may be possible.
  3. Pathogenicity prediction
    1. Transfer the content of the data1_output.xlsx file into the academic version of dbNSFP server30,31 accessed via http://database.liulab.science/dbNSFP. To do this, copy/paste or directly upload the file in .txt format.
    2. Ensure that the following options are preselected and confirmed in the server: HG38 (genome build), ClinPred32, and BayesDEL33 before submission. Within a few minutes, the server will generate the results.
      NOTE: In the present protocol, two ensemble predictors, namely BayesDEL33 and ClinPred32, were selected for high accuracy34 and practicality. However, other predictors, such as AlphaMissense, which is available in the dbNSFP database30,31 can also be selected. The selection of in silico tools depends on several factors, including the generation of sufficient multiple lines of computational evidence for powerful prediction12. Ensemble predictors integrating the analysis of multiple predictive algorithms can serve this purpose.
    3. Download the output file (a .txt format) and save it as data2.xlsx (Supplementary File 4: Supplementary Table S6).
    4. Set the filters in data2.xlsx (Supplementary File 4: Supplementary Table S6) by clicking on the filter option in the menu and determining the consensus variants in both columns by filtering for D. This will give the list of the most pathogenic variants; save it (see Consensus tab in Supplementary Table S6 [ Supplementary File 4]).
  4. Variant selection
    1. Among the consensus pathogenic predictions, determine the variants in the proximity of epileptogenic mutations obtained from the literature. Ensure that the latter have structural and biophysical parameters suitable for neuron modeling.
      NOTE: This step is exploratory and is also related to surveying the protein of interest in terms of its structural, physicochemical, and biophysical parameters. In the present study, these data were obtained from Brünger et al.35 and Guo et al.36 in addition to a survey of epilepsy associated mutations. Besides, as an option, AlphaMissense37 scores were accessed from the dbNSFP database30,31 repeating step 1.3 (Supplementary File 4: Supplementary Table S7). More details are given in protocol sections 2.1.1 and 2.1.2 and in the Results (see "Clustering Variants for Structural and Biophysical Parameters").
    2. For basic visualization, use Protter38 ((https://wlab.ethz.ch/protter/start/), and HOPE39 (https://www3.cmbi.umcn.nl/hope/) servers to examine the variants in the previous step in the context of selected GABRG2 gene mutations: P302L40 and K328M (or K289M41, when excluding the 39-residue signal peptide).
      NOTE: Due to the enormous complexity, structural evaluation of variant effects should be conducted at multiple levels of analysis. Tools like Protter38 will allow the clear visualization of the variants in the context of topological features of the protein and user-friendly servers such as HOPE39 will give insight into the variant effect by molecular modeling. In addition, a comprehensive literature review of the protein of interest is critical to identify and integrate the information about mutations associated with epilepsy.
    3. Analysis of evolutionary conservation and structural insights
      1. Open Jalview42,43,44, an open-source program for editing, visualization, and analysis of proteins.
      2. Import sequences for alignment. Click on File in the top menu | Fetch sequences; select the database in the dialog box (such as UniProt); click on the retrieve IDs tab; and as described in the dialog box, enter UniProt accession IDs of the gene of interest (GABRG2) from human and other vertebrate species: P18507, P22723, Q6PW52, A0A2I3TKX0, F1RR72, A0A8I3MDZ2, A0A8M1P4D6. Click OK.
        NOTE: UniProt accession numbers of proteins encoded by GABRG2 are as follows: P18507 (P18507-2) for Homo sapiens, P22723 for Mus musculus, A0A2I3TKX0 for Pan troglodytes, F1RR72 for Sus scrofa, A0A8I3MDZ2 for Canis familiaris, and A0A8M1P4D6 for Danio rerio.
      3. Depending on the gene of interest, some sequences may not be annotated; therefore, perform a BLAST search to identify relevant information and potential homologs for a better contextual understanding. In this case, upload the FASTA format of protein sequences via the Add Sequences/From text box option under the File menu to produce multiple sequence alignments of the desired sequences.
      4. Once the alignment is loaded, observe the sequences displayed for multiple sequence comparison. Each row represents a sequence, and each column represents a position in the alignment. To determine the best alignment method, use different approaches; for instance, click on the Web services in the Sequence menu and select the Run T-Coffee with preset option, which allows optimal alignment.
      5. Right-click on the sequence P18507 Homo sapiens (the reference sequence in the present study) and set it as the reference sequence. Choose Format in the upper menu and click on Wrap for the visualization of the full alignment in the screen. In the same Format menu, click on the scale above to enhance the visualization of specific residue numbers. To further enhance visualization, adjust the color schemes by going to Color and selecting different options (e.g., Clustal Color, Chemical Property); modify the font size if required.
      6. Click on Calculate in the Menu bar and select Autocalculate consensus to highlight conserved regions.
      7. Focus on the position of variants of interest identified in the in-silico prediction step and examine specific variant positions. Annotate specific residues by right-clicking on them and selecting Add Annotation. Write the label (e.g., variant ID) with the appropriate color code and save.
        NOTE: In the present analysis, P302L (purple) and A303T (red) were selected to visualize them in the multiple sequence alignment together with the structural data (see the next section).
    4. Three-dimensional reconstruction of the full protein showing the selected conserved residues
      1. In the file obtained from the previous step, right-click on the reference sequence (GABRG2 human) and select 3D structure data.
      2. Identify the appropriate structural data (7QNE, Chain C)26 from the dropdown menu and select Open new structure view with Jmol.
        NOTE: This will allow the incorporation of the residues selected in the multiple sequence alignment into the structural data by Jmol, an open-source Java-based viewer for 3D chemical structures.

2. Parameter selection and biophysical modeling

  1. Variant-specific meta-analysis and parameter normalization
    1. Survey current literature to gather identified subunit variants with electrophysiological data-channel conductance (gGABAA), deactivation time (τdeactivation), rise time (τrise), and maximum current amplitude (Imax). Provide the subunit compositions, cell type, and wild-type measurements for each case. Label the variants and their controls accordingly (e.g., known for variants with identified biophysical characteristics and known control for the wild-type measurements for each variant).
    2. Obtain AlphaMissense pathogenicity scores for variants with identified biophysical characteristics.
      NOTE: See protocol section 1.3 for more details.
    3. Create a data frame with subunit and amino acid position for each variant, the original and altered amino acids, pathogenicity score, and biophysical parameters obtained from the literature. To avoid experimental discrepancies, normalize the biophysical parameters for identified variants as x-fold changes on wild-type measurements.
  2. Comparative variant analysis by structural and functional characteristics
    1. Organize the predicted variants on a data frame; label accordingly (e.g., predicted for variants without literature available on their biophysical characteristics).
    2. Classify the variants by their location in the amino acid sequence and tertiary structure. Add structural classification parameters (e.g., localization in alpha helices, coils, beta sheets, extracellular, intracellular or transmembrane domains, pore lining, agonist binding, protein-protein interactions) on the data frame and provide information for each variant with respect to their amino acid position.
    3. Classify the variants by their distance to the membrane center and pore axis. Add distance to the pore axis and distance to the membrane center parameters on the data frame.
    4. Analyze the correlation among structural and biophysical parameters over known variants. If possible, evaluate the predicted variants with respect to the obtained correlations.
  3. Synapse and neuron model construction
    1. Use the Brian245, an open-source neural simulator developed in Python for modeling and simulating spiking neural networks, to build a multi-compartmental biophysical model of GABAergic synapse on a multi-compartmental conductance-based hippocampal pyramidal neuron.
    2. Design the conductance-based model by defining ion channel gating kinetics, passive and active parameters, and postsynaptic conductances. Define the conductance-based model as given in Supplementary File 2, which describes the equations used in the model.
      1. Set membrane capacitance (Cm) as 1 µF/cm2 and intracellular resistance (Ra) as 200 Ω.cm.
      2. Use the modified Hodgkin-Huxley type conductances for hippocampal pyramidal neurons39 with gL= 0.0003 S/cm2, gK= 0.036 S/cm2, EL = -76.5 mV, ENa = 50 mV, and EK = -90 mV.
      3. Adjust the density distribution of NaV channels over gNa as 0.05 S/cm2 for soma, 0.5 S/cm2 for axon initial segment (AIS) and node of Ranvier (NR), and 0.005 S/cm2 for dendrites. Set gK and gNa as 0 in myelinated segments.
      4. Build ion channel gating kinetics for NaV and KV as described in Supplementary File 2.
      5. Introduce synaptic currents (Isyn) as the summation of all glutamatergic and GABAergic synapses in a compartment. Include both fast AMPA receptor-mediated current (IAMPA) and slow NMDA receptor-mediated current (INMDA) in the glutamatergic current (Iglu). Include only fast GABAA receptor-mediated current in GABAergic current (IGABA). Assume that a constant amount of glutamate is released to the synapse for every presynaptic spike; therefore, the activation of receptors are spike-time-dependent (sAMPA and sNMDA) and the total receptor conductances (gAMPA  and gNMDA) reflect the amount of glutamate that is released by every event.
      6. Use the synaptic model as described in Supplementary File 2.
        NOTE: For a detailed explanation of the equations, see Supplementary File 2 describing the equations used in the model.
    3. Obtain the experimentally measured diameter for soma and neurites and the length of each neurite compartment and branching patterns from previous literature46,47. Reduce the real neuron morphology into a multi-compartmental model, by dividing the cell into multiple compartments, that accurately preserves the main branching structure and maintains bilateral symmetry.
    4. Set the morphological (segment length and diameter; i.e., d_soma: 30 µm; l_AH: 5 µm; d_AH_i: 1.5 µm; d_AH_f: 1.3 µm; l_AIS: 40 µm; d_axon: 1 µm; l_myseg: 100 µm; l_NR: 2 µm; l_AxTer: 4 µm; d_AxTer: 2 µm; l_approx: 100 µm; l_apmed: 100 µm; l_apdis: 200 µm; d_approx_i: 4 µm; d_approx_f: 3 µm; d_apmed : 2 µm; d_apdis: 2 µm; l_apLM: 70 µm; d_apLM: 2 µm; l_nAcDbasal: 400 µm; d_nAcDbasal: 1.4 µm; l_nAcDbasal_stem: 20 µm; d_nAcDbasal_stem: 1.5 µm) and biophysical parameters (as given in section 2.3.2) for each compartment of the pyramidal neuron model46,47 as also detailed in the Python script (Supplementary File 3: GABAAvar.py).
    5. Determine the biophysical parameters for the GABAergic synapse model by evaluating the wild-type control measurements obtained in step 2.1.1.
  4. Design the topology of the neuron model and assign morphological and biophysical parameters, which includes specifying the spatial arrangement and interconnections of the compartments, based on the previously obtained morphological and branching information. Assign the appropriate morphological (e.g., segment length and diameter) and biophysical parameters (Section 2.3.2) to each compartment of the model, as outlined in Supplementary File 3: GABAAvar.py.
  5. Building of synapses and current injection
    1. Create the presynaptic activity using SpikeGeneratorGroup (a class from the Brian2 library) as given in "GABAAvar.py" (Supplementary File 3). Connect the spike generator to the target compartment of the model neuron using Synapses class to model synaptic connections.
    2. Set a sustained constant current (Iinj) as 0.85 nA and place at the soma to mimic the subthreshold activity driven by baseline ionic current load at a given time as outlined in Supplementary File 3: GABAAvar.py.
  6. To build recording monitors, record voltage traces from target compartments using StateMonitor.
  7. Build and run the network.
    1. Build the network with the model neuron, connections, and monitors using Network.
    2. Set the time step of the simulation by defaultclock.dt (e.g., 0.01 ms).
    3. Run the simulation on the network with network.run(T*ms), where T is set as 1,000 ms in the example.
  8. Testing the impact of GABAA receptor missense mutations
    1. Define the impact of each missense mutation on channel kinetics through the biophysical parameters collected in step 2.1.1.
    2. Run the stimulation by altering these parameters and plot the results using "matplotlib.pyplot" as given in "GABAAvar.py" (Supplementary File 3).
  9. Test parameter combinations to analyze the changes in firing patterns and rates. Plot results for comparisons.

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Results

This study utilizes a multiscale approach to predict and characterize the pathogenic variants in the γ2 subunit of the GABAA receptor, a key component in the pathophysiology of epilepsy. Through the use of predictive models, molecular modeling, evolutionary conservation, structural examination, correlation analysis, and neural simulations, this approach enhances the classification of variants, with significant relevance for epilepsy research and possibly for clinical use. The o...

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Discussion

By applying a combination of computational genetics, molecular modeling and neural simulations, the approach presented in this paper has the potential to improve the classification of GABAA receptor variants, offering valuable insights for both epilepsy research and clinical applications. A comprehensive analysis for the identification and prioritization of predicted pathogenic mutations is presented and extended into a framework that potentially bridges the gap between variant effects on protein and cellular ...

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Disclosures

All authors declare that they have no conflicts of interest related to this work.

Acknowledgements

We thank Çağla Koca for her assistance with the construction of the model neuron.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Brian2 Sorbonne Université, INSERM, CNRS, Institut de la Vision, France; Imperial College London, United Kingdom2.8.0.4Stimberg et al., 2019 (https://pypi.org/project/Brian2/ )
dbNSFP server  Genos Bioinformatics LLC, USAv3.0Liu et al., 2020 (http://database.liulab.science/dbNSFP) (https://sites.google.com/site/jpopgen/dbNSFP)
HOPE  Centre for Molecular and Biomolecular Informatics CMBI, Radboud University, Netherlands 1.1.1Venselaar et al., 2010 (https://www3.cmbi.umcn.nl/hope/)
Jalview  University of Dundee, UKJV2Waterhouse et al., 2009 (https://www.jalview.org/)
Jupyter NotebookProject Jupyter, USAhttps://jupyter.org/install 
PhytonPython Software Foundation, USA3.13https://www.python.org/downloads/
Protter  ETH Zurich, SwitzerlandVersion 1.0Omasits, et al., 2014 (https://wlab.ethz.ch/protter/start/)
R The R Foundation for Statistical Computing, USAR version 4.3.2  https://www.r-project.org/ 
RStudioPosit software, PBC, USARStudio 2023.12.1+402 "Ocean Storm" Releasehttps://posit.co/downloads/

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GABAa Receptor VariantsEpileptogenic MutationsPathogenic Mutation PredictionMolecular ModelingNeural SimulationEnsemble PredictorsEvolutionary ConservationConductance-Based Model
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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