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README.md

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    - uptake type,
    - sequence.
 
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 <h3> 2. Natural CPPs </h3>
    
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    Represents a balanced dataset of CPPs and non-CPPs; often used for model benchmarking.
 
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 <h3> 3. Non-CPPs </h3>
    
    Contains negative CPP samples in .txt format.
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    Contains non-CPP sequences shown not to demonstrate activity experimentally.
 
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 <h3> 4. Non-Natural CPPs </h3>
    
    Contains CPPs consisting of non-natural amino acids.
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    Contains a list of abbreviations for modified amino acids  in .txt format (ABBREVIATION: NAME; ...: ...).
 
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 <h2> Useful tools :bookmark_tabs: </h2>
 
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    <img src="https://github.com/acid-design-lab/DataCon24/assets/82499756/640ee468-cac2-4e7d-8042-8baf68bbe865" alt="drawing" width="500"/>
 
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 <h3> Modelling of interaction with membrane </h3>
 
    For CPPs from 7 to 24 amino acids you can use [PMIpred neural network model](https://pmipred.fkt.physik.tu-dortmund.de/curvature-sensing-peptide/) trained on Molecular Dynamics (MD) data to predict its interaction with the cellular membrane. Please use modelling on neutral membrane for better differentiation between CPPs and non-CPPs.
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    <img src="https://github.com/acid-design-lab/DataCon24/assets/82499756/22cd60b9-0d0f-4021-a61e-8b0865c8b583" alt="drawing" width="500"/>
 
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 <h3> Membrane permeability prediction </h3>
 
    For so-called stapled peptides consisting of both natural and modified amino acids you can predict membrane permeability using [STAPEP package](https://github.com/dahuilangda/stapep_package) offering the full pipeline from data preprocessing to ML model development and use on novel samples.