DataCon 3.0. Design a Peptide Vector for Drug Delivery

About the project ℹ️

What are CPPs?

Cell-penetrating peptides (CPPs) are short sequences of amino acids that have the remarkable ability to cross cellular membranes, facilitating the intracellular delivery of various therapeutic agents, including drugs, nucleic acids, and proteins. These peptides exploit mechanisms such as direct penetration or endocytosis to traverse cell membranes, making them powerful tools in drug delivery systems.

In real-world medical applications, CPPs are being leveraged to enhance the efficacy of treatments for a range of conditions. For instance, they are used in targeted cancer therapies to deliver chemotherapeutic agents directly to tumor cells, minimizing damage to healthy tissues. Additionally, CPPs are employed in gene therapy to transport genetic material into cells, offering potential treatments for genetic disorders like cystic fibrosis and muscular dystrophy. Their versatility and efficiency in overcoming cellular barriers position CPPs as a promising frontier in the development of advanced therapeutic strategies.

Project pipeline ▶️

Our ultimate goal is to develop precise machine learning (ML) model allowing to design CPPs with superior activity. Here are the main steps which will allow you to build a precise model for CPP design:

1. Data curation and cleaning. All inappropriate or ambiguous data should be removed or corrected.

2. Data unification. The data presented in Datasets are heterogeneous and should be unified in terms of variables, measurement units etc.

3. System parametriation. You need to choose the set of parameters to describe CPPs as well as experimental setup. Most of the models use symbolic representations lacking physico-chemical properties crucial for CPP activity prediction.

4. Model selection. Best-performing models should be choosen for screening depending on the task complexity (sequence classification or sequence generation).

5. Feature selecction. After model selection, features used in the model should be choosen showing optimal prediction performance, robustness, and interpretability.

6. Evaluation. Every model should be evaluated beyond performance on train/test datasets. It can be structural analysis of CPP candidates, modelling of interaction with cellular membranes etc.

7. Project design. All results should be structured and systematized on GitHub for transparency and reproducibility.

Challenges 🏆

The main challenge here is to develop unbiased model not limited to existing CPP structures and cell penetration mechanisms. Another challenge is to develop CPPs for particular drug delivery system and setup, which includes multi-property optimization (amphiphilicity, molecular weight, toxicity etc.). Finally, models should be interpretable, which means user should know why particular CPP demonstrates its activity, and what are the possible ways to improve it further.

Schedule 📆

DataCon 3.0 includes not only practices but authoritative lectures and other activities, therefore check for any schedule updates HERE.

Contents 📖

This repository contains the following data:

1. Articles about CPPs to read (see the relevant folder)
2. Available datasets for model development (see Data Description section)
3. Useful tools for property and structure prediction (see Useful tools section and relevant folder)

Data description 💾

1. Mixed CPPs

Contains CPPs with natural or modified amino acids.

  1.1. POSEIDON

  Contains heterogeneous experimental data regarding CPP (natural and non-natural amino acids) activity measurements (.csv format), which are:
  - peptide name,
  - target cell line CPP was tested on cell penetration ability,
  - delivered molecule/protein,
  - paper PubMed ID,
  - cellular uptake measurement + measurement units,
  - CPP+cargo concentration,
  - incubation time,
  - incubation temperature,
  - determination method,
  - uptake type,
  - sequence.

2. Natural CPPs

Contains only sequences with natural amino acids.

   2.1. CPPBase
  
   Contains sequences of CPPs with experimentally proved activity in .fasta format.


   2.2. Experimental and Experimental2
  
   Contain more sequences of CPPs with experimentally proved activity in .txt format.


   2.3. Experimental_high_uptake
  
   Contains CPP sequences with high (but not stated) uptake in .txt format.

   2.4. Balanced_dataset
   
   Represents a balanced dataset of CPPs and non-CPPs; often used for model benchmarking.

3. Non-CPPs

Contains negative CPP samples in .txt format.

   3.1. Generated
  
   Contains randomly generated sequences treated as negative.


   3.2. Experimental
  
   Contains non-CPP sequences shown not to demonstrate activity experimentally.

4. Non-Natural CPPs

Contains CPPs consisting of non-natural amino acids.

   4.1. CPPBase_modified
  
   Contains a list of modified CPPs with experimentally proved activity in .fasta format.

  
   4.2. CPPBase_modified_symbols
  
   Contains a list of abbreviations for modified amino acids  in .txt format (ABBREVIATION: NAME; ...: ...).

Useful tools 📑

Structure prediction

In the relevant folder you can find a Jupiter notebook with AlphaFold 2.

Just insert the sequence

and get a 3D structure!

Modelling of interaction with membrane

For CPPs from 7 to 24 amino acids you can use PMIpred neural network model 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.

Example:

CPP sequence FSLHRYMAWFCPWTGAWLMLD is predicted to BIND to the membrane.

Membrane permeability prediction

For so-called stapled peptides consisting of both natural and modified amino acids you can predict membrane permeability using STAPEP package offering the full pipeline from data preprocessing to ML model development and use on novel samples.

Non-CPP sequence is predicted NOT TO BIND to the membrane.