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 selection. 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.

---

What do you need to do? 💻

1. Choose the tasks. Sequence classification, uptake quantitative prediction, or sequence generation.

a) Sequence classification is the easiest task, where you need to develop the model differentiating between CPP and non-CPP sequences based on the sequence. The main problem is such models do not inherently allow to find the best CPP sequences. Additional algorithm for sequence generation should be developed to discover novel CPP sequences.

b) Uptake quantitative prediction is more challenging due to small data existing in the field, where you need not just predict either sequence is CPP or not, but to predict its cellular uptake depending on conditions and cell type. The problem is, despite such model allows to compare CPP activities with each other and find the best ones, additional algorithm for sequence generation is still needed.

c) Sequence generation is the most challenging task since it requires deep learning models and a lot of data. Generation can be simple or conditional. In the first case, the model should basically learn the patterns in CPP sequences and be able to generate new sequences based on these intrinsic rules (cell type, uptake value, and experimental setup are not considered). In the second case, the model should generate potential CPP sequences based on information about desired uptake, cell type, and experimental setup.

d) Hybrid algorithm is the most optimal choice, since simple classification/regression models can be "inversed" using evolutionary algorithms. Moreover, results obtained by simpler models can be reused by more complex to compensate for insufficient data.

2. Create a database. Process datasets, look for more data, merge it, clean, and unify, create a database with DBMS.

• study the organization of data in the datasets
• search for additional data (high throughput screening studies, review papers, databases, datasets etc.)
• search for additional parameters describing CPPs or experimental setup, which can be computed, predicted, or parsed
• unify the data (measurement units, amino acid notations etc.)
• remove duplicated samples
• choose the type of DBMS
• develop a database structure for DBMS
• move the data to DBMS
• set up access, data retrieval etc.

3. Analyze the data. Perform sequence alignment, look for conservative patterns, study correlations.

• perform local or global sequence alignment on CPP and non-CPP sequences (either all or particular groups/clusters)
• make amino acid frequency maps to search for conservative patterns and dependencies
• compare these findings with literature information
• make correlation plots for categorical and numeric parameters
• try to answer the question what parameters and sequence patterns lead to best-performing CPPs

4. Choose the models. Find the best-performing classification/regression/generation models to develop and compare.

• do not screen models which were shown to underperform most of the modern ML/DL models
• use the models with documented performance
• you can use models pre-trained on more abundant data (transfer learning)
• prioritize interpretable models over black-box

5. Build and optimize the models. Check model performance on default parameters, optimize hyperparameters and architechture.

• choose the logic of train/test split (random, stratified, rule-based etc.)
• build basic models in simplest form
• optimize the code until it runs correctly
• make a list of architectures you want to test (for neural networks)
• choose a method for hyperparameter tuning (Optuna, Grid search etc.)

6. Choose the best-performing model. Prioritize the list of models by accuracy, interpretability, extrapolative power, and robustness.

• analyze model performance (use appropriate classification/regression metrics or loss functions)
• check model extrapolative power (ability to work on samples, which differ a lot from train samples)
• analyze model feature importance and its consistency with literature observations and basic logic
• analyze model speed
• choose the best model according to these parameters

7. Validate your model. Use computational, predictive, or hybrid approaches to check model consistency with first principles and previous studies.

• choose the methods for additional model validation (computational models, benchmarked ML/DL models, hybrid approaches)
• check these methods on correlation with labeled data (for instance, how good these methods differentiate between CPPs and non-CPPs)
• analyze how good these methods explain obtained results
• generate novel CPPs for validation

8. Formalize the results. Create a repo, systematize analysis results, submit the code, ensure the code is reproducible, usable, and readable.

• create a GitHub repository structure
• sort and publish all the results obtained during data analysis
• structure, debug, and prettify the code
• ensure all dependencies needed for correct code work are listed in requirements.txt

---

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.

Non-CPP sequence GSRVQIRCRFRNSTR is predicted NOT 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.