Input Requirements and Constraints

Supported Inputs

• Amino acid sequences: Linear peptides composed of standard 20 amino acids
• SMILES: Chemically modified peptides, including cyclization, D-amino acids, and noncanonical resiudes

  Validation

• Invalid sequences or SMILES will be rejected
• Properties not supported are labeled as (Not Supported)

Training Data Collection

Data distribution. Classification tasks report counts for class 0/1; regression tasks report total sample size (N). AA stands for amino acid-based sequences.

Classification (counts for class 0 / 1)

Regression (total N)

Our models are trained on curated datasets from multiple sources. For detailed cleaning up procedures please refer to our paper.

🩸 Hemolysis Dataset

• Primary Source: the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv3)
• Secondary Source: peptide-dashboard
• Description: Probability of peptide disrupting red blood cell membranes.
• Interpretation 50% of read blood cells being lysed at x ug/ml concetration (HC50). If HC50 < 100uM, considered as hemolytic, otherwise non-hemolytic, resulting in a binary 0/1 dataset. Scores close to 1 indicate a high probability of red blood cell membrane disruption, while scores close to 0 indicate low hemolytic risk. The predicted probability should therefore be interpreted as a risk indicator, not an exact concentration estimate.

💧 Solubility Dataset

• Primary Source: PROSO-II
• Secondary Source: peptideBERT
• Description: Probability of peptide remaining dissolved in aqueous conditions.
• Interpretation: Outputs a probability (0–1) that a peptide remains soluble in aqueous conditions. Higher scores indicate lower aggregation risk and better formulation stability.

👯 Non-Fouling Dataset

• Primary Source: Classifying antimicrobial and multifunctional peptides with Bayesian network models
• Secondary Source: peptideBERT
• Description: A nonfouling peptide resists nonspecific interactions and protein adsorption.
• Interpretation: Outputs the probability (0–1) that a peptide resists nonspecific protein adsorption. Higher scores indicate stronger non-fouling behavior, desirable for circulation and surface-exposed applications.

🪣 Permeability Dataset

• Primary Source: CycPeptMPDB, PAMPA
• Secondary Source: PepLand
• Description: Probability of peptide penetrating the cell membrane.
• Interpretation: For PAMPA and CACO-2 regression, outputs are log-scaled permeability values. Following CycPeptMPDB conventions, log Pexp ≥ −6.0 indicates favorable permeability, while values below −6.0 indicate weak permeability. For penetrance prediction, the probability closer to 1 indicates higher risk of cell penetrance, and vice versa.

⏱️ Half-Life Dataset

• Primary Source: Thpdb2, PepTherDia, peplife
• Interpretation: Predicted values reflect relative peptide stability for the unit in hours. Higher scores indicate longer persistence in serum, while lower scores suggest faster degradation.

☠️ Toxicity Dataset

• Primary Source: ToxinPred3.0
• Interpretation: Outputs a probability (0–1) that a peptide exhibits toxic effects. Higher scores indicate increased toxicity risk.

🔗 Binding Affinity Dataset

• Primary Source: PepLand
• Description: Binding probability normalized in PepLand already. It's a combination of Kd, Ki, IC50.
• Description: The model predicts a continuous binding affinity score, where higher values indicate stronger binding. Scores are comparable across peptides binding to the same protein target.
• Interpretation:
  
  • Scores ≥ 9 correspond to tight binders (K ≤ 10⁻⁹ M, nanomolar to picomolar range)
    
  • Scores between 7 and 9 correspond to medium binders (10⁻⁷–10⁻⁹ M, nanomolar to micromolar range)
    
  • Scores < 7 correspond to weak binders (K ≥ 10⁻⁶ M, micromolar and weaker)
    
  • A difference of 1 unit in score corresponds to an approximately tenfold change in binding affinity.
    

Model Architecture

• Sequence Embeddings: ESM-2 650M model / PeptideCLM model. Foundational embeddings are frozen.
• XGBoost Model: Gradient boosting on pooled embedding features for efficient, high-performance prediction.
• CNN/Transformer Model: One-dimensional convolutional/self-attention transformer networks operating on unpooled embeddings to capture local sequence patterns.
• Binding Model: Transformer-based architecture with cross-attention between protein and peptide representations.
• SVR Model: Support Vector Regression applied to pooled embeddings, providing a kernel-based, nonparametric regression baseline that is robust on smaller or noisy datasets.
• Others: SVM and Elastic Nets were trained with RAPID cuML, which requires a CUDA environment and is therefore not supported in the web app. Model checkpoints remain available in the Hugging Face repository.

Model Training and Weight Hosting

• More instructions can be found here at PeptiVersse

🧪 Physicochemical Properties

Net Charge Calculation

• Uses Henderson-Hasselbalch equation
• pH-dependent calculation
• Considers all ionizable groups (K, R, H, D, E, C, Y, termini)

Isoelectric Point (pI)

• Bisection method to find pH where net charge = 0
• Precision: ±0.01 pH units

Hydrophobicity (GRAVY)

• Grand Average of Hydropathy
• Uses Kyte-Doolittle scale
• Range: -4.5 (hydrophilic) to +4.5 (hydrophobic)

Citation

If you use this tool, please cite:

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Contact

For questions or collaborations: yzhang@u.duke.nus.edu or pranam@seas.upenn.edu