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| ## Data Sources and Methods | |
| ### Training Data Collection | |
| Our models are trained on curated datasets from multiple sources: | |
| #### Hemolysis Dataset | |
| - **Primary Source:** [the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv3)](https://academic.oup.com/nar/article-abstract/49/D1/D288/5957160) | |
| - **Secondary Source:** [peptideBERT](https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.3c02398) | |
| - **Size:** 9,316 peptides, with 19.6% being positive (hemolytic) and 80.4% being negative (nonhemolytic) | |
| - **Description:** Probability of peptide disrupting red blood cell membranes. | |
| - **Download:** [hemo-positive.npz](https://huggingface.co/ChatterjeeLab/Classifier_Weight/blob/main/training_data/hemo-positive.npz) | |
| - **Interpretation** 50% of read blood cells being lysed at x ug/ml concetration (HC50). If HC50 < 100uM, considered as hemolytic, otherwise non-hemolytic. | |
| #### Solubility Dataset | |
| - **Primary Source:** [PROSO-II](https://febs.onlinelibrary.wiley.com/doi/abs/10.1111/j.1742-4658.2012.08603.x) | |
| - **Secondary Source:** [peptideBERT](https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.3c02398) | |
| - **Size:** 18,453 sequences, with 47.6% being labeled as positives and 52.4% being labeled as negatives | |
| - **Description:** Probability of peptide remaining dissolved in aqueous conditions. | |
| - **Download:** [sol-positive.npz](https://huggingface.co/ChatterjeeLab/Classifier_Weight/blob/main/training_data/sol-positive.npz) | |
| #### Non-Fouling Dataset | |
| - **Primary Source:** [Classifying antimicrobial and multifunctional peptides with Bayesian network models](https://doi.org/10.1002/pep2.24079) | |
| - **Secondary Source:** [peptideBERT](https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.3c02398) | |
| - **Size:** 3,600 positive, 13,585 negative | |
| - **Description:** A nonfouling peptide resists nonspecific interactions and protein adsorption. | |
| - **Download:** [nf-positive.npz](https://huggingface.co/ChatterjeeLab/Classifier_Weight/blob/main/training_data/nf-positive.npz) | |
| #### Permeability Dataset | |
| - **Primary Source:** [CycPeptMPDB](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.2c01573), [PAMPA](https://doi.org/10.1517/17425255.1.2.325) | |
| - **Secondary Source:** [PepLand](https://arxiv.org/abs/2311.04419) | |
| - **Size:** 1162 positive and negative for nanonical samples each (22 relevant cell-penetrating peptide databases by compiling literature on existing cell-penetrating peptide prediction models ); CycPeptMPDB provides extra 7334 cyclic peptides | |
| - **Description:** Probability of peptide penetrating the cell membrane. | |
| - **Download:** [nc-CPP-processed.csv](https://huggingface.co/ChatterjeeLab/Classifier_Weight/blob/main/training_data/nc-CPP-processed.csv) | |
| #### Half-life Dataset | |
| - **Primary Source:** [Thpdb2](https://doi.org/10.1016/j.drudis.2024.104047), [PepTherDia](https://doi.org/10.1016/j.drudis.2021.02.019), [peplife](https://www.nature.com/articles/srep36617) | |
| - **Size:** 105 wt, 275 wt+noncanonical, human-only | |
| - **Clean-ups:** Data are all transformed into log\(hour\) | |
| - **Download:** [binding_affinity_training_data.csv](https://huggingface.co/ChatterjeeLab/Classifier_Weight/blob/main/training_data/half_life_smiles.csv) | |
| #### Binding Affinity Dataset | |
| - **Primary Source:** [PepLand](https://arxiv.org/abs/2311.04419) | |
| - **Size:** 1,781 protein-peptide complexes, canonical and non-canonical | |
| - **Description:** Binding probability normalized in PepLand already. It's a combination of IC50/EC50. | |
| - **Quality:** Binding class cutoffs: Tight ≥ 7.5, Medium 6.0–7.5, Weak < 6.0 | |
| - **Download:** [binding_affinity_training_data.csv](https://huggingface.co/ChatterjeeLab/Classifier_Weight/blob/main/training_data/c-binding.csv) | |
| - **Interpretation** Affinity_measure = -log_10(K). | |
| ### Model Architecture | |
| - **Sequence Embeddings:** ESM-2 650M parameter model | |
| - **XGBoost Models:** Gradient boosting on pooled ESM embeddings | |
| - **CNN Models:** 1D convolutional networks with attention mechanisms | |
| - **Binding Model:** Cross-attention between protein and peptide representations | |
| ### Model Training and Weight Hosting | |
| - [Classifier_weights](https://huggingface.co/ChatterjeeLab/Classifier_Weight) | |
| ### 🧪 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: | |
| ``` | |
| place holder | |
| ``` | |
| ### Contact | |
| For questions or collaborations: [yzhang@u.duke.nus.edu](mailto:yzhang@u.duke.nus.edu) |