Pepti-drift: Toxicity-repulsive drifting for antigen-conditioned discrete peptide generation
1. Pepti-drift is a one-step, antigen-conditioned peptide generator that explicitly tackles a core therapeutic-design tension: sequence features that improve binding often overlap with features linked to cytotoxicity/hemolysis, so “optimize binding only” can land candidates in a risky overlap zone.
2. The method reframes peptide design as controlled movement in a peptide embedding space: generated peptide latents are pulled toward antigen-matched binders (attraction) while being pushed away from toxicity-associated regions (repulsion), aiming for a “safe & active” region rather than a generic binder-rich area.
3. Architecture in one pass: frozen ESM-2 (3B) encodes antigen and peptide sequences; learned projection heads reshape embeddings into a compact normalized latent space; an antigen-conditioned latent generator samples an initial latent from Gaussian noise; a drifting block applies a single learned refinement step; a non-autoregressive Transformer decodes the final latent into a peptide sequence in parallel.
4. The drift field is decomposed into two forces: (i) attraction toward the matched positive binder latent for the given antigen, and (ii) repulsion away from a local neighborhood of “hard” toxic negatives (nearest toxicity-associated peptide latents), weighted by a kernel so closer negatives repel more strongly.
5. A key training issue is gradient competition because binders and toxic peptides can be close in representation space. Pepti-drift introduces a warm-up schedule: learn binding-oriented attraction first, then gradually ramp the repulsion coefficient, stabilizing learning under overlapping positive/negative distributions.
6. Dataset design supports antigen generalization: 20,547 antigen–peptide binding pairs (8,712 antigens; 13,910 peptides) plus 12,709 toxicity/hemolysis-associated peptides curated from DRAMP 4.0, ToxinPred 3.0, and Hemolytik 2.0; evaluation uses an antigen-level CD-HIT split at 90% identity to reduce train-test antigen similarity.
7. Warm-up is not cosmetic: without warm-up, drifted latents stay far from matched binders and sequence metrics degrade; with warm-up, the model achieves much better binder-proximal drift and improves uniqueness/diversity/novelty in test-time generation.
8. Efficiency is central: generating 64 peptides for each of 1,095 test antigens, Pepti-drift runs at ~0.302 ms/peptide (3,312 peptides/s), ~16.2x faster than PepMLM and ~1,092x faster than PepTune in end-to-end timing (including antigen embedding and decoding).
9. Sequence-level behavior: all methods yield 100% valid sequences, but Pepti-drift shows the strongest diversity profile (98.1% uniqueness; highest Shannon entropy) and near-zero cross-antigen reuse (0.27), indicating high antigen specificity rather than recycling the same peptides across targets.
10. Property predictions suggest a practical trade-off: Pepti-drift preserves target-related binding signal (PeptiVerse binding-affinity score below PepMLM but above PepTune) while consistently lowering predicted toxicity across length bins and reducing hemolysis risk in most ranges; an independent hemolysis predictor (HemoPI2) also reports lower mean hemolysis scores for Pepti-drift across all length bins.
📜Paper: arxiv.org/abs/2606.27824
#ComputationalBiology #ProteinDesign #PeptideDesign #GenerativeAI #MachineLearning #DrugDiscovery #Bioinformatics #ProteinLanguageModels #SafetyByDesign #Therapeutics
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