Target sequence-conditioned design of peptide binders using masked language modeling
Bibliographic record
Abstract
The computational design of protein-based binders presents unique opportunities to access 'undruggable' targets, but effective binder design often relies on stable three-dimensional structures or structure-influenced latent spaces. Here we introduce PepMLM, a target sequence-conditioned designer of de novo linear peptide binders. Using a masking strategy that positions cognate peptide sequences at the C terminus of target protein sequences, PepMLM finetunes the ESM-2 protein language model to fully reconstruct the binder region, achieving low perplexities matching or improving upon validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-based docking, we experimentally validate the efficacy of PepMLM through both binding and degradation assays. PepMLM-derived peptides demonstrate sequence-specific binding to cancer and reproductive targets, including NCAM1 and AMHR2, and enable targeted degradation of proteins across diverse disease contexts, from Huntington's disease to live viral infections. Altogether, PepMLM enables the design of candidate binders to any target protein, without requiring structural input, facilitating broad applications in therapeutic development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".