PepMLM

What is PepMLM?

PepMLM is a de novo peptide binder design tool that generates linear peptide sequences predicted to bind specific target proteins. Developed by the Chatterjee Lab and published in Nature Biotechnology in 2025, PepMLM employs a target sequence-conditioned masked language modeling approach built on ESM-2, a state-of-the-art protein language model.

Unlike structure-based design methods that require 3D coordinates of the target protein, PepMLM operates exclusively from amino acid sequences. The model positions peptide binders at the C-terminus of target proteins and reconstructs the binder region through span masking, enabling generative design of candidate binders to any target protein without structural information.

PepMLM was experimentally validated through binding assays and targeted protein degradation studies, demonstrating efficacy against cancer biomarkers, viral phosphoproteins, and Huntington's disease-related proteins.

How to use PepMLM online

ProteinIQ provides a web-based interface for running PepMLM without command-line installation or GPU configuration. Paste or upload target protein sequences, adjust generation parameters, and receive ranked peptide binder candidates with confidence scores.

Inputs

Settings

Generation parameters

Results

The output presents a ranked table of peptide binder candidates with confidence metrics.

Interpreting pseudo-perplexity

Pseudo-perplexity quantifies how "surprised" the ESM-2 model is by the generated peptide sequence conditioned on the target protein. The metric is calculated exclusively on the masked binder region by measuring reconstruction loss.

• < 15 — Very high confidence, strong sequence likelihood
• 15–25 — High confidence, typical for validated binders
• 25–35 — Moderate confidence, may benefit from experimental screening
• > 35 — Lower confidence, consider adjusting parameters or target sequence

In the original benchmarking, pseudo-perplexity showed statistically significant negative correlation (p < 0.01) with AlphaFold-Multimer structural metrics (ipTM and pLDDT scores), validating its use as a selection criterion.

How does PepMLM work?

PepMLM adapts the ESM-2 protein language model for conditional peptide generation through a novel masking strategy. The approach treats binder design as a sequence reconstruction problem where peptides are predicted given their target protein context.

Masked language modeling

The core innovation positions peptide sequences at the C-terminus of target proteins during training. The entire binder region is masked, forcing the model to reconstruct it based solely on the target sequence context. This contrasts with traditional masked language models that mask random tokens throughout the sequence.

During inference, the model receives a target protein sequence concatenated with a masked peptide template of length $L$. Top-k sampling with categorical probability distributions generates amino acids position-by-position, producing diverse binder candidates rather than a single deterministic prediction.

Training methodology

PepMLM fine-tunes ESM-2 (650M parameters) on merged datasets from PepNN and Propedia, comprising 10,000 training samples and 203 test sequences. The model constrains binders to maximum 50 amino acids and target proteins to 500 residues.

The training objective minimizes masked language modeling loss exclusively on binder regions:

$$\mathcal{L} = -\sum_{i \in \text{masked}} \log P(x_i | x_{\text{target}}, x_{\text{context}})$$

where $x_i$ represents amino acids in the masked binder region, $x_{\text{target}}$ is the target protein sequence, and $x_{\text{context}}$ includes already-generated positions.

Benchmark performance

In comparative studies, PepMLM achieved superior performance against RFdiffusion on structured targets:

• Hit rate — >38% success rate compared to RFdiffusion's <30%
• Binding affinity — Top peptides demonstrated significantly stronger binding to NCAM1 than all RFdiffusion candidates
• Structural validation — AlphaFold-Multimer predictions confirmed binding pockets matching validated test sequences

Limitations

• Linear peptides only — Does not design cyclic peptides, stapled peptides, or structures with disulfide bridges
• No structural optimization — Generated sequences represent initial candidates that may benefit from cyclization, stapling, or other post-design modifications
• Sequence-only conditioning — Cannot incorporate structural constraints or binding site information when available
• Unknown amino acid tokens — Some positions may generate X (any amino acid), requiring manual substitution or experimental screening
• No affinity prediction — Pseudo-perplexity indicates sequence likelihood but does not directly predict binding affinity or $K_d$ values

Related tools

• BindCraft — Structure-based binder design using RFdiffusion for targets with known 3D structures
• RFdiffusion — Diffusion-based protein design that can generate binder backbones from target structures
• ProteinMPNN — Sequence design for fixed protein backbones using message-passing neural networks
• AlphaFold 2 — Structure prediction tool for validating designed peptide-protein complexes
• ESM-2 — Protein language model for generating sequence embeddings and property predictions

Applications

• Targeted protein degradation — Design peptide-E3 ubiquitin ligase fusions for inducing degradation of disease proteins, demonstrated with Huntington's disease proteins (mHTT, MSH3) and viral phosphoproteins
• Therapeutic development — Generate peptide binder candidates for cancer biomarkers (NCAM1, AMHR2) and reproductive targets without requiring structural data
• Proteome editing — Enable programmable manipulation of cellular protein levels through sequence-specific binding and functional fusion proteins
• Drug discovery — Rapidly screen peptide candidates against novel targets where structural information is unavailable or difficult to obtain
• Antiviral therapeutics — Design binders for emerging viral proteins, with experimental validation showing >50% protein reduction for select candidates against Nipah, Hendra, and metapneumovirus phosphoproteins