Target sequence-conditioned design of peptide binders using masked language modeling
Tianlai Chen, Zachary Quinn, Madeleine Dumas, Christina Peng, Lauren Hong, Moises Lopez-Gonzalez, Alexander Mestre, Rio Watson, Sophia Vincoff, Lin Zhao, Jianli Wu, Audrey Stavrand, Mayumi Schaepers-Cheu, Tian Wang, Divya Srijay, Connor Monticello, Pranay Vure, Rishab Pulugurta, Sarah Pertsemlidis, Kseniia Kholina, Shrey Goel, Matthew P. DeLisa, Jen‐Tsan Chi, Ray Truant, Hector C. Aguilar, Pranam Chatterjee
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.