Peptide binder design with inverse folding and protein structure prediction

The computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and AlphaFold2 (AF) in a joint framework. Foldseek generates backbone seeds for a modified version of ESM-IF1 adapted to protein complexes. The resulting sequences are evaluated with AF using an MSA representation for the receptor structure and a single sequence for the binder. We show that AF can accurately evaluate protein binders and that our bind score can select these (ROC AUC = 0.96 for the heterodimeric case). We find that designs created from seeds with more contacts per residue are more successful and tend to be short. There is a relationship between the sequence recovery in interface positions and the plDDT of the designs, where designs with >= 80% recovery have an average plDDT of 84 compared to 55 at 0%. Designed sequences have 60% higher median plDDT values towards intended receptors than non-intended ones. Successful binders (predicted interface RMSD <= 2 angstrom) are designed towards 185 (6.5%) heteromeric and 42 (3.6%) homomeric protein interfaces with ESM-IF1 compared with 18 (1.5%) using ProteinMPNN from 100 samples. Designing peptides that bind to specific protein targets is crucial for peptidic drug development, however, traditional computer-aided binder design is outperformed by AlphaFold2. Here, the authors develop a peptide binder designing tool by combining Foldseek, ESM-IF1 and AlphaFold2 to increase the success rate.

Automated summary

This article describes a computational method for designing peptide binders towards specific protein interfaces. By combining multiple methods, including Foldseek, ESM-IF1, and AlphaFold2, the researchers developed a peptide binder design tool and demonstrated its ability to improve the success rate.