Accelerated expression and biophysical validation of computationally designed de novo binders targeting pMHC-I/NRASQ61K

Immunotherapy based on the modulation of cellular immune responses represents one of the most promising approaches in modern oncology. However, several oncogenic peptides, such as those derived from mutations in the NRAS protein, exhibit low immunogenicity when presented by major histocompatibility complex class I (pMHC-I) molecules, thereby escaping recognition by native cytotoxic T-cell receptors (TCRs). In this context, de novo computational protein design emerges as a versatile tool to overcome this limitation, enabling the creation of synthetic binders engineered for site-specific recognition on pMHC-I complexes.

Aiming to establish a high-throughput screening pipeline capable of evaluating candidate design plates within a 1–2 day turnaround, this study developed an integrated workflow for the in silico design and accelerated biophysical validation of de novo binders targeting pMHC-I/NRAS^Q61K. Candidate backbones and sequences were generated using RFdiffusion and LigandMPNN, followed by structural screening via PyRosetta and AlphaFold. A panel of 48 de novo designs alongside 2 single-chain TCRs (scTCRs; positive controls) was selected and synthesized directly from linear expression templates (LETs) using an in vitro cell-free transcription-translation system, successfully confirming protein production. Concurrently, the pMHC-I complex presenting the NRAS^Q61K peptide was heterologously expressed in bacteria, refolded in vitro, site-specifically biotinylated, and purified by size-exclusion chromatography. Target immobilization on streptavidin (SSA) biosensors at 1 µg/mL for Biolayer Interferometry (BLI) was validated, yielding a stable and distinct binding response signal. 

Consequently, integrating LET-driven cell-free expression with BLI functional assays provides a high-throughput framework designed to shorten the design–build–test cycle to 1–2 days. This strategy establishes the experimental foundation to rapidly screen and identify lead de novo binders with high-affinity recognition toward pMHC-I/NRASQ61K, while providing essential biophysical data to fuel the iterative feedback loop with in silico modeling against challenging therapeutic targets.