RFdiffusion Exhibits Low Success Rate in De Novo Design of Functional Protein Binders for Biochemical Detection

Abstract The design of high-affinity protein binders is critical for biochemical detection, yet traditional methods remain labor-intensive. AI-driven tools like RFdiffusion, a RoseTTAFold-based diffusion model, offer promising alternatives for generating protein structures with tailored binding interfaces. This study evaluates RFdiffusion’s efficacy in designing de novo binders for six targets: Strep-Tag II (a peptide tag) and five eukaryotic proteins (STAT3, FGF4, EGF, PDGF-BB, and CD4). Five binders were designed for each target and experimentally validated. While two Strep-Tag II binders outperformed streptavidin in Western blot assays, none matched the sensitivity of anti-Strep-Tag II antibodies. Binders for the other targets failed due to low expression, nonspecific binding, or undetectable affinity. Despite generating structurally diverse candidates, RFdiffusion’s success rate was limited by low-affinity designs and inconsistent recombinant expression. These results underscore the need for further optimization of AI-driven protein design tools for practical biochemical applications.