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Abstract Many peptide hormones form an α-helix on binding their receptors^1–4, and sensitive methods for their detection could contribute to better clinical management of disease⁵. De novo protein design can now generate binders with high affinity and specificity to structured proteins^6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion⁸to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful. 

We have developed Differential Specificity and Energy Landscape (DiSEL) analysis to comprehensively compare DNA–protein interactomes (DPIs) obtained by high-throughput experimental platforms and cutting edge computational methods. While high-affinity DNA binding sites are identified by most methods, DiSEL uncovered nuanced sequence preferences displayed by homologous transcription factors. Pairwise analysis of 726 DPIs uncovered homolog-specific differences at moderate- to low-affinity binding sites (submaximal sites). DiSEL analysis of variants of 41 transcription factors revealed that many disease-causing mutations result in allele-specific changes in binding site preferences. We focused on a set of highly homologous factors that have different biological roles but “read” DNA using identical amino acid side chains. Rather than direct readout, our results indicate that DNA noncontacting side chains allosterically contribute to sculpt distinct sequence preferences among closely related members of transcription factor families.