ProChoreo: de novo Binder Design from Conformational Ensembles with Generative Deep Learning

Abstract Deep learning has transformed protein structure prediction and de novo protein design; however, most existing frameworks operate on a single static conformation and underutilize the conformational heterogeneity that governs protein binding and function. We introduce ProChoreo, a generalizable framework for de novo binder design that explicitly incorporates conformational ensembles. ProChoreo is pretrained with multimodal contrastive learning to align protein sequences with corresponding molecular dynamics (MD)-derived ensembles, producing a shared latent representation that captures both sequence-level and dynamic structural information. This representation is then integrated into an autoregressive generator to design protein binders conditioned on receptor sequences. Designed binders are evaluated using Boltz 1 for complex structure and interaction quality, followed by MD simulations of complexes with two representative receptors: the human sweet taste receptor TAS1R2 and FGFR2. ProChoreo designs binders that encode conformational features, highlighting dynamics-informed design as a route to protein design. Competing Interest Statement The authors have declared no competing interest. Footnotes Saisai Ding. E-mail: sqd5856{at}psu.edu Code is inlcuded in the revision: https://github.com/SaaaaiDing/PD_NLP