MevoCraft: Motif-Scoped Evolutionary Conditioning for Multistate Protein Design

Many proteins function by switching among multiple distinct conformational states, and designing proteins with such behavior could enable new applications in synthetic biology and therapeutics. A recent method, SwitchCraft, introduces a unified framework for multistate protein design that optimizes a sequence by gradient-based hallucination through a biomolecular structure predictor. Though promising, SwitchCraft performs every forward pass without a multiple sequence alignment (MSA), omitting the evolutionary information that is typically useful for structure prediction. As a result, the gradient used to update the sequence is computed from the inaccurate structures. Incorporating an MSA into this hallucination process is challenging because the query sequence changes at every optimization step, meaningful homologs may not exist, and the sequence is often represented as a continuous distribution rather than a discrete sequence. To address these challenges, in this paper we propose a novel method, called $\underline{\text{m}}$otif-scoped $\underline{\text{evo}}$lutionary conditioning for multistate protein design (MevoCraft). MevoCraft conditions each forward pass of gradient-based hallucination with a motif MSA which is constructed by a single homology search on the fixed functional motif and injected only into states required to exhibit that motif. To the best of our knowledge, MevoCraft is the first method to incorporate an MSA into gradient-based hallucination for multistate protein design. Experiments show that MevoCraft outperforms SwitchCraft in 50 of 70 positive-allostery cases and 49 of 70 negative-allostery cases, while matching it in another 19 positive-allostery cases and 14 negative-allostery cases. In motif switching, MevoCraft produces $3.8\times$ the number of successful designs achieved by SwitchCraft.