RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design

Sequences produced by state-of-the-art backbone-conditioned design models such as LigandMPNN often fail to fold into the target structure when checked with an independent structure predictor. We introduce RosettaSearch, an inference-time, multi-objective search that repairs such sequences without any model retraining. RosettaSearch uses an LLM as a generative optimizer inside a priority-based parallel search. At each step, the LLM proposes targeted edits after reading structured feedback from RosettaFold3: global rewards (pLDDT, TM-score, C$\alpha$-RMSD), residue-level annotations of problem regions, and text constraints that discourage reward hacking. On 400 LigandMPNN designs for native PDB monomers, RosettaSearch improves fidelity metrics by 18% to 68% and raises the design success rate 2.5$\times$ (7.9% to 20.5%). The gains persist under two independent predictors not used in the search (Chai-1 and Boltz-2), and a random-mutation baseline with identical feedback and oracle budget shows no improvement. On 275 de novo Dayhoff backbones, RosettaSearch raises the success rate of ProteinMPNN designs from 72% to 90%, and it rescues sequences for difficult to design backbones on which ProteinMPNN fails. RosettaSearch can also optimize a physics-based Rosetta energy term, and a small policy fine-tuned with a MultiMax reinforcement-learning reward improves the search's success rate. In a preliminary wet-lab test, we find that a designed sequence for a de novo backbone shows a single cooperative thermal unfolding transition at 89 ${}^{\circ }$C. To our knowledge, this is the first large-scale demonstration that LLMs can serve as effective generative optimizers for backbone-conditioned protein sequence design.