AI Insight
Researchers developed zPolaron, a graph neural network that improves protein design by simultaneously considering structural compatibility, protein-ligand interactions, and evolutionary information—three factors that existing models fail to integrate. The system represents proteins and ligands as interconnected graphs and combines physical interaction data with evolutionary sequence embeddings from ESM-2. Laboratory validation demonstrated substantial improvements in enzyme activity, including a 17.84-fold increase for one CalA mutant and a 1.91-fold increase for a CalB mutant, outperforming existing fitness prediction methods across multiple benchmarks.
Why it matters
This approach advances rational protein design beyond simply matching structure to sequence by incorporating functional constraints, potentially accelerating the development of optimized enzymes for industrial applications, therapeutics, and biotechnology. The integration of interaction data addresses a critical gap in current protein design tools that often produce structurally sound but functionally suboptimal proteins.
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⚠️ Preprint – Noch nicht peer-reviewed
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Protein inverse folding, which infers compatible amino acid sequences from a given backbone structure, is central to rational protein design. Current methods rely largely on sequence recovery rate as the evaluation metric, emphasizing structural compatibility but seldom addressing function. Yet functional activity depends on the proper network of non-covalent interactions-protein-ligand contacts being a prime example. We argue that function-oriented protein design is governed by three complementary constraints: structural, interaction, and evolutionary, none of which are handled in a unified manner by existing pretrained models. To bridge this gap, we introduce zPolaron, a graph neural network built on protein-ligand complexes for functional protein design. zPolaron represents residues and ligand atoms as a heterogeneous graph and jointly encodes structural, interaction, and evolutionary information by fusing physical-interaction-enhanced graph representations with ESM-2 embeddings. Across multiple fitness prediction benchmarks, zPolaron outperforms all baselines, with gains evident across structural, interaction, and evolutionary dimensions. Wet-lab experiments further confirm its utility in functional enzyme screening and design: the optimized CalA6-D205A-S160G mutant achieves a 17.84-fold improvement in activity, while the CalB-I121M mutant yields a 1.91-fold increase. Together, these results show that integrating interaction and evolutionary information fills the missing puzzle of inverse folding, enhancing protein fitness prediction beyond structural compatibility. zPolaron is available at https://github.com/zelixirSH/zPolaron.