PRISM: Enhancing Protein Inverse Folding through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations

2025-10-11

AI Summary

Protein inverse folding—generating sequences compatible with a target 3D structure—is highly challenging due to the vast sequence space and complex local structural constraints. This paper proposes a fine-grained multimodal retrieval-augmented generation framework: first, retrieving evolutionarily conserved local structure–sequence motifs from a natural protein database; second, integrating retrieved motifs with the target structure via a hybrid self-cross-attention decoder; and third, explicitly modeling and reusing these motifs through a latent-variable probabilistic model. To our knowledge, this is the first approach to incorporate fine-grained, naturally occurring structure–sequence co-occurrence patterns into inverse folding. Evaluated on five standard benchmarks, our method achieves significant improvements in amino acid recovery rate and perplexity, while simultaneously enhancing foldability metrics—including RMSD, TM-score, and pLDDT—demonstrating both effectiveness and strong generalization capability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation
• Search and Optimization: Evolutionary Computation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Designing protein sequences that fold into a target three-dimensional structure, known as the inverse folding problem, is central to protein engineering but remains challenging due to the vast sequence space and the importance of local structural constraints. Existing deep learning approaches achieve strong recovery rates, yet they lack explicit mechanisms to reuse fine-grained structure-sequence patterns that are conserved across natural proteins. We present PRISM, a multimodal retrieval-augmented generation framework for inverse folding that retrieves fine-grained representations of potential motifs from known proteins and integrates them with a hybrid self-cross attention decoder. PRISM is formulated as a latent-variable probabilistic model and implemented with an efficient approximation, combining theoretical grounding with practical scalability. Across five benchmarks (CATH-4.2, TS50, TS500, CAMEO 2022, and the PDB date split), PRISM establishes new state of the art in both perplexity and amino acid recovery, while also improving foldability metrics (RMSD, TM-score, pLDDT), demonstrating that fine-grained multimodal retrieval is a powerful and efficient paradigm for protein sequence design.

Problem

• Designing protein sequences for target 3D structures
• Reusing conserved structure-sequence patterns from natural proteins
• Improving accuracy and foldability in protein inverse folding

Innovation

• Retrieves fine-grained motifs from known proteins
• Integrates motifs with hybrid self-cross attention decoder
• Uses latent-variable probabilistic model with efficient approximation