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RipplePLM addresses protein mutation effect generation by decoupling structural and biochemical information, a limitation in prior undifferentiated encoding. The framework employs Direct-Distal Cross-Attention (DDCA) to process mutation representations through immediate contact neighborhoods and multi-hop distal contexts, guided by predicted contact maps. Additionally, the Property Latent Chain (PLChain) injects biochemical property supervision via latent tokens, significantly improving generation quality and biological accuracy over baselines.
Unlocking biologically accurate protein mutation effect descriptions by disentangling structural neighborhoods and biochemical properties.
Protein mutation effect generation asks a model to describe the functional consequence of a point mutation in natural language. Existing protein-to-text systems typically encode mutation information into undifferentiated representations, overlooking the organization of mutation-induced evidence across structural and biochemical factors. We propose RipplePLM, a mutation-aware generation framework centered on Direct-Distal Cross-Attention (DDCA). By constructing a residue-level Mutation Perturbation Field from pre-trained protein language models, DDCA leverages predicted contact maps to organize mutation representations into two pathways: the mutation site's immediate contact neighborhood and its multi-hop distal context. To complement this structural decomposition, we further introduce the Property Latent Chain (PLChain), which injects expert-guided supervision of biochemical property changes (e.g., thermostability and optimal pH) into the LLM hidden-state pathway through latent property tokens. On MutaDescribe, RipplePLM improves over mutation-specific baselines on temporal and structural splits; under a matched-backbone comparison, average structural-split ROUGE-L increases from {22.23} to {35.65}. Expert evaluation further shows a higher proportion of biologically accurate or relevant descriptions than the mutation-specific baseline. Additional ablations, representation diagnostics, and low-$N$ fitness regression experiments further support the effectiveness of the learned mutation-aware representations. Code: https://github.com/Lyu6PosHao/RipplePLM.