Nat. Comput. Sci. | 结构比对驱动的跨图建模实现功能性RNA设计

TL;DR - AlignIF is an RNA inverse-design framework (Nature Computational Science, 2026) that borrows the MSA idea from structure prediction and applies it in reverse: it aligns multiple structurally similar RNAs into corresponding geometric graphs and uses cross-graph attention to extract conserved 3D patterns, addressing the scarcity of experimental RNA structures.

• Architecture: per-structure geometric graphs (nodes = nucleotides; features include sugar-ring conformation, backbone/base orientation, dihedrals, inter-atomic distances) with intra-graph message passing plus cross-graph node and edge attention over aligned structures, decoded by a random-order autoregressive generator that allows fixing known functional motifs first.
• Benchmarks: 0.595 recovery / 2.221 perplexity on the benchmark set, 0.487 / 2.723 on a time-independent set, and 0.552 / 2.551 on AlphaFold3-predicted structures; even without alignment inputs it reaches 0.574, and gains grow with alignment depth. Redesigned sequences also re-fold closer to targets under AlphaFold3 self-consistency metrics.
• Ablations: removing cross-graph node updates, edge updates, or random-order decoding all degrade performance, with combined node+edge removal worst; substituting Gaussian-perturbed backbones for real homologs gives no gain, indicating the benefit comes from evolutionarily plausible conformational variation, not data augmentation.
• Wet-lab validation: 23 fluorescent aptamers and 7 self-cleaving ribozymes tested — all iMango-III/Mango-I designs fluoresced (two Mango-I designs at ~1.6x and ~1.4x wild-type, attributed to ~2x fluorophore binding capacity and more stable G-quadruplex formation by CD), and all seven pistol ribozymes cleaved (best ~11% of wild-type activity).