Universal physical principles of protein structural genesis emerge in language-model representation space

Abstract

Protein structure is usually treated as the endpoint of sequence by most AI models, yet its true biological emergence is a process of ordered change, whose logic remains hidden in opaque black box. ProtGenesis creates a bidirectional mirror world: it maps amino acid assembly, elongation and mutation in biological space into quantitative trajectories and ensembles in protein language model representation space, then translates spatial geometry into testable biophysical hypotheses. Across peptides, reporter proteins and protein families, three universal general principles emerged: hierarchical directional assembly (Principle I), quantitative and reproducible structural-emergence trajectories (Principle II) and discrete topological transitions (Principle III) from short- to long-range order. Three novel metrics of spatial features [D,{rho} ,{delta} ], complemented by squared Gaussian Wasserstein-2 measures, located structural anchors, sensitive regions and state transitions, enabling split-protein engineering and programmable protein design. ProtGenesis makes latent representations mechanistically interpretable, offering AI a route to discovering, rather than merely predicting, scientific principles. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=173 HEIGHT=200 SRC="FIGDIR/small/706798v2_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@18f690org.highwire.dtl.DTLVardef@e36903org.highwire.dtl.DTLVardef@35f15org.highwire.dtl.DTLVardef@1577b81_HPS_FORMAT_FIGEXP M_FIG C_FIG