Decoding the allosteric grammar of protein kinases: A dual‐stream framework integrating protein language models and energy landscape frustration analysis
The spatial and energetic encoding of allosteric regulatory sites remains a major challenge in structural biology, frequently representing a “blind spot” for sequence‐based artificial intelligence (AI) models. We present a protein language model (PLM)‐guided approach complemented by the energy landscape frustration analysis as a dual‐stream framework to investigate the relationship between AI prediction of binding sites and biophysical organization of regulatory pockets across the human kinome. By probing a fine‐tuned residue‐level PLM classifier across 453 kinase structures, a clear performance gap is discovered between highly predictable orthosteric pockets (Types I, I.5, and II) and poorly resolved distal allosteric sites (Type IV). Rather than attempting to interpret this blind spot through internal AI attributions alone, we use independent local frustration profiles to analyze the underlying physics of these sites. We determine that the detectability of orthosteric and allosteric binding sites reflects their energetic embedding within the protein energy landscape. Orthosteric catalytic sites reside within minimally frustrated, optimized energetic regions that are consistently detected with high confidence. In contrast, allosteric sites are enriched in neutrally frustrated zones, producing diffuse and context‐dependent predictions. We demonstrate that this neutral frustration of functional regions acts as a biophysical lubricant, facilitating the conformational plasticity required for regulatory transitions while simultaneously eroding the coevolutionary signals exploited by PLMs. Atomic‐resolution analysis of abelson murine leukemia (ABL) kinase spanning multiple conformational states and complexes bound to diverse ligands provides mechanistic validation of this principle. The myristoyl allosteric pocket in ABL remains neutrally frustrated across complexes with physiological ligands, chemically diverse modulators, from allosteric inhibitors to activators, and conformations engaged with SH2–SH3 regulatory domains. We propose that allosteric sites are encoded in persistent neutrally frustrated regions optimized for context‐dependent regulatory modulation. This study reveals how the organization of the protein energy landscape shapes universal “allosteric grammar” and algorithmic detectability of regulatory binding sites.