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An AI-enabled structural atlas decodes kinase specificity across the human proteome
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DOI：10.1038/s41587-026-03239-5.png)
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Abstract
En 中文
Of the 1.8 million serine/threonine/tyrosine residues in the human proteome, only 6% bear experimental validation of phosphorylation, and only 5% of these have been mapped to a kinase. Here we present KinoPlex, a computational framework that integrates predicted protein structures and kinase recognition motifs to assign phosphorylation potential and kinase specificity to all serine/threonine/tyrosine residues. Using ~20,000 AlphaFold models and positive-unlabeled transfer learning, we identified ~567,000 residues as structurally phospho-competent. We intersected these with kinase position-specific scoring matrices to quantify motif specificity, yielding ~250,000 high-confidence candidates with sequence recognition potential and optimal structural presentation. The structural atlas uncovered fundamental organizing principles guiding kinase substrate recognition and dynamics of phosphorylation, including a phenomenon we call sequence–structure selective coupling, whereby kinases achieve specificity through structural scarcity of their preferred motif (negative-selecting kinases) or promiscuity through its structural accessibility (positive-selecting kinases), rather than by motif discrimination alone. Deep phosphoproteomics in K562 cells validates KinoPlex predictions and kinase enrichment capacities. Phosphorylation potential and kinase specificity are assigned for the entire human proteome.
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41.7
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