Artificial Intelligence–Driven Structural Mining Enables Functional Inference in the Human Dark Proteome

The rapid diffusion of high-throughput sequencing technologies has generated a vast repertoire of protein-coding se- quences whose biological roles remain unknown. This discrepancy between sequence availability and functional under- standing has led to the definition of the dark proteome, comprising proteins or protein regions that lack experimentally resolved structures and reliable functional annotations. Classical sequence-based approaches often fail to characterize these targets due to extreme sequence divergence, intrinsic disorder, or membrane localization. Here, we present an inte- grated, structure-centric computational framework that leverages recent advances in artificial intelligence to enable func- tional inference in the human dark proteome. By combining deep learning–based protein structure prediction, large-scale structural alignment, and machine learning–driven surface pocket analysis, we uncover remote evolutionary relationships and conserved functional features that remain invisible to traditional bioinformatics pipelines. Our results demonstrate that artificial intelligence provides a powerful strategy to bridge the gap between genomic information and biological function, opening new avenues for systematic exploration of uncharacterized regions of the human proteome.