GLproxScape reconstructs spatial chromatin occupancy landscapes from tiled genomic locus proteomics

Abstract

Genomic locus proteomics combines proximity labeling with mass spectrometry to identify the proteins associated with user-defined genomic loci. However, per-region enrichment values from tiled guide designs are typically pooled before hit calling, collapsing the latent spatial structure encoded by overlapping measurements. Here, we describe GLproxScape, an R package that treats per-region enrichments as indirect spatial measurements and reconstructs latent chromatin occupancy landscapes through a Gaussian labeling-kernel forward model. Sequence-specific transcription factors are resolved by motif-anchored non-negative least-squares deconvolution against JASPAR or HOCOMOCO position weight matrices, while chromatin regulators which lack defined DNA-binding motifs are inferred as broad occupancy zones, enabling recovery of overlapping members of multi-subunit complexes. Applied to published genomic locus proteomics datasets at the human TERT, MYC, FOXP2, and FOXQ1 loci and the mouse Ripk3 locus, GLproxScape recovered known regulators with predicted positions independently supported by ChIP-Atlas peaks, reconstructed candidate co-binding relationships, and identified chromatin complexes inaccessible to pooled analyses. Systematic sgRNA-ablation experiments further showed that densely tiled designs improve event recovery and positional stability, providing concrete experimental guidance for future genomic locus proteomics studies.