GLproxScape reconstructs spatial chromatin occupancy landscapes from tiled genomic locus proteomics

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 tiledguide designs are typically pooled before hit calling, collapsing the latent spatial structure encodedby overlapping measurements. Here, we describe GLproxScape, an R package that treats per-region enrichments as indirect spatial measurements and reconstructs latent chromatin occupancylandscapes through a Gaussian labeling-kernel forward model. Sequence-specific transcriptionfactors are resolved by motif-anchored non-negative least-squares deconvolution against JASPARor HOCOMOCO position weight matrices, while chromatin regulators which lack defined DNA-binding motifs are inferred as broad occupancy zones, enabling recovery of overlapping membersof multi-subunit complexes. Applied to published genomic locus proteomics datasets at the humanTERT, MYC, FOXP2, and FOXQ1 loci and the mouse Ripk3 locus, GLproxScape recovered knownregulators with predicted positions independently supported by ChIP-Atlas peaks, reconstructedcandidate co-binding relationships, and identified chromatin complexes inaccessible to pooledanalyses. Systematic sgRNA-ablation experiments further showed that densely tiled designsimprove event recovery and positional stability, providing concrete experimental guidance for futuregenomic locus proteomics studies.