ProteoMapper: Alignment-Aware Identification and Quantitative Analysis of Contextual Motif-Domain Patterns in Protein Families

Protein function depends on interactions between structural domains and regulatory motifs. Yet current tools analyze these elements separately, hindering investigation of disease mutations affecting evolutionarily conserved, structurally constrained motifs. We present ProteoMapper, a computational framework integrating HMMER-based domain annotation with user-defined motif detection to quantify motif-domain spatial relationships in protein families. ProteoMapper introduces two discovery metrics: (1) positional conservation scoring, identifying motifs at identical alignment coordinates in [&ge;] N% of sequences (default 60%), indicating purifying selection; (2) Motif-Domain Coverage Score (MDCS), quantifying motif embedding within Pfam domains (MDCS=1: fully embedded; MDCS=0: extra-domain). The platform processes Excel-formatted alignments without programming requirements, delivering color-coded reports with conserved motif positions, domain boundaries, and MDCS values. Parallel execution of sequence batches enables rapid analysis (8 motifs were searched in 150 sequences with complete Pfam scanning in <6 seconds on standard hardware). Validation across three protein families confirmed technical accuracy and biological insight. In PLATZ transcription factors (24 proteins), domain predictions achieved 0.94 mean intersection-over-union versus published annotations, exactly reproducing 22 of 23 reported spans. In Arabidopsis ERD6-like sugar transporters (17 proteins), MDCS analysis revealed canonical PROSITE signatures PS00216 and PS00217 are equally domain-embedded (MDCS=1.0) but evolutionarily divergent. PS00217 shows positional conservation (58.8% of sequences) while PS00216 exhibits dispersal, suggesting subfunctionalization. In tomato actin-depolymerizing factors (11 proteins), domain detection achieved 100% sensitivity with >93% positional concordance. ProteoMapper enables hypothesis-driven investigation of evolutionary constraints, regulatory mechanisms, and variant effect prediction in biomedical and functional proteomics. Source code, documentation, and test results with datasets at https://github.com/sifullah0/ProteoMapper.