ProteoMapper: Alignment-aware identification and quantitative analysis of contextual motif-domain patterns in protein...

Understanding how sequence patterns relate to conserved protein domains is fundamental for interpreting protein function and organization. Although numerous tools support motif detection and domain annotation, assessing their positional relationships across multiple sequence alignments typically requires combining outputs from separate analyses. Here, we present ProteoMapper, a Python-based toolkit for alignment-aware annotation and visualization of protein sequences. The framework integrates user-defined regular expression-based motif detection with HMMER-based domain annotation using Pfam profiles, and maps both features directly onto aligned sequences within a unified system. Results are exported as a multi-sheet spreadsheet with standardized visual annotations, enabling simultaneous inspection of motif occurrences, domain regions, and positional patterns across homologous proteins. ProteoMapper additionally computes a motif-domain coverage score (MDCS), defined as the fraction of motif residues overlapping annotated domains. This metric provides a descriptive measure of motif localization relative to domain regions and supports differentiation between domain-associated sequence signatures and motifs occurring outside annotated domains. To complement this, the tool reports positional conservation of motifs across alignments, facilitating identification of positionally constrained sequence patterns. The utility of ProteoMapper is demonstrated through case studies including validation of domain detection against published datasets, analysis of conserved sequence signatures in ERD6-like sugar transporter proteins, and evaluation of regulatory motif localization in HIF-1α sequences. ProteoMapper provides an integrated and accessible framework for visualization and basic quantification of motif and domain annotations within an alignment context, supporting exploratory analysis of protein sequence organization. Source code, documentation, and datasets are available at https://github.com/sifullah0/ProteoMapper.