EMAP-SSN: An Embedding- and Multiple-Alignment-Integrated Sequence Similarity Network Platform for Interactive Exploration of Protein Sequence Space

Abstract

Sequence similarity networks (SSNs) are graphical representations of sequence relationship frequently used for exploring protein sequence space. Conventional SSN workflows typically use BLAST to calculate sequence similarities and rely on external visualization tools to gener-ate the final networks. Consequently, raw sequence data is often detached from the calculat-ed similarities during visualization, complicating SSN analyses that require residue-level in-formation. To bridge this gap, we present EMAP-SSN, an open-source, cross-platform soft-ware suite that integrates SSN computation, visualization, and analyses in streamlined work-flows. The program provides BLAST- and embedding-based alignment pipelines for se-quence-similarity calculation and directly links network nodes to their original sequences and multiple alignments for analyses. Modular architectures for embedding generation, com-mand integration, and browser-based utilities allow additions of research-specific functionalities and facilitate future development. Using a set of fold-type IV pyridoxal 5'-phosphate-dependent enzymes, we demonstrate how EMAP-SSN connects network topology with residue-level variation to identify sequence clusters, map functional motifs, and detect subgroup-specific conservation patterns. These capabilities provide a practical route from large protein sequence sets to experimentally verifiable hypotheses on enzyme function and targets for protein engineering. The EMAP-SSN program can be accessed from https://github.com/Xuebin-Feng/EMAP-SSN.