Advances in Decoding Bacterial N-Terminal Proteoforms: Technologies, Challenges, and Functional Insights.

The bacterial proteome is a highly dynamic landscape rather than simply a static reflection of the genome. Recent research has revealed that proteome complexity extends far beyond canonical gene annotation, with N-terminal (Nt-)proteoforms emerging as an important underexplored additional regulatory layer. These molecular variants originate from a single genetic locus through alternative translation initiation at internal or external in-frame start sites, thereby generating N-terminal heterogeneity that can influence protein stability, subcellular localization, interaction networks, and the stoichiometric assembly of multiprotein complexes. While recent advances in riboproteogenomics, N-terminomics, and computational annotation strategies have enabled proteoform mapping at single-amino-acid resolution, rapid high-throughput discovery currently outpaces downstream functional characterization. This review discusses the technological advances driving Nt-proteoform discovery, including emerging ribosome profiling and proteogenomic approaches, and further evaluates strategies for the functional characterization of Nt-proteoforms. Particular emphasis is placed on the transition from conventional plasmid-based heterologous expression systems towards precise genome-engineering approaches that enable selective manipulation of alternative translation initiation events within their native genomic context. Such targeted strategies are essential to bridge the gap between Nt-proteoform identification and functional understanding, ultimately uncovering how individual bacterial genomic loci can encode proteoforms with distinct and potentially divergent functional roles in bacterial physiology and pathogenesis. Ultimately, we hypothesize that alternative translation initiation represents a biologically meaningful post-transcriptional regulatory mechanism that contributes to maximizing prokaryotic coding capacity without expanding genome size, rather than merely constituting stochastic translational noise.