Identifying all essential genomic components is critical for the assembly of minimal artificial life. In the genome-reduced bacterium Mycoplasma pneumoniae, we found that small ORFs (smORFs; < 100 residues), accounting for 10% of all ORFs, are the most frequently essential genomic components (53%), followed by conventional ORFs (49%). Essentiality of smORFs may be explained by their function as members of protein and/or DNA/RNA complexes. In larger proteins, essentiality applied to individual domains and not entire proteins, a notion we could confirm by expression of truncated domains. The fraction of essential non-coding RNAs (ncRNAs) non-overlapping with essential genes is 5% higher than of non-transcribed regions (0.9%), pointing to the important functions of the former. We found that the minimal essential genome is comprised of 33% (269,410 bp) of the M. pneumoniae genome. Our data highlight an unexpected hidden layer of smORFs with essential functions, as well as non-coding regions, thus changing the focus when aiming to define the minimal essential genome.

A new genome-scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome-reduce human pathogen.

Identification of small open reading frames (sm ORF s) encoding small proteins (≤ 100 amino acids; SEP s) is a challenge in the fields of genome annotation and protein discovery. Here, by combining a novel bioinformatics tool (Ran SEP s) with “‐omics” approaches, we were able to describe 109 bacterial small ORF omes. Predictions were first validated by performing an exhaustive search of SEP s present in Mycoplasma pneumoniae proteome via mass spectrometry, which illustrated the limitations of shotgun approaches. Then, Ran SEP s predictions were validated and compared with other tools using proteomic datasets from different bacterial species and SEP s from the literature. We found that up to 16 ± 9% of proteins in an organism could be classified as SEP s. Integration of Ran SEP s predictions with transcriptomics data showed that some annotated non‐coding RNA s could in fact encode for SEP s. A functional study of SEP s highlighted an enrichment in the membrane, translation, metabolism, and nucleotide‐binding categories. Additionally, 9.7% of the SEP s included a N‐terminus predicted signal peptide. We envision Ran SEP s as a tool to unmask the hidden universe of small bacterial proteins.