Unraveling the hidden universe of small proteins in bacterial genomes. Issue 2 (22nd February 2019) - Miravet‐Verde, Samuel; Ferrar, Tony; Espadas‐García, Guadalupe; Mazzolini, Rocco; Gharrab, Anas; Sabido, Eduard; Serrano, Luis; Lluch‐Senar, Maria
Abstract: Identification of small open reading frames (smORFs) encoding small proteins (≤ 100 amino acids; SEPs) is a challenge in the fields of genome annotation and protein discovery. Here, by combining a novel bioinformatics tool (RanSEPs) with "‐omics" approaches, we were able to describe 109 bacterial small ORFomes. Predictions were first validated by performing an exhaustive search of SEPs present in Mycoplasma pneumoniae proteome via mass spectrometry, which illustrated the limitations of shotgun approaches. Then, RanSEPs predictions were validated and compared with other tools using proteomic datasets from different bacterial species and SEPs from the literature. We found that up to 16 ± 9% of proteins in an organism could be classified as SEPs. Integration of RanSEPs predictions with transcriptomics data showed that some annotated non‐coding RNAs could in fact encode for SEPs. A functional study of SEPs highlighted an enrichment in the membrane, translation, metabolism, and nucleotide‐binding categories. Additionally, 9.7% of the SEPs included a N‐terminus predicted signal peptide. We envision RanSEPs as a tool to unmask the hidden universe of small bacterial proteins. Synopsis: RanSEPs is a random forest‐based computational approach capable of predicting small encoded proteins in a species‐specific context. Running this tool in 109 bacterial genomes indicated that up to 16 ± 9.5% of the proteins in a genome could be SEPs. Integration of transcriptomics and proteomics from 12 bacterial species showed that high‐throughput experimental characterization of small proteins (SEPs) presents multiple limitations and false positive detections. RanSEPs is a computational approach that assigns coding potential scores to SEP candidates in a species‐specific manner based on sequence features. After running RanSEPs in 109 bacterial genomes, we determined that between 6 and 25% of the proteins of a bacterial genome could be SEPs. Function prediction of RanSEPs‐predicted SEPs revealed an enrichment in translation, metabolism and nucleotide‐binding proteins. Abstract : RanSEPs is a random forest‐based computational approach capable of predicting small encoded proteins in a species‐specific context. Running this tool in 109 bacterial genomes indicated that up to 16 ± 9.5% of the proteins in a genome could be SEPs.