Prediction of lipoprotein signal peptides in Gram‐negative bacteria

TLDR

A method, LipoP, was developed to predict lipoprotein signal peptides in Gram‑negative Eubacteria. The method uses a hidden Markov model that distinguishes lipoproteins, SPaseI‑cleaved, cytoplasmic, and transmembrane proteins, was applied to 12 Gram‑negative and one Gram‑positive genomes, and is available as a web server at www.cbs.dtu.dk/services/LipoP/. The predictor correctly identified 96.8 % of Gram‑negative lipoproteins with only 0.3 % false positives, outperformed prior methods, achieved 92.9 % accuracy on a Gram‑positive test set, and its predictions for *E.

Abstract

Abstract A method to predict lipoprotein signal peptides in Gram‐negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII‐cleaved proteins), SPaseI‐cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI‐cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram‐positive lipoprotein signal peptides differ from Gram‐negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram‐positive test set. A genome search was carried out for 12 Gram‐negative genomes and one Gram‐positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network‐based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/ .