Prediction of lipoprotein signal peptides in Gram-negative bacteria

@article{citeulike:2609190, 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/. 10.1110/ps.0303703}, author = {Juncker, Agnieszka S. and Willenbrock, Hanni and von Heijne, Gunnar and Brunak, Soren and Nielsen, Henrik and Krogh, Anders }, citeulike-article-id = {2609190}, doi = {10.1110/ps.0303703}, journal = {Protein Sci}, keywords = {bioinformatics, microbiology, signal\_peptides}, month = {August}, number = {8}, pages = {1652--1662}, posted-at = {2008-03-28 19:07:50}, priority = {2}, title = {Prediction of lipoprotein signal peptides in Gram-negative bacteria}, url = {http://dx.doi.org/10.1110/ps.0303703}, volume = {12}, year = {2003} }

TY - JOUR ID - citeulike:2609190 L3 - citeulike-article-id:2609190 TI - Prediction of lipoprotein signal peptides in Gram-negative bacteria JF - Protein Sci VL - 12 IS - 8 SP - 1652 EP - 1662 N2 - 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/. 10.1110/ps.0303703 KW - bioinformatics KW - microbiology KW - signal_peptides AU - Juncker, Agnieszka AU - Willenbrock, Hanni AU - von Heijne, Gunnar AU - Brunak, Soren AU - Nielsen, Henrik AU - Krogh, Anders PY - 2003/08/01/ UR - http://dx.doi.org/10.1110/ps.0303703 ER -