Dataset Information

Lipoprotein computational prediction in spirochaetal genomes.

ABSTRACT: Lipoproteins are of great interest in understanding the molecular pathogenesis of spirochaetes. Because spirochaete lipobox sequences exhibit more plasticity than those of other bacteria, application of existing prediction algorithms to emerging sequence data has been problematic. In this paper a novel lipoprotein prediction algorithm is described, designated SpLip, constructed as a hybrid of a lipobox weight matrix approach supplemented by a set of lipoprotein signal peptide rules allowing for conservative amino acid substitutions. Both the weight matrix and the rules are based on a training set of 28 experimentally verified spirochaetal lipoproteins. The performance of the SpLip algorithm was compared to that of the hidden Markov model-based LipoP program and the rules-based algorithm Psort for all predicted protein-coding genes of Leptospira interrogans sv. Copenhageni, L. interrogans sv. Lai, Borrelia burgdorferi, Borrelia garinii, Treponema pallidum and Treponema denticola. Psort sensitivity (13-35 %) was considerably less than that of SpLip (93-100 %) or LipoP (50-84 %) due in part to the requirement of Psort for Ala or Gly at the -1 position, a rule based on E. coli lipoproteins. The percentage of false-positive lipoprotein predictions by the LipoP algorithm (8-30 %) was greater than that of SpLip (0-1 %) or Psort (4-27 %), due in part to the lack of rules in LipoP excluding unprecedented amino acids such as Lys and Arg in the -1 position. This analysis revealed a higher number of predicted spirochaetal lipoproteins than was previously known. The improved performance of the SpLip algorithm provides a more accurate prediction of the complete lipoprotein repertoire of spirochaetes. The hybrid approach of supplementing weight matrix scoring with rules based on knowledge of protein secretion biochemistry may be a general strategy for development of improved prediction algorithms.

SUBMITTER: Setubal JC

PROVIDER: S-EPMC2667199 | biostudies-literature | 2006 Jan

REPOSITORIES: biostudies-literature

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Lipoprotein computational prediction in spirochaetal genomes.

Setubal João C JC Reis Marcelo M Matsunaga James J Haake David A DA

Microbiology (Reading, England) 20060101 Pt 1

Lipoproteins are of great interest in understanding the molecular pathogenesis of spirochaetes. Because spirochaete lipobox sequences exhibit more plasticity than those of other bacteria, application of existing prediction algorithms to emerging sequence data has been problematic. In this paper a novel lipoprotein prediction algorithm is described, designated SpLip, constructed as a hybrid of a lipobox weight matrix approach supplemented by a set of lipoprotein signal peptide rules allowing for ...[more]

PMID: 16385121

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Project description:BackgroundCyclic AMP receptor protein (CRP), also known as catabolite gene activator protein (CAP), is an important transcriptional regulator widely distributed in many bacteria. The biological processes under the regulation of CRP are highly diverse among different groups of bacterial species. Elucidation of CRP regulons in cyanobacteria will further our understanding of the physiology and ecology of this important group of microorganisms. Previously, CRP has been experimentally studied in only two cyanobacterial strains: Synechocystis sp. PCC 6803 and Anabaena sp. PCC 7120; therefore, a systematic genome-scale study of the potential CRP target genes and binding sites in cyanobacterial genomes is urgently needed.ResultsWe have predicted and analyzed the CRP binding sites and regulons in 12 sequenced cyanobacterial genomes using a highly effective cis-regulatory binding site scanning algorithm. Our results show that cyanobacterial CRP binding sites are very similar to those in E. coli; however, the regulons are very different from that of E. coli. Furthermore, CRP regulons in different cyanobacterial species/ecotypes are also highly diversified, ranging from photosynthesis, carbon fixation and nitrogen assimilation, to chemotaxis and signal transduction. In addition, our prediction indicates that crp genes in modern cyanobacteria are likely inherited from a common ancestral gene in their last common ancestor, and have adapted various cellular functions in different environments, while some cyanobacteria lost their crp genes as well as CRP binding sites during the course of evolution.ConclusionThe CRP regulons in cyanobacteria are highly diversified, probably as a result of divergent evolution to adapt to various ecological niches. Cyanobacterial CRPs may function as lineage-specific regulators participating in various cellular processes, and are important in some lineages. However, they are dispensable in some other lineages. The loss of CRPs in these species leads to the rapid loss of their binding sites in the genomes.

Project description:BackgroundAccurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.ResultsBased on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, i.e. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.ConclusionLarge-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.

Dataset Information

Lipoprotein computational prediction in spirochaetal genomes.

Publications

Lipoprotein computational prediction in spirochaetal genomes.

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