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What is the difference between a signal peptide and a transit peptide?

What is the difference between a signal peptide and a transit peptide?


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From what I know, the two names are used interchangeably and I haven't found any resource which says otherwise either. Is there at all any difference, is there a transit peptide that is not a signal peptide or vice versa?


Signal peptides are typically located at the N terminus of a protein. The signal peptides are processed by the translocon machinery and are cleaved off after sorting through the membranes of organelles in the secretory system:

  • endoplasmic reticulum
  • Golgi apparatus
  • ER-Golgi transition vesicles
  • plasma membrane
  • lysosomes

Transit peptides target the protein to other subcellular organelles such as (from UniProt):

  • Mitochondrion
  • Apicoplast
  • Chromoplast
  • Chloroplast
  • Cyanelle
  • Thylakoid
  • Amyloplast
  • Peroxisome
  • Glyoxysome
  • Hydrogenosome

N-terminal transit peptides are quite rare. C-terminal transit peptide motifs are much more common. UniProt holds transit peptides as a discrete controlled vocabulary, separate from signal peptides.


Signal Peptide

The region of a messenger RNA (mRNA) molecule that precedes the coding sequence of a gene is called the ‘leader sequence’. This region is also known as the ‘five prime untranslated region’ (Figure 1) of the mRNA. Leader sequences have the propensity for forming secondary structures (stem-loops) by base pairing of complementary sequences. They are involved in the regulation of gene expression in eukaryotes and prokaryotes. In eukaryotes, the leader sequence may vary from few nucleotides to more than 1000 nucleotides. In prokaryotes, the leader sequences are usually short and at times contain an attenuator segment that is translated to a short-leader peptide. The leader peptide functions to terminate transcripts before the RNA polymerase reaches the first structural gene of the operon. The leader sequences in viruses have been shown to play an important role in the regulation of gene expression, replication, and pathogenicity. Mutations in the leader sequences of cellular mRNAs can have implications for disease and tumorigenesis.


Background

Primary plastids are organelles of endosymbiontic origin [e.g. 1, 2]. In the course of the transition from an (endo-)symbiont to an organelle, most of its genes were either lost or, to a higher degree, transferred into the cell nucleus [e.g. 3, 4, 5]. Hence, most of the plastid proteome is encoded in the nucleus of the host cell, implying that the encoded proteins must be transported post-translationally across the two envelope membranes into the plastid lumen. For accurate trafficking, nearly all nuclear-encoded plastid proteins are equipped with a characteristic N-terminal topogenic signal sequence, the transit peptide [6]. This targeting information is necessary and sufficient for plastid import and interacts with translocons of the outer/inner envelope membrane of chloroplasts [TOC and TIC recently reviewed in 7]. Interestingly, surveys of transit peptides indicate no strict consensus sequence [8] but some common features such as a positive net charge, elevated levels of hydroxylated amino acids and binding motifs for molecular chaperones [9 and references therein].

Secondarily evolved organisms such as diatoms, apicomplexa or cryptophytes harbour plastids surrounded by two additional membranes [10, 11]. Genomic analyzes indicated a common set of nuclear-encoded proteins with a plastid destination as in primary plastids [4]. In contrast to the primary plastids, proteins here are equipped with a bipartite topogenic signal sequence (BTS), consisting of a classical ER-like signal peptide (SP) followed by a transit peptide-like sequence (TP) [2, 12, 13]. This transit peptide-like sequence is - as in archaeplastida - indispensable for plastid import as shown by in vivo experiments on apicomplexa and diatoms [5, 14, 15]. Recently, Tonkin et al. [16] demonstrated that even randomly picked sequences, which follow the basic rules for transit peptides (see above), could function as targeting sequences in apicomplexa, indicating a low complexity of transit peptides. However, in diatoms and cryptophytes, at least one major difference to the apicomplexan transit peptide composition exists, which is the presence of a highly conserved aromatic amino acid at position +1 of the TP crucial for plastid protein import [5, 15, 17]. The TPs of apicomplexa are not as heavily dependent on the phenylalanine as diatoms and cryptophytes [18].

In order to investigate further features in secondary transit peptide-like regions, we comprehensively studied in the diatom Phaeodactylum tricornutum the targeting behaviour of GFP fused to the BTS of the fucoxanthin-chlorophyll a/c binding protein D (FcpD) with modifications in the transit peptide-like region. P. tricornutum is the most appropriate system for such studies, since - contrary to apicomplexan parasites like Plasmodium falciparum - intermediates that are either transported across one of the four surrounding membranes into the chloroplast ER (cER) only or transported across two into the periplastid compartment (PPC) (Figure 1) [1] can be easily monitored and discriminated from completed import (across all four envelope membranes). Our studies confirmed that (i) a positive net charge is critical for protein transport across the innermost two plastid membranes (in case of an aromatic amino acid at the +1 position of the TP), whereas transport across the second outermost membrane obviously is not governed in that way. Here, negative charges hinder a membrane passage. Moreover, we demonstrate that (ii) the N-terminus of the mature protein can contribute to the functional necessities of the transit peptide-like sequence. Thus, our findings may additionally indicate how transit peptide-like regions have evolved during the course of evolution.

Schematic depiction of the plastid architecture of P. tricornutum. The complex plastid is surrounded by four membranes (counted from outside to inside) with the outermost one being continuous with the endoplasmic reticulum. The cER is studded with ribosomes facilitating co-translational import of plastid precursors across the 1 st membrane into the ER lumen. The candidates for translocons of the subsequent membranes (not shown) of secondary plastids with red algal ancestry have been elucidated recently [see 30, 31, 32, 33, 38, 40, 41, 42]. cER, chloroplast endoplasmic reticulum PPC, periplastid compartment IMS, intermembrane space.


MATERIALS AND METHODS

Training and test sets

Olof Emanuelsson (Stockholm Bioinformatics Center) supplied the 150 sequence ChloroP data set ( 3), which we randomly divided into 20 pairs of training and validation sets for the purpose of setting the parameters of our method. Training sets consisted of 124 sequences, and validation sets consisted of the remaining 26 sequences with each set containing equal numbers of in-class (e.g. cTP) and out-of-class examples. Note that when we use the phrase ‘validation set testing’ we refer to testing done on a partition of the training set. For final testing, we downloaded the TargetP training set ( 5), and used SWISS-PROT accession numbers to remove those sequences already contained in the ChloroP training set. The TargetP training set consisted of 371 mitochondrial transit peptides (mTP), 269 secretory pathway/signal peptide (SP), 48 ‘nuclear’ (Nuc), and 87 ‘cystolic’ (Cyt) sequences from which we removed 17, 14, 9 and 10 sequences respectively. The SP, Nuc and Cyt sequences were all from the TargetP ‘plant set’. From the 141 cTP sequences we removed 28 redundant sequences. These were the only sequences removed, and the remaining test set contains 113 in-class and 725 out-of-class sequences.

Encoding a protein

For our PCLR, logistic regression, and neural network models, the input size is 21. The first 20 inputs consist of percentages of amino acid composition in the first 55 positions of the protein sequence. The 21st input is a measure of variance of the particular protein’s amino acid distribution in the first 55 positions. Our methods performed similarly on the validation sets with sequence lengths between 45 and 60, but ultimately a length of 55 was chosen for our study, based on sum of squared errors (SSE) measurements.

Principal component logistic regression

Principal components analysis is a method of factoring co-linearity out of data and reducing dimensionality for a machine learning algorithm ( 6). We performed principal component analysis and subsequent stepwise logistic regression on the first 12 components (ordered by decreasing eigenvalue magnitude) on the principal component matrix using the R statistics package ( 7). We transformed testing data into the training data principal component space before generating prediction results.

The logistic regression always makes predictions between (0,1), but we require a threshold to use for classification. Based on ‘total number correct’ counts during validation set testing we chose a decision threshold of 0.42 for classification (e.g. a prediction of 0.41 means our method predicts ‘non-chloroplast targeting’). After deciding on a number of principal components to consider and the classification threshold, we trained PCLR on the entire ChloroP training set. The resulting predictor, principal components, and regression coefficients are available online at http://apicoplast.cis.upenn.edu/pclr/.

Logistic regression

We attempted a standard stepwise logistic regression in addition to the principal component stepwise logistic regression to see if a simpler model would provide equal performance. In the R package we used the same input to the logistic regression as in the PCLR case. A decision threshold of 0.40 was selected during validation set testing and then used on the TargetP test set.

Neural network

We used NevProp4r1, a standard feed-forward neural network with sigmoidal hidden units and one sigmoidal output unit (http://www.scs.unr.edu/nevprop). We used the same inputs as in the PCLR case described above. The number of hidden units was varied from 1 to 12, with peak performance occurring with 4 hidden units and decreasing performance soon after. A weight decay of 0.005 was chosen based on validation set performance. For training, we picked a maximum iteration of 700, and used NevProp’s auto-train switch to pick a good stopping point. Based on validation set performance (total number correct), we chose a classification threshold of 0.59.

The ChloroP neural network architecture

The ChloroP architecture is described in Emanuelsson et al. ( 3) however, for clarification and comparative purposes a brief description is included. ChloroP consists of two neural networks where the output of the first network against a set of different inputs feeds into the second neural network for a final prediction. The input to the first network consists of a sliding window of 51 amino acids from the first 100 positions of a protein. There are 100 ordered windows per protein, and they start so that the first window consists of the first 51 amino acids of the protein sequence. Shifting the previous window to the right one place forms each subsequent window. As windows overlap an area past position 100, ‘blank’ amino acids feed into the predictor. 100 of these windows feed into the first layer, and so 100 predictions are made.

The first network consists of 1020 input units, 2 hidden units and 1 output unit. The rather large number of input units is the result of using categorical data in a neural network. There are 20 possible attributes (amino acids) in a position, and so each position has 20 input units. Only one of these units is turned on (denoted by ‘1.0’): the other 19 are left at ‘0.0’. Hence, a window of 51 positions requires 51 × 20 = 1020 input units. Compounding this explosion in input size are the 100 windows per protein sequence that feed into the first layer network. All together, it takes 102 000 total inputs to the first-layer network to make a prediction on a single protein. The second layer network has 100 input units, 10 hidden units and 1 output unit. For both networks, sigmoidal units are used in hidden and output layers.

We benchmarked the ChloroP model using the web-accessible ChloroPv1.1 release located at http://www.cbs.dtu.dk/services/ChloroP/. We used the classification threshold 0.50 as suggested by Emanuelsson et al. ( 3).


Materials and Methods

Identification of the phytoplasma homologous sequences was performed using the BLASTP software (Camacho et al., 2009) against the ‘non-redundant’ database (NCBI Resource Coordinators, 2018) with default parameters at the NCBI website. For the SAP54 dataset, the sequences from the phyl-B group of Iwabuchi et al. (2020) were excluded as they did not show the phyllody inducing phenotype observed with other members, although they may still have a functional signal peptide and yet-to-discover functions. For Amp and Imp, that can be highly variable, we first extracted from draft or complete phytoplasma genomes the coding sequences located between groEL and nadE, and DnaD and PyrG respectively. We then used the translated sequences as BLASTP queries to retrieve the full dataset of Amp and Imp homologous sequences. To ensure that our dataset was as exhaustive as possible, a keyword search (𠇊ntigenic membrane protein phytoplasma” and “imp” respectively) was also performed at Genbank, and validated hits from both strategies were merged.


Steps in the secretory protein production process that are affected by signal peptides

As mentioned above, signal peptides discriminate exported proteins from proteins that remain in the cytosol. Signal peptides mediate the targeting and binding of exported precursor proteins to the respective protein translocases in the cytoplasmic membrane [49]. An additional important role of Sec signal peptides that mediate a posttranslational mode of export is to slow down the folding of the attached mature protein part to allow its efficient interaction with posttranslationally interacting proteins (such as SecB) and, by this means, help to maintain the respective export proteins in their export competent state [50, 51]. Furthermore, the gene regions for Sec signal peptides have a strong bias for non-optimal codons, a feature that by slowing down the kinetics of translation has a profound positive effect on the export efficiency and the overall productivity of the secretory production process [52]. Replacing the non-optimal codons by optimal codons in the gene regions for the signal peptides of the Escherichia coli maltose-binding protein [53] or β-lactamase [54] resulted in lower protein production which could be partially increased in strains that are defective in multiple proteases or at lowered temperatures. This indicates that slowing down the rate of translation by means of the rare codons present in Sec signal peptides is highly important to ensure an efficient interaction of the export proteins with the components of the export machinery and to prevent their degradation. Additionally, Sec signal peptides have also been found to function as allosteric activators of the Sec translocase [55].

Besides these steps in the secretory protein production pathway that directly determine the efficiency and kinetics by which a protein is targeted to and translocated across the cytoplasmic membrane, signal peptides also indirectly have an effect on the overall production process. For example, the fusion of different signal peptides to a given target protein results in different mRNA transcripts that can vary in their secondary structure and/or in their stability and, due to this, significantly can influence the amounts of the respective precursor proteins that are synthesized [56, 57].


Results

We evaluated the performance of Philius on the development dataset using ten-fold cross-validation. We measured the performance of the model as well as the accuracy of all three types of confidence scores. For proteins containing a signal peptide, we also considered the accuracy with which the cleavage site is localized.

We chose to compare our method to Phobius because it is the only method that we know of that simultaneously predicts signal peptides and complete transmembrane topologies. Several methods, such as MemBrain [29] and P roteus [30], predict transmembrane helices and signal peptides, but without any topological (inside/outside) information. The web server PONGO [31] gives predictions from individual transmembrane topology and signal peptide predictors without combining the individual predictors.

Protein Type Classification

Initially, we evaluate how accurately Philius identifies a given protein's class as G, SP+G, TM or SP+TM. Table 1 shows the performance of Phobius and Philius at this task using accuracy, precision, sensitivity, specificity and Matthews correlation coefficient as metrics. Note that, because the SP+TM subset consists of only 45 examples, fewer than 2% of the 2654 proteins in the development set, we will sometimes group them together with the other TM proteins to provide more meaningful statistics. The largest difference between Philius and Phobius at this level is in the precision for the TM and SP+TM category, for which Philius calls 29% fewer false positives than Phobius. (Phobius finds 265 of the 292 true positives, and miscalls 82 of the 2362 true negatives on the same data, Philius finds 268 TPs and miscalls 58 TNs.) Overall, the performance on the G and SP+G subsets has decreased slightly in exchange for an improvement on the TM subset which is of greatest interest. Note that the class sizes in this dataset are skewed (48% SP+G, 41% G, and 11% TM and SP+TM), and that compared to a complete proteome, the transmembrane proteins are underrepresented in this dataset by a factor of 2 to 3.


ORIGINAL RESEARCH article

Although phytoplasma studies are still hampered by the lack of axenic cultivation methods, the availability of genome sequences allowed dramatic advances in the characterization of the virulence mechanisms deployed by phytoplasmas, and highlighted the detection of signal peptides as a crucial step to identify effectors secreted by phytoplasmas. However, various signal peptide prediction methods have been used to mine phytoplasma genomes, and no general evaluation of these methods is available so far for phytoplasma sequences. In this work, we compared the prediction performance of SignalP versions 3.0, 4.0, 4.1, 5.0 and Phobius on several sequence datasets originating from all deposited phytoplasma sequences. SignalP 4.1 with specific parameters showed the most exhaustive and consistent prediction ability. However, the configuration of SignalP 4.1 for increased sensitivity induced a much higher rate of false positives on transmembrane domains located at N-terminus. Moreover, sensitive signal peptide predictions could similarly be achieved by the transmembrane domain prediction ability of TMHMM and Phobius, due to the relatedness between signal peptides and transmembrane regions. Beyond the results presented herein, the datasets assembled in this study form a valuable benchmark to compare and evaluate signal peptide predictors in a field where experimental evidence of secretion is scarce. Additionally, this study illustrates the utility of comparative genomics to strengthen confidence in bioinformatic predictions.


Materials and Methods

Cell Culture, mRNA Processing, and Library Assembly

Cells were grown at 25 °C with a 16 h light and 8 h dark cycle in Tropic Marin PRO-REEF (Tropic Marin, Germany) supplemented with f/2 AlgaBoost (AusAqua, Australia). Cells of 800 ml culture (about 5 × 10 5 cells/ml) from three different time points (every 8 h starting 1 h before the light turned on) were harvested by centrifugation at 3,000 × g for 20 min. RNA of those three samples was isolated separately with TRIzol (Invitrogen, Germany) following the manufactures protocol with the following modification: the cell pellet was grinded in the presence of liquid nitrogen for 5–10 min before TRIzol was added. After RNA quantification, the samples were pooled so that an equal amount of each was present and send on dry ice for further processing to GATC-Biotech (Germany). At GATC, the RNA was amplified using their standard protocol for “True-Full-Length cDNA” and then additionally normalized before sequencing 2 million reads on a Titanium GS FLX (Roche). Trimming of adapter sequences, primary clustering, and assembly of the reads was performed by GATC-Biotech. Sequencing resulted in 2502269 reads with an average length of 239 bases, which were assembled into 29,856 contigs. Additionally, we included 2,854 C. velia expressed sequence tags (ESTs) from GenBank ( Benson et al. 2009). Multiple copy proteins were unified and EST-contigs shorter than 100 nt removed. Furthermore, such EST-contigs with BlastN hits to the plastidal genome of C. velia (e value cutoff 10 − 10 , downloaded from RefSeq, Pruitt et al. 2007) or the Rfam database ( Gardner et al. 2009) were deleted in order to remove remnants of chloroplast-encoded transcripts and noncoding RNA families. All sequences have been deposited under JO786643–JO814452.

Database Preparation

The protein database sequences were obtained from either EuPathDB ( Aurrecoechea et al. 2007) RefSeq or in the case of Cyanidioschyzon merolae ( Matsuzakiet al. 2004), Ectocarpus siliculosus ( Cock et al. 2010), and Emiliania huxleyi (http://genome.jgi-psf.org/Emihu1/Emihu1.download.ftp.html) from their corresponding genome project homepages. From the downloaded files, we removed C-terminal stop codons and replaced selenocysteins by Xs. In cases where no adequate number of protein sequences was available, EST-contigs were used instead or in addition. For this purpose, we created an EST-contig database by downloading ESTs for all lineages with >1,000 entries from GenBank, with exception of the Galdieria ESTs, which were downloaded from the Galdieria sulphuraria genome project homepage ( Weber et al. 2004). For further information and a list of organisms, see supplementary information ( Supplementary Material online). The EST-contigs were translated into proteins by the method described below and merged with the protein database.

Chromera EST-contigs were translated in a protein sequence similarly to the method described in Min et al. (2005). The EST sequences were blasted (BlastX Altschul et al. 1997), using e value threshold ≤ 1 × 10 − 5 to the protein database and SwissProt database ( Boeckmann et al. 2003). For sequences with blast hits, we translated the EST-contigs using the reading frame of the best blast hit (BBH). Sequences lacking a blast hit were predicted de novo by searching for the open reading frame (ORF) yielding the longest polypeptide (using both sense and antisense). In ORFs lacking an N-terminal methionine, the first codon in the EST-contig was translated into the first amino acid. When a C-terminal STOP codon was missing, the last codon in the EST-contig was translated into the last amino acid. Translated EST-contigs of C. velia were clustered into cognates of nearly identical EST-contigs by CDHIT ( Weizhong and Godzik 2006) with a 95% amino acid sequence identity as a threshold, using the slow mode (–g 1). For the remaining EST-contigs, a search for reciprocal BBH (rBBH Tatusov et al. 1997) with an e value cutoff of <1 × 10 − 10 was performed against the protein/EST data set of each species/genus. In case of multiple BBH having identical e values, all hits were retained. In this case, the rBBH approach was used to reduce redundant hits within the ESTs of the same gene. Pairwise alignments of Chromera EST-contigs and their rBBH were reconstructed with Needleman and Wunsch alignment algorithm ( Needleman and Wunsch 1970) using Needle (EMBOSS Rice et al. 2000). Pairs with a global amino acid identity ≥25% (excluding external gapped positions) were retained for further analysis. In case of multiple equally similar hits per one Chromera EST-contig or per one protein within the Chromera EST-contigs, the rBBH with the highest global similarity was used. Clusters of homologous proteins were constructed for Chromera EST-contigs and their homologs in all species data sets. An exclusion of 359 clusters comprising only EST-contigs yielded 3,151 clusters in total.

Phylogenetic Trees and Splits Networks

To reconstruct phylogenetic trees, all “nonchromalveolate” sequences except for one outgroup (the one showing the higher sequence similarity to the Chromera EST-contigs) were excluded from the clusters. Clusters having <4 remaining members were omitted. A total of 3,151 clusters of homologous proteins were aligned by MAFFT ( Katoh and Toh 2008) using the default parameters. Multiple alignment quality was assessed using Guidance ( Penn et al. 2010). Gapped alignment positions were removed and 86 short alignments (<10 positions) were excluded from further analysis. Phylogenetic trees were reconstructed from 2,258 multiple sequence alignments with PhyML ( Guindon and Gascuel 2003) using the best fit model as inferred by ProtTest 3 ( Darriba et al. 2011) using the Akaike information criterion ( Akaike 1974) measure. For the reconstruction of a splits network, all splits within the phylogenetic trees were extracted using a Perl script and converted into a binary pattern that included 37 digits. If the split contained taxon i then digit xi in the corresponding pattern was set to “1,” otherwise it was “0.” Taxa that were missing in a tree were indicated by a “?.” The resulting patterns were summarized in a splits network using SplitsTree ( Huson and Bryant 2006).

To find Chromera sequences of green or red origin, only1,174 clusters including proteins from Rhodophyta and Chloroplastida were used. All nonrhodophyta and nonchloroplastida sequences were removed from the clusters, except for those of Chromera. As an outgroup for each tree, the BBH to C. velia was used, which did not belong to Rhodophyta, Chloroplastida, a translated EST-contig or any organisms with a red algae as secondary endosymbiont. Phylogenetic trees were reconstructed from the resulting alignments (having ≥50 positions) using the same methodology described above, yielding 813 trees with an outgroup in total. The nearest neighbor to Chromera within each tree was determined by searching for the smallest clade that included C. velia and either only rhodophyta (red signal) or chloroplastida (green signal) and did not include the outgroup. For the determination of the position of C. velia in the trees as sister group or inside the red or green clades, we rooted the trees by the outgroups and searched for the second nearest neighbors using Newick Utilities package ( Junier and Zdobnov 2010). Extraction of the longest branches to assess long-branch attraction was performed by the same package. Additional two split networks were reconstructed from trees sorted into red or green nearest neighbor using a composite outgroup regardless of the outgroup identity in each single tree.

Absence/Presence of Homologs in Other Species

In addition to the rBBH approach, homologs to Chromera EST-contigs within each species were identified by Blasting the clustered Chromera EST-contigs against the species data set. BBHs with an e value ≤ 1 × 10 −10 were aligned with their Chromera homolog using Needle (EMBOSS Rice et al. 2000). Global pairwise alignments resulting in ≥25% amino acid identity after removal of external gapped positions were classified as a present homolog. The global amino acid identities presented in figure 2 were extracted from the pairwise alignments. The clusters that are shown along the y axis are sorted as follows: 1) all clusters specific for the apicomplexan phylum, 2) clusters of all members, 3) clusters that, except for C. velia, do have members just outside of apicomplexa. Within the three categories, the clusters were sorted by ascending number of present homologs within the Apicomplexa and descending number of present homologs within the non-Apicomplexa.

Sequence logo of the BTS of nuclear-encoded plastid proteins. The logo was curated based on 255 sequences, which encode an N-terminal signal peptide followed by a transit peptide. The −20/+20 positions relative to the cleavage site (red arrow) between the two parts of the BTS are shown. Secretory and plastid proteins both encode an almost identical signal peptide but only in the latter case a transit peptide follows. The N-terminal part of the transit peptide is enriched in serine residues and the C-terminal end with positively charged arginine residues.

Sequence logo of the BTS of nuclear-encoded plastid proteins. The logo was curated based on 255 sequences, which encode an N-terminal signal peptide followed by a transit peptide. The −20/+20 positions relative to the cleavage site (red arrow) between the two parts of the BTS are shown. Secretory and plastid proteins both encode an almost identical signal peptide but only in the latter case a transit peptide follows. The N-terminal part of the transit peptide is enriched in serine residues and the C-terminal end with positively charged arginine residues.

Presence/absence pattern and identity of the nuclear-encoded Chromera velia ESTs compared with 34 organisms. (A) The 3,151 sequences are sorted by their specificity and frequency to other Apicomplexa sequences. One hundred and fifty-one sequences have homologs only in Apicomplexa, whereas 1,316 sequences had homologs only in organisms other than Apicomplexa. Note that outside the Apicomplexa, C. velia shares the highest amount of overall identity with Perkinsus marinus. In (B), the potential amount of proteins encoded within the genomes used in the analysis.

Presence/absence pattern and identity of the nuclear-encoded Chromera velia ESTs compared with 34 organisms. (A) The 3,151 sequences are sorted by their specificity and frequency to other Apicomplexa sequences. One hundred and fifty-one sequences have homologs only in Apicomplexa, whereas 1,316 sequences had homologs only in organisms other than Apicomplexa. Note that outside the Apicomplexa, C. velia shares the highest amount of overall identity with Perkinsus marinus. In (B), the potential amount of proteins encoded within the genomes used in the analysis.

Prediction of Plastidal and Secretory Proteins

For the prediction of a signal peptide, only EST-contigs that were translated into a protein that started with a methionine were used. SignalP V3.0 ( Emanuelsson et al. 2007) was used to find sequences with potential plastidal signal peptides. Chromera sequences having homologs (see “Database Preparation“) that were annotated as plastid targeted were classified as plastidal proteins as well. All 657 detected sequences were then manually inspected, and an analysis including BlastP, SignalP, and TargetP ( Emanuelsson et al. 2007) was used to determine the cleavage sites and distinguish plastidal from other secretory proteins. A sequence logo of the targeting signal was created using Weblogo ( Crooks et al. 2004) from positions −20 to +20 in respect to the predicted cleavage site.

Annotation of Sequences

KEGG annotations were determined by using KAAS ( Moriya et al. 2007) using translated Chromera sequences as query against the KEGG maps of 27 eukaryotes including (for the complete species name, see http://www.genome.ad.jp/tools/kaas/): hsa, dme, cel, ath, osa, olu, cme, sce, ddi, ehi, pfa, pyo, pkn, tan, tpv, bbo, cpv, cho, tgo, tet, ptm, tbr, tcr, lma, tva, pti, and tps. Protein functional categories were summarized as follows: KOs were mapped to the corresponding annotations obtained from KEGG FTP Server (http://www.genome.jp/kegg/download/). The main categories “Cellular Processes” and “Environmental Information Processing” were merged into “Cellular Processing and Signaling.” Proteins in the “Unclassified, poorly characterized” category were classified as “Unclassified.” All other “Unclassified” categories were added to subcategory “Other” of the corresponding main classification. Genes potentially associated with photosynthetic were identified by searching for the KEGG categories “Photosynthesis” and “Photosynthetic.”


Prediction of signal peptides and signal anchors by a hidden Markov model.

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ISMB-98 Proceedings. Vol. 6 AAAI Press, 1998. p. 122-130 (International Conference on Intelligent Systems for Molecular Biology. Proceedings).

Research output : Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

T1 - Prediction of signal peptides and signal anchors by a hidden Markov model.

AU - Krogh, Anders Stærmose

N2 - A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our data, the length distributions for the three regions are significantly different from expectations. For instance, the assigned hydrophobic region is between 8 and 12 residues long in almost all eukaryotic signal peptides. This analysis also makes obvious the difference between eukaryotes, Gram-positive bacteria, and Gram-negative bacteria. The model can be used to predict the location of the cleavage site, which it finds correctly in nearly 70% of signal peptides in a cross-validated test--almost the same accuracy as the best previous method. One of the problems for existing prediction methods is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model.

AB - A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our data, the length distributions for the three regions are significantly different from expectations. For instance, the assigned hydrophobic region is between 8 and 12 residues long in almost all eukaryotic signal peptides. This analysis also makes obvious the difference between eukaryotes, Gram-positive bacteria, and Gram-negative bacteria. The model can be used to predict the location of the cleavage site, which it finds correctly in nearly 70% of signal peptides in a cross-validated test--almost the same accuracy as the best previous method. One of the problems for existing prediction methods is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model.

KW - artificial intelligence

KW - artificial neural network

KW - Artificial Intelligence

KW - Neural Networks (Computer)

KW - Protein Sorting Signals

M3 - Article in proceedings

T3 - International Conference on Intelligent Systems for Molecular Biology. Proceedings


Watch the video: signal peptides (May 2022).


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