Identification Of Signal Peptides Research Articles

Identifying genes involved in the secretory physiological response to feeding in Pacific White Shrimp (Litopenaeus vannamei) using transcriptomics

The physiological response to feeding is important for production aspects that include feed utilization and growth, and the responses require the action of numerous secretory factors. However, as an important aquaculture animal, the secretory response of Pacific White Shrimp (Litopenaeus vannamei) after feeding has not been comprehensively characterized. In this study, transcriptome analysis showed that 3172 differentially expressed genes were involved in the post-feeding response, including 289 new genes not annotated in the L. vannamei reference genome. Subsequently, 715 differentially expressed secretory reference genes and 18 new differentially expressed secretory genes were obtained through the identification of signal peptides in secreted proteins. Functional classification revealed that differentially expressed secretory genes were enriched in pathways pertaining to lipid metabolism (20 genes), carbohydrate metabolism (21 genes), glycan biosynthesis and metabolism (27 genes), digestive system (40 genes), and transport and metabolism (43 genes). The 14 pathways most enriched by differentially expressed secretory genes involved 83 genes, 71 of which encoded enzymes involved in food digestion and metabolism. Specific enzymes such as lipase 3-like and NPC intracellular cholesterol transporter 1-like in lipid metabolism, alpha-amylase-like and glucosylceramidase-like in carbohydrate metabolism, and cysteine proteinase 4-like and trypsin-1-like in the digestive system were found to be differentially expressed. Furthermore, we discovered a new gene, MSTRG.2504, that participates in the digestive system and carbohydrate metabolism. The study provides valuable insights into the secretory response (especially metabolism-related enzymes) to feeding in L. vannamei, uncovering the significant roles of both known and new genes. Furthermore, this study will improve our understanding of the feeding physiology of L. vannamei and provide a reference basis for further feeding endocrine research in the future.

A Global Analysis of Enzyme Compartmentalization to Glycosomes.

In kinetoplastids, the first seven steps of glycolysis are compartmentalized into a glycosome along with parts of other metabolic pathways. This organelle shares a common ancestor with the better-understood eukaryotic peroxisome. Much of our understanding of the emergence, evolution, and maintenance of glycosomes is limited to explorations of the dixenous parasites, including the enzymatic contents of the organelle. Our objective was to determine the extent that we could leverage existing studies in model kinetoplastids to determine the composition of glycosomes in species lacking evidence of experimental localization. These include diverse monoxenous species and dixenous species with very different hosts. For many of these, genome or transcriptome sequences are available. Our approach initiated with a meta-analysis of existing studies to generate a subset of enzymes with highest evidence of glycosome localization. From this dataset we extracted the best possible glycosome signal peptide identification scheme for in silico identification of glycosomal proteins from any kinetoplastid species. Validation suggested that a high glycosome localization score from our algorithm would be indicative of a glycosomal protein. We found that while metabolic pathways were consistently represented across kinetoplastids, individual proteins within those pathways may not universally exhibit evidence of glycosome localization.

Membrane protein serendipity

My scientific career has taken me from chemistry, via theoretical physics and bioinformatics, to molecular biology and even structural biology. Along the way, serendipity led me to work on problems such as the identification of signal peptides that direct protein trafficking, membrane protein biogenesis, and cotranslational protein folding. I've had some great collaborations that came about because of a stray conversation or from following up on an interesting paper. And I've had the good fortune to be asked to sit on the Nobel Committee for Chemistry, where I am constantly reminded of the amazing pace and often intricate history of scientific discovery. Could I have planned this? No way! I just went with the flow ….

Signal Peptide Sequence Analysis of Selected Protein Sequences from Cryptosporidium parvum

Background and Objective: Studies on signal peptides remain little and lacking to Cryptosporidium. This study represents the first attempt to investigate selected putative protein sequences of Cryptosporidium parvum in its established genome database for signal peptide. Methods: Prediction analysis of protein sequences and identification of signal peptides on this parasite were analysed by SignalP 4.1 server. A total of 100 protein sequences were randomly selected and analysed, of which 6 (6%) corresponded to be predicted as signal peptide sequences. Results: Based on the comparison of sequences with database protein sequences in GenBank NCBI, almost all protein sequences were highly conserved with other protein sequences from different species of Cryptosporidium. Considering the secretory proteins in Cryptosporidium, several virulence protein sequences of this parasite may also show presence of N-terminal signal peptide or not. Conclusions: Hence, further studies need to be carried out for identification of N-terminal signal peptide function in Cryptosporidium for facilitating its virulence and pathogenesis pathway.

DeepSig: deep learning improves signal peptide detection in proteins.

MotivationThe identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.ResultsHere, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification.Availability and implementationDeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website.Supplementary information Supplementary data are available at Bioinformatics online.

Proteome-based identification of signal peptides for improved secretion of recombinant cyclomaltodextrin glucanotransferase in Escherichia coli

Secretion of recombinant proteins in heterologous host has drawn attention for its simpler purification and downstream processes. Searching for secretion aid molecules to improve protein secretion can be done through synthetic biology, screening of genome data and proteome-based approach. In the present study, the extracellular proteome on starch-containing medium of Bacillus lehensis G1 was analyzed to identify naturally secreted proteins with signal peptide. A total of 87 protein spots were identified by mass spectrometry, which were categorized mostly in the metabolism of carbohydrates and related molecules (20%). Over-expression and secretion studies were performed for all the 14 selected signal peptides fused to a reporter protein, cyclomaltodextrin glucanotransferase (CGTase). All clones were found to allow CGTase to be excreted into the medium, as observed and measured from the iodine plate assay and enzyme activity assay. Compared to native signal peptide (G1) of CGTase, signal peptide of GlcNAc-binding protein A (GAP) significantly improved CGTase activities by 735% and 205% in extracellular and periplasmic compartment, respectively, with an increase of only ∼1.7 fold the amount of β-galactosidase (cell lysis) in the medium. GAP has the highest secretion rate of 45.6 U/ml/h among all clones, where physicochemical characteristics of signal peptide play significant role.

QPMS9: an efficient algorithm for quorum Planted Motif Search.

Discovering patterns in biological sequences is a crucial problem. For example, the identification of patterns in DNA sequences has resulted in the determination of open reading frames, identification of gene promoter elements, intron/exon splicing sites, and SH RNAs, location of RNA degradation signals, identification of alternative splicing sites, etc. In protein sequences, patterns have led to domain identification, location of protease cleavage sites, identification of signal peptides, protein interactions, determination of protein degradation elements, identification of protein trafficking elements, discovery of short functional motifs, etc. In this paper we focus on the identification of an important class of patterns, namely, motifs. We study the (ℓ, d) motif search problem or Planted Motif Search (PMS). PMS receives as input n strings and two integers ℓ and d. It returns all sequences M of length ℓ that occur in each input string, where each occurrence differs from M in at most d positions. Another formulation is quorum PMS (qPMS), where the motif appears in at least q% of the strings. We introduce qPMS9, a parallel exact qPMS algorithm that offers significant runtime improvements on DNA and protein datasets. qPMS9 solves the challenging DNA (ℓ, d)-instances (28, 12) and (30, 13). The source code is available at https://code.google.com/p/qpms9/.

The secreted proteins of Achlya hypogyna and Thraustotheca clavata identify the ancestral oomycete secretome and reveal gene acquisitions by horizontal gene transfer.

Saprotrophic and parasitic microorganisms secrete proteins into the environment to breakdown macromolecules and obtain nutrients. The molecules secreted are collectively termed the “secretome” and the composition and function of this set of proteins varies depending on the ecology, life cycle, and environment of an organism. Beyond the function of nutrient acquisition, parasitic lineages must also secrete molecules to manipulate their host. Here, we use a combination of de novo genome and transcriptome sequencing and bioinformatic identification of signal peptides to identify the putative secreted proteome of two oomycetes, the facultative parasite Achlya hypogyna and free-living Thraustotheca clavata. By comparing the secretomes of these saprolegnialean oomycetes with that of eight other oomycetes, we were able to characterize the evolution of this protein set across the oomycete clade. These species span the last common ancestor of the two major oomycete families allowing us to identify the ancestral secretome. This putative ancestral secretome consists of at least 84 gene families. Only 11 of these gene families are conserved across all 10 secretomes analyzed and the two major branches in the oomycete radiation. Notably, we have identified expressed elicitin-like effector genes in the saprotrophic decomposer, T. clavata. Phylogenetic analyses show six novel horizontal gene transfers to the oomycete secretome from bacterial and fungal donor lineages, four of which are specific to the Saprolegnialeans. Comparisons between free-living and pathogenic taxa highlight the functional changes of oomycete secretomes associated with shifts in lifestyle.

Functional annotation of putative hypothetical proteins from Candida dubliniensis

An extensive analysis of C. dubliniensis proteomics data showed that ~22% protein are conserved hypothetical proteins (HPs) whose function is still not determined precisely. Analysis of gene sequence of HPs provides a platform to establish sequence-function relationships to a more profound understanding of the molecular machinery of organisms at systems level. Here we have combined the latest versions of bioinformatics tools including, protein family, motifs, intrinsic features from the amino acid sequence, sequence-function relationship, pathway analysis, etc. to assign a precise function to HPs for which no any experimental information is available. Our results show that 27 HPs have well defined functions and we categorized them as enzyme, nucleic acid binding, transport protein, etc. Five HPs showed adhesin character that is likely to be essential for the survival of yeast and pathogenesis. We also addressed issues related to the sub-cellular localization and signal peptide identification which provides an idea about its colocalization and function. The outcome of the present study may facilitate better understanding of mechanism of virulence, drug resistance, pathogenesis, adaptability to host, tolerance for host immune response, and drug discovery for treatment of C. dubliniensis infections.

Using Multitask Learning Methods to Investigate Signal Peptides and Signal Anchors.

The identification of signal peptides and signal anchors is critical to understand the related biological mechanisms, and to find more effective vehicles for proteins production. Instead of studying signal peptides and signal anchors of single species separately, we developed a multi-tasking learning framework to investigate them across species simultaneously. By a multi-tasking feature selection method, we identified 12 important features out of 560 amino acid indices. The effectiveness and classification abilities of these 12 features were evaluated by another multi-tasking method, based on cross-validation and independent test. Further analysis of selected features brought some new insights into the physiochemical properties of signal peptides and signal anchors.

A speedup technique for (l, d)-motif finding algorithms.

BackgroundThe discovery of patterns in DNA, RNA, and protein sequences has led to the solution of many vital biological problems. For instance, the identification of patterns in nucleic acid sequences has resulted in the determination of open reading frames, identification of promoter elements of genes, identification of intron/exon splicing sites, identification of SH RNAs, location of RNA degradation signals, identification of alternative splicing sites, etc. In protein sequences, patterns have proven to be extremely helpful in domain identification, location of protease cleavage sites, identification of signal peptides, protein interactions, determination of protein degradation elements, identification of protein trafficking elements, etc. Motifs are important patterns that are helpful in finding transcriptional regulatory elements, transcription factor binding sites, functional genomics, drug design, etc. As a result, numerous papers have been written to solve the motif search problem.ResultsThree versions of the motif search problem have been proposed in the literature: Simple Motif Search (SMS), (l, d)-motif search (or Planted Motif Search (PMS)), and Edit-distance-based Motif Search (EMS). In this paper we focus on PMS. Two kinds of algorithms can be found in the literature for solving the PMS problem: exact and approximate. An exact algorithm identifies the motifs always and an approximate algorithm may fail to identify some or all of the motifs. The exact version of PMS problem has been shown to be NP-hard. Exact algorithms proposed in the literature for PMS take time that is exponential in some of the underlying parameters. In this paper we propose a generic technique that can be used to speedup PMS algorithms.ConclusionsWe present a speedup technique that can be used on any PMS algorithm. We have tested our speedup technique on a number of algorithms. These experimental results show that our speedup technique is indeed very effective. The implementation of algorithms is freely available on the web at http://www.engr.uconn.edu/rajasek/PMS4.zip

Response to Letter Regarding Article, “B-Type Natriuretic Peptide Signal Peptide Circulates in Human Blood: Evaluation as a Potential Biomarker of Cardiac Ischemia”

We thank Drs Goetze and Kavsak and their respective colleagues for their interest in our study.1 Our identification of signal peptide of B-type natriuretic peptide (BNPsp)(17-26) was achieved through specific immunoaffinity purification followed by multiple tandem mass spectrometry with the use of the same antibody that is present in the assay. Cross-reactivity data demonstrated negligible interference from many other peptides, including other human peptide signal sequences and standard medications. Rat and sheep plasma samples display no observable immunoreactivity. Analysis of the BNPsp(17-26) amino acid sequence on the National Center for Biotechnology Information protein Basic Local Alignment Search Tool reveals that only the signal peptide of human …

A proteogenomic update to Yersinia: enhancing genome annotation

BackgroundModern biomedical research depends on a complete and accurate proteome. With the widespread adoption of new sequencing technologies, genome sequences are generated at a near exponential rate, diminishing the time and effort that can be invested in genome annotation. The resulting gene set contains numerous errors in even the most basic form of annotation: the primary structure of the proteins.ResultsThe application of experimental proteomics data to genome annotation, called proteogenomics, can quickly and efficiently discover misannotations, yielding a more accurate and complete genome annotation. We present a comprehensive proteogenomic analysis of the plague bacterium, Yersinia pestis KIM. We discover non-annotated genes, correct protein boundaries, remove spuriously annotated ORFs, and make major advances towards accurate identification of signal peptides. Finally, we apply our data to 21 other Yersinia genomes, correcting and enhancing their annotations.ConclusionsIn total, 141 gene models were altered and have been updated in RefSeq and Genbank, which can be accessed seamlessly through any NCBI tool (e.g. blast) or downloaded directly. Along with the improved gene models we discover new, more accurate means of identifying signal peptides in proteomics data.

PROTEUS2: a web server for comprehensive protein structure prediction and structure-based annotation

PROTEUS2 is a web server designed to support comprehensive protein structure prediction and structure-based annotation. PROTEUS2 accepts either single sequences (for directed studies) or multiple sequences (for whole proteome annotation) and predicts the secondary and, if possible, tertiary structure of the query protein(s). Unlike most other tools or servers, PROTEUS2 bundles signal peptide identification, transmembrane helix prediction, transmembrane β-strand prediction, secondary structure prediction (for soluble proteins) and homology modeling (i.e. 3D structure generation) into a single prediction pipeline. Using a combination of progressive multi-sequence alignment, structure-based mapping, hidden Markov models, multi-component neural nets and up-to-date databases of known secondary structure assignments, PROTEUS is able to achieve among the highest reported levels of predictive accuracy for signal peptides (Q2 = 94%), membrane spanning helices (Q2 = 87%) and secondary structure (Q3 score of 81.3%). PROTEUS2's homology modeling services also provide high quality 3D models that compare favorably with those generated by SWISS-MODEL and 3D JigSaw (within 0.2 Å RMSD). The average PROTEUS2 prediction takes ∼3 min per query sequence. The PROTEUS2 server along with source code for many of its modules is accessible a http://wishart.biology.ualberta.ca/proteus2.

A Facile Reporter System for the Experimental Identification of Twin-Arginine Translocation (Tat) Signal Peptides from All Kingdoms of Life

We have developed a reporter protein system for the experimental verification of twin-arginine signal peptides. This reporter system is based on the Streptomyces coelicolor agarase protein, which is secreted into the growth medium by the twin-arginine translocation (Tat) pathway and whose extracellular activity can be assayed colorimetrically in a semiquantitative manner. Replacement of the native agarase signal peptide with previously characterized twin-arginine signal peptides from other Gram-positive and Gram-negative bacteria resulted in efficient Tat-dependent export of agarase. Candidate twin-arginine signal peptides from archaeal proteins as well as plant thylakoid-targeting sequences were also demonstrated to mediate agarase translocation. A naturally occurring variant signal peptide with an arginine–glutamine motif instead of the consensus di-arginine was additionally recognized as a Tat-targeting sequence by Streptomyces. Application of the agarase assay to previously uncharacterized candidate Tat signal peptides from Bacillus subtilis identified two further probable Tat substrates in this organism. This is the first versatile reporter system for Tat signal peptide identification.

TISs-ST: a web server to evaluate polymorphic translation initiation sites and their reflections on the secretory targets.

BackgroundThe nucleotide sequence flanking the translation initiation codon (start codon context) affects the translational efficiency of eukaryotic mRNAs, and may indicate the presence of an alternative translation initiation site (TIS) to produce proteins with different properties. Multi-targeting may reflect the translational variability of these other protein forms. In this paper we present a web server that performs computations to investigate the usage of alternative translation initiation sites for the synthesis of new protein variants that might have different functions.ResultsAn efficient web-based tool entitled TISs-ST (Translation Initiation Sites and Secretory Targets) evaluates putative translation initiation sites and indicates the prediction of a signal peptide of the protein encoded from this site. The TISs-ST web server is freely available to both academic and commercial users and can be accessed at .ConclusionThe program can be used to evaluate alternative translation initiation site consensus with user-specified sequences, based on their composition or on many position weight matrix models. TISs-ST provides analytical and visualization tools for evaluating the periodic frequency, the consensus pattern and the total information content of a sequence data set. A search option allows for the identification of signal peptides from predicted proteins using the PrediSi software.

Prediction of Signal Peptides Using Bio-Basis Function Neural Networks and Decision Trees

Signal peptide identification is of immense importance in drug design. Accurate identification of signal peptides is the first critical step to be able to change the direction of the targeting proteins and use the designed drug to target a specific organelle to correct a defect. Because experimental identification is the most accurate method, but is expensive and time-consuming, an efficient and affordable automated system is of great interest. In this article, we propose using an adapted neural network, called a bio-basis function neural network, and decision trees for predicting signal peptides. The bio-basis function neural network model and decision trees achieved 97.16% and 97.63% accuracy respectively, demonstrating that the methods work well for the prediction of signal peptides. Moreover, decision trees revealed that position P(1'), which is important in forming signal peptides, most commonly comprises either leucine or alanine. This concurs with the (P(3)-P(1)-P(1')) coupling model.

Genome-wide identification of Pseudomonas aeruginosa exported proteins using a consensus computational strategy combined with a laboratory-based PhoA fusion screen

The Gram-negative pathogen Pseudomonas aeruginosa encodes multiple protein export systems, the substrates of which contain export signals such as N-terminal signal peptides. Here we report the first genome-wide computational and laboratory screen for N-terminal signal peptides in this important opportunistic pathogen. The computational identification of signal peptides was based on a consensus between multiple predictive tools and showed that 38% of the P. aeruginosa PAO1 proteome was predicted to encode exported proteins, most of which utilize cleavable type I signal peptides or uncleavable transmembrane helices. In addition, known and novel lipoproteins (type II), twin arginine transporter (TAT), and prepilin peptidase substrates (type IV) were also identified. A laboratory-based screen using the alkaline phosphatase (PhoA) fusion method was then used to test our predictions. In total, 310 nonredundant PhoA fusions were successfully identified, 296 of which possess a predicted export signal. Analysis of the PhoA fusion proteins lacking an export signal revealed that three proteins have alternate translation start sites that encode signal peptides, two proteins may use an unknown export signal, and the remaining nine proteins are likely cytoplasmic proteins and represent false positives associated with the PhoA screen. Our approach to identify exported proteins illustrates how computational and laboratory-based methods are complementary, where computational analyses provide a large number of accurate predictions while laboratory methods both confirm predictions and reveal unique cases meriting further analysis.

A Genetic Programming Method for the Identification of Signal Peptides and Prediction of Their Cleavage Sites

A novel approach to signal peptide identification is presented. We use an evolutionary algorithm for automatic evolution of classification programs, so-called programmatic motifs. The variant of evolutionary algorithm used is called genetic programming where a population of solution candidates in the form of full computer programs is evolved, based on training examples consisting of signal peptide sequences. The method is compared with a previous work using artificial neural network (ANN) approaches. Some advantages compared to ANNs are noted. The programmatic motif can perform computational tasks beyond that of feed-forward neural networks and has also other advantages such as readability. The best motif evolved was analyzed and shown to detect the h-region of the signal peptide. A powerful parallel computer cluster was used for the experiment.

A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.

We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied to genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server: http://www.cbs.dtu.dk/services/SignalP/.