scholarly journals Efficacy of signal peptide predictors in identifying signal peptides in the experimental secretome of Picrophilous torridus, a thermoacidophilic archaeon

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255826
Author(s):  
Neelja Singhal ◽  
Anjali Garg ◽  
Nirpendra Singh ◽  
Pallavi Gulati ◽  
Manish Kumar ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Signal Peptide ◽  
Carbon Metabolism ◽  
Metabolic Pathways ◽  
Protein Translocation ◽  
Mass Spectrometric ◽  
Secreted Proteins ◽  
Secretory Proteins ◽  
Signal Peptides ◽  
Microbial Adaptation

Secretory proteins are important for microbial adaptation and survival in a particular environment. Till date, experimental secretomes have been reported for a few archaea. In this study, we have identified the experimental secretome of Picrophilous torridus and evaluated the efficacy of various signal peptide predictors (SPPs) in identifying signal peptides (SPs) in its experimental secretome. Liquid chromatography mass spectrometric (LC MS) analysis was performed for three independent P. torridus secretome samples and only those proteins which were common in the three experiments were selected for further analysis. Thus, 30 proteins were finally included in this study. Of these, 10 proteins were identified as hypothetical/uncharacterized proteins. Gene Ontology, KEGG and STRING analyses revealed that majority of the sercreted proteins and/or their interacting partners were involved in different metabolic pathways. Also, a few proteins like malate dehydrogenase (Q6L0C3) were multi-functional involved in different metabolic pathways like carbon metabolism, microbial metabolism in diverse environments, biosynthesis of antibiotics, etc. Multi-functionality of the secreted proteins reflects an important aspect of thermoacidophilic adaptation of P. torridus which has the smallest genome (1.5 Mbp) among nonparasitic aerobic microbes. SPPs like, PRED-SIGNAL, SignalP 5.0, PRED-TAT and LipoP 1.0 identified SPs in only a few secreted proteins. This suggests that either these SPPs were insufficient, or N-terminal SPs were absent in majority of the secreted proteins, or there might be alternative mechanisms of protein translocation in P. torridus.

scholarly journals Evaluation of signal peptide prediction algorithms for identification of mycobacterial signal peptides using sequence data from proteomic methods

Microbiology ◽  
2009 ◽  
Vol 155 (7) ◽  
pp. 2375-2383 ◽  
Author(s):  
Nils Anders Leversen ◽  
Gustavo A. de Souza ◽  
Hiwa Målen ◽  
Swati Prasad ◽  
Inge Jonassen ◽  
...  
Keyword(s):  
Markov Model ◽  
Signal Peptide ◽  
Cleavage Site ◽  
Hidden Markov ◽  
Signal Peptidase ◽  
Secreted Proteins ◽  
Signal Peptides ◽  
Sanger Institute ◽  
Signal Peptidase I ◽  
Peptide Prediction

Secreted proteins play an important part in the pathogenicity of Mycobacterium tuberculosis, and are the primary source of vaccine and diagnostic candidates. A majority of these proteins are exported via the signal peptidase I-dependent pathway, and have a signal peptide that is cleaved off during the secretion process. Sequence similarities within signal peptides have spurred the development of several algorithms for predicting their presence as well as the respective cleavage sites. For proteins exported via this pathway, algorithms exist for eukaryotes, and for Gram-negative and Gram-positive bacteria. However, the unique structure of the mycobacterial membrane raises the question of whether the existing algorithms are suitable for predicting signal peptides within mycobacterial proteins. In this work, we have evaluated the performance of nine signal peptide prediction algorithms on a positive validation set, consisting of 57 proteins with a verified signal peptide and cleavage site, and a negative set, consisting of 61 proteins that have an N-terminal sequence that confirms the annotated translational start site. We found the hidden Markov model of SignalP v3.0 to be the best-performing algorithm for predicting the presence of a signal peptide in mycobacterial proteins. It predicted no false positives or false negatives, and predicted a correct cleavage site for 45 of the 57 proteins in the positive set. Based on these results, we used the hidden Markov model of SignalP v3.0 to analyse the 10 available annotated proteomes of mycobacterial species, including annotations of M. tuberculosis H37Rv from the Wellcome Trust Sanger Institute and the J. Craig Venter Institute (JCVI). When excluding proteins with transmembrane regions among the proteins predicted to harbour a signal peptide, we found between 7.8 and 10.5 % of the proteins in the proteomes to be putative secreted proteins. Interestingly, we observed a consistent difference in the percentage of predicted proteins between the Sanger Institute and JCVI. We have determined the most valuable algorithm for predicting signal peptidase I-processed proteins of M. tuberculosis, and used this algorithm to estimate the number of mycobacterial proteins with the potential to be exported via this pathway.

scholarly journals A signal sequence suppressor mutant that stabilizes an assembled state of the twin arginine translocase

2017 ◽  
Vol 114 (10) ◽  
pp. E1958-E1967 ◽  
Author(s):  
Qi Huang ◽  
Felicity Alcock ◽  
Holger Kneuper ◽  
Justin C. Deme ◽  
Sarah E. Rollauer ◽  
...  
Keyword(s):  
Signal Peptide ◽  
Protein Translocation ◽  
Signal Sequence ◽  
Transmembrane Helix ◽  
Peptide Binding ◽  
Signal Peptides ◽  
Arginine Residues ◽  
Tat System ◽  
Twin Arginine Translocase

The twin-arginine protein translocation (Tat) system mediates transport of folded proteins across the cytoplasmic membrane of bacteria and the thylakoid membrane of chloroplasts. The Tat system ofEscherichia coliis made up of TatA, TatB, and TatC components. TatBC comprise the substrate receptor complex, and active Tat translocases are formed by the substrate-induced association of TatA oligomers with this receptor. Proteins are targeted to TatBC by signal peptides containing an essential pair of arginine residues. We isolated substitutions, locating to the transmembrane helix of TatB that restored transport activity to Tat signal peptides with inactivating twin arginine substitutions. A subset of these variants also suppressed inactivating substitutions in the signal peptide binding site on TatC. The suppressors did not function by restoring detectable signal peptide binding to the TatBC complex. Instead, site-specific cross-linking experiments indicate that the suppressor substitutions induce conformational change in the complex and movement of the TatB subunit. The TatB F13Y substitution was associated with the strongest suppressing activity, even allowing transport of a Tat substrate lacking a signal peptide. In vivo analysis using a TatA–YFP fusion showed that the TatB F13Y substitution resulted in signal peptide-independent assembly of the Tat translocase. We conclude that Tat signal peptides play roles in substrate targeting and in triggering assembly of the active translocase.

scholarly journals Annotating eukaryotic and toxin-specific signal peptides using Razor

2020 ◽  
Author(s):  
Bikash K. Bhandari ◽  
Paul P. Gardner ◽  
Chun Shen Lim
Keyword(s):  
Signal Peptide ◽  
Web Application ◽  
Protein Transport ◽  
Protein Translocation ◽  
Recombinant Protein Expression ◽  
Disease Diagnosis ◽  
Signal Peptides ◽  
Specific Signal ◽  
Link Type ◽  
Command Line Tool

ABSTRACTMotivationSignal peptides are responsible for protein transport and secretion and are ubiquitous to all forms of life. The annotation of signal peptides is important for understanding protein translocation and toxin secretion, optimising recombinant protein expression, as well as for disease diagnosis and metagenomics.ResultsHere we explore the features of these signal sequences across eukaryotes. We find that different kingdoms have their characteristic distributions of signal peptide residues. Additionally, the signal peptides of secretory toxins have common features across kingdoms. We leverage these subtleties to build Razor, a simple yet powerful tool for annotating signal peptides, which additionally predicts toxin- and fungal-specific signal peptides based on the first 23 N-terminal residues. Finally, we demonstrate the usability of Razor by scanning all reviewed sequences from UniProt. Indeed, Razor is able to identify toxins using their signal peptide sequences only. Strikingly, we discover that many defensive proteins across kingdoms harbour a toxin-like signal peptide; some of these defensive proteins have emerged through convergent evolution, e.g. defensin and defensin-like protein families, and phospholipase families.Availability and implementationRazor is available as a web application (https://tisigner.com/razor) and a command-line tool (https://github.com/Gardner-BinfLab/Razor).

In Silico Evaluation of Various Signal Peptides to Improve Secretion of Humulin Protein in E. coli Host

2020 ◽  
Vol 17 ◽  
Author(s):  
Soudabe Kavousipour ◽  
Mahadi Barazesh ◽  
Shiva Mohammadi ◽  
Meghdad Abdollahpour- Alitappeh ◽  
Shirzad Fallahi ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Signal Peptide ◽  
In Silico ◽  
Signal Sequence ◽  
Expression Vectors ◽  
Secretory Proteins ◽  
Signal Peptides ◽  
Heterologous Proteins ◽  
Genetic Characteristics ◽  
E Coli

Background:: Escherichia coli host has been the workhorse for the production of heterologous proteins due to simplicity of use, low cost, availability of various expression vectors, and widespread knowledge on its genetic characteristics, but without a suitable signal sequence, this host cannot be used for production secretory proteins. Humulin is a form of insulin used to treat hyperglycemia caused by types 1 and 2 diabetes. To improve expression and make a straightforward production of Humulin protein, we chose a series of signal peptides. Objective:: aim our study to predict the most excellent signal peptides to express secretory Humulin in E. coli organisms. Method:: Therefore, to forecast the most excellent signal peptides for expression of Humulin in Escherichia coli, 47 signal sequences from bacteria organisms were elected and the most imperative elements of them were studied. Hence, signal peptide probability along with physicochemical features was evaluated by signal 4.1, and Portparam, PROSO II servers respectively. Later, the in-silico cloning in a known pET28a plasmid system also estimated the possibility of best signal peptide+ Humulin expression in E.Coli. Results:: The outcomes demonstrated among 47 signal peptides only 2 signal peptides can be suggested as suitable signal peptides. Conclusion:: Ultimately protein yebF precursor (YEBF_ECOLI) and protein yebF precursor (YEBF_YERP3) were suggested severally; as the most excellent signal peptides to express Humulin (With D scores 0.812 and 0.623 respectively). Although verification of these results want experimental analysis.

scholarly journals Interactions of signal peptides with signal-recognition particle

1990 ◽  
Vol 266 (1) ◽  
pp. 149-156 ◽  
Author(s):  
A Robinson ◽  
O M R Westwood ◽  
B M Austen
Keyword(s):  
Signal Peptide ◽  
Wheat Germ ◽  
Signal Recognition Particle ◽  
Translation System ◽  
Secretory Proteins ◽  
Signal Peptides ◽  
Signal Recognition ◽  
Synthetic Signal ◽  
Cross Links

The mechanisms whereby isolated or synthetic signal peptides inhibit processing of newly synthesized prolactin in microsome-supplemented lysates from reticulocytes and wheat-germ were investigated. At a concentration of 5 microM, a consensus signal peptide reverses the elongation arrest imposed by the signal-recognition particle (SRP), and at higher concentrations in addition inhibits elongation of both secretory and non-secretory proteins. A photoreactive form of a synthetic signal peptide cross-links under u.v. illumination to the 54 kDa and 68 kDa subunits of SRP, whereas the major cross-linked protein produced after photoreaction of rough microsomes is of 45 kDa. As SRP-mediated elongation arrest is unlikely to be essential for translocation, it is suggested that signal peptides may interact with components other than SRP in the translation system in vitro.

scholarly journals In silico analysis of signal peptides for secretory production of a-amylase in Bacillus subtilis

2019 ◽  
pp. 113-124
Author(s):  
Marzieh Asadi ◽  
Morgaza Taheri-Anganeh ◽  
Zeinab Jamali ◽  
Seyyed Hossein Khatami ◽  
Cambyz Irajie ◽  
...  
Keyword(s):  
Bacillus Subtilis ◽  
Signal Peptide ◽  
In Silico ◽  
In Silico Analysis ◽  
Essential Elements ◽  
Secretory Proteins ◽  
Signal Peptides ◽  
Experimental Production ◽  
Secretory Production ◽  
Peptide Secretion

α-Amylases are important commercial enzymes and have a broad application in industrial processes and medicine. Gram-positive bacteria such as Bacillus subtilis are possible host organisms for α-amylases secretory production. Secretion of α-amylases to the culture medium versus intracellular production has several advantages such as prevention of inclusion bodies accumulation, higher product stability and solubility. Signal peptides are considered as one of the most essential elements for successful secretory synthesis of the recombinant proteins. Therefore, by the selection of an efficient signal peptide, secretion of the recombinant protein can be enhanced. The goal of this investigation was the in silico evaluation of several peptides to find the most suitable leader peptides for secretory production of α-amylase in B. subtilis. In present work, 30 signal peptides were selected, and numerous online servers such as SignalP, ProtParam, SOLpro, PRED-TAT and ProtComp was used for investigation of suitable signal peptides. According to in silico predictions all other signal peptides connected to α-amylase were stable and soluble except PPBD_BACSU. PPBD_BACSU because of having D-score below cut-off could not be recognized as a suitable signal peptide for α-amylase. Computational analysis identified QOX2_BACSU may direct protein into transmembrane location and was ignored. All 28 remained were predicted as secretory signal peptides which can excrete protein out of the bacteria. The signal peptides recommended by the present study are valuable for rational designing of secretory soluble α-amylase. Although, such information can be useful for future experimental production of these mentioned secretory proteins.

scholarly journals Specificity of Signal Peptide Recognition in Tat-Dependent Bacterial Protein Translocation

2001 ◽  
Vol 183 (2) ◽  
pp. 604-610 ◽  
Author(s):  
Natascha Blaudeck ◽  
Georg A. Sprenger ◽  
Roland Freudl ◽  
Thomas Wiegert
Keyword(s):  
Fusion Protein ◽  
Signal Peptide ◽  
Protein Translocation ◽  
Signal Peptides ◽  
E Coli ◽  
Amino Terminal ◽  
Twin Arginine Translocation ◽  
Tat Pathway ◽  
Dependent Protein ◽  
Folded Proteins

ABSTRACT The bacterial twin arginine translocation (Tat) pathway translocates across the cytoplasmic membrane folded proteins which, in most cases, contain a tightly bound cofactor. Specific amino-terminal signal peptides that exhibit a conserved amino acid consensus motif, S/T-R-R-X-F-L-K, direct these proteins to the Tat translocon. The glucose-fructose oxidoreductase (GFOR) ofZymomonas mobilis is a periplasmic enzyme with tightly bound NADP as a cofactor. It is synthesized as a cytoplasmic precursor with an amino-terminal signal peptide that shows all of the characteristics of a typical twin arginine signal peptide. However, GFOR is not exported to the periplasm when expressed in the heterologous host Escherichia coli, and enzymatically active pre-GFOR is found in the cytoplasm. A precise replacement of the pre-GFOR signal peptide by an authentic E. coli Tat signal peptide, which is derived from pre-trimethylamine N-oxide (TMAO) reductase (TorA), allowed export of GFOR, together with its bound cofactor, to the E. coli periplasm. This export was inhibited by carbonyl cyanide m-chlorophenylhydrazone, but not by sodium azide, and was blocked in E. coli tatC andtatAE mutant strains, showing that membrane translocation of the TorA-GFOR fusion protein occurred via the Tat pathway and not via the Sec pathway. Furthermore, tight cofactor binding (and therefore correct folding) was found to be a prerequisite for proper translocation of the fusion protein. These results strongly suggest that Tat signal peptides are not universally recognized by different Tat translocases, implying that the signal peptides of Tat-dependent precursor proteins are optimally adapted only to their cognate export apparatus. Such a situation is in marked contrast to the situation that is known to exist for Sec-dependent protein translocation.

Comparison of signal peptides for efficient protein secretion in the baculovirus-silkworm system

2013 ◽  
Vol 8 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Yasuhiko Soejima ◽  
Jae Lee ◽  
Yudai Nagata ◽  
Hiroaki Mon ◽  
Kazuhiro Iiyama ◽  
...  
Keyword(s):  
Recombinant Proteins ◽  
Signal Peptide ◽  
Protein Production ◽  
Expression System ◽  
Secretory Protein ◽  
Secretory Proteins ◽  
Signal Peptides ◽  
Secretion Pathway ◽  
Key Factor ◽  
Secretion Efficiency

AbstractThe baculovirus-silkworm expression system is widely used as a mass production system for recombinant secretory proteins. However, the final yields of some recombinant proteins are not sufficient for industrial use. In this study, we focused on the signal peptide as a key factor for improving the efficiency of protein production. Endoplasmic reticulum (ER) translocation of newly synthesized proteins is the first stage of the secretion pathway; therefore, the selection of an efficient signal peptide would lead to the efficient secretion of recombinant proteins. The Drosophila Bip and honeybee melittin signal peptides have often been used in this system, but to the best of our knowledge, there has been no study comparing secretion efficiency between exogenous and endogenous signal peptides. In this study we employed signal peptides from 30K Da and SP2 proteins as endogenous signals, and compared secretion efficiency with those of exogenous or synthetic origins. We have found that the endogenous secretory signal from the 30K Da protein is the most efficient for recombinant secretory protein production in the baculovirus-silkworm expression system.

Authenticity of sugar – Identification of plant source and determination of geographical origin of sucrose

2015 ◽  
pp. 40-43 ◽  
Author(s):  
Andreas G. Degenhardt
Keyword(s):  
Metabolic Pathways ◽  
Isotope Ratio ◽  
Geographical Origin ◽  
Hydrological Cycle ◽  
Isotope Ratios ◽  
Saccharum Officinarum ◽  
Mass Spectrometric ◽  
C3 And C4 Plants ◽  
Plant Source

The isotope ratios of water, organic matter and micronutrients from food are dependent on the circumstances and sites of their origin and production. Analytical methods, based on mass spectrometry, are established for routine determination of isotopes. Differentiation between metabolic pathways of C3 and C4 plants is realizable by determination 13C/12C ratios which can distinguish and identify sucrose from pure beet (Beta vulgaris) and pure cane (Saccharum officinarum). Influenced by the worldwide hydrological cycle the isotope ratios of 2H/1H and 18O/16O vary systematically, the variations give information about geographical origin. The exemplarily determination of authenticity is demonstrated by using mass spectrometric isotope ratio evaluation for identification of plant source and geographical origin with the help of selected sugar samples with known origin.

scholarly journals Categorization of Orthologous Gene Clusters in 92 Ascomycota Genomes Reveals Functions Important for Phytopathogenicity

2021 ◽  
Vol 7 (5) ◽  
pp. 337
Author(s):  
Daniel Peterson ◽  
Tang Li ◽  
Ana M. Calvo ◽  
Yanbin Yin
Keyword(s):  
Comparative Genomics ◽  
Orthologous Gene ◽  
Gene Clusters ◽  
Phytopathogenic Fungi ◽  
Secreted Proteins ◽  
Economic Losses ◽  
Comparative Genomic ◽  
Signal Peptides ◽  
Comparative Genomics Analysis

Phytopathogenic Ascomycota are responsible for substantial economic losses each year, destroying valuable crops. The present study aims to provide new insights into phytopathogenicity in Ascomycota from a comparative genomic perspective. This has been achieved by categorizing orthologous gene groups (orthogroups) from 68 phytopathogenic and 24 non-phytopathogenic Ascomycota genomes into three classes: Core, (pathogen or non-pathogen) group-specific, and genome-specific accessory orthogroups. We found that (i) ~20% orthogroups are group-specific and accessory in the 92 Ascomycota genomes, (ii) phytopathogenicity is not phylogenetically determined, (iii) group-specific orthogroups have more enriched functional terms than accessory orthogroups and this trend is particularly evident in phytopathogenic fungi, (iv) secreted proteins with signal peptides and horizontal gene transfers (HGTs) are the two functional terms that show the highest occurrence and significance in group-specific orthogroups, (v) a number of other functional terms are also identified to have higher significance and occurrence in group-specific orthogroups. Overall, our comparative genomics analysis determined positive enrichment existing between orthogroup classes and revealed a prediction of what genomic characteristics make an Ascomycete phytopathogenic. We conclude that genes shared by multiple phytopathogenic genomes are more important for phytopathogenicity than those that are unique in each genome.

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