Evaluating signal peptide prediction methods for Gram-positive bacteria

Gram-positive bacteria have been widely investigated for their huge capability to secrete proteins, such as those involved in gene expression, bacterial surface display and bacterial pathogenesis. The N-terminal signal peptide of a secretory protein is responsible for the translocation of polypeptide through the cytoplasmic membrane. Recently, the signal peptide prediction has become a major task in bioinformatics, and many programs with different algorithms were developed to predict signal peptides. In this paper, five prediction programs (SignalP 3.0, PrediSi, Phobius, SOSUIsignal and SIG-Pred) were selected to evaluate their prediction accuracy for signal peptides and cleavage site using 509 unbiased and experimentally verified Gram-positive protein sequences. The results showed that SignalP was the most accurate program in signal peptide (96% accuracy) and cleavage site (83%) prediction. Prediction performance could further be improved by combining multiple methods into consensus prediction, which would increase the accuracy to 98%, and decrease the false positive to zero. When the consensus method was used to predict Bacillus’s extracellular proteins identified by proteomics, more new signal peptides were successfully identified. It could be concluded that the consensus method would be useful to make prediction of signal peptides more reliable.     

New light on bacterial carbonic anhydrases phylogeny based on the analysis of signal peptide sequences

Among protein families, carbonic anhydrases (CAs, EC 4.2.1.1) are metalloenzymes characterized by a common reaction mechanism in all life domains: the carbon dioxide hydration to bicarbonate and protons (CO₂+H₂O ? HCO₃^?+H⁺). Six genetically distinct CA families are known to date, the α-, β-, γ-, δ-, ζ- and η-CAs. The last CA class was recently discovered analyzing the amino acid sequences of CAs from Plasmodia. Bacteria encode for enzymes belonging to the α-, β-, and γ-CA classes and recently, phylogenetic analysis revealed an interesting relationship regarding the evolution of bacterial CA classes. This result evidenced that the three bacterial CA classes, in spite of the high level of the structural similarity, are evolutionarily distinct, but we noted that the primary structure of some β-CAs identified in the genome of Gram-negative bacteria present a pre-sequence of 18 or more amino acid residues at the N-terminal part. These observations and subsequent phylogenetic data presented here prompted us to propose that the β-CAs found in Gram-negative bacteria with a periplasmic space and characterized by the presence of a signal peptide might have a periplasmic localization and a role similar to that described previously for the α-CAs.     

ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites.

We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level is well above that of the publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within +/-2 residues from the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web-server at http://www.cbs.dtu.dk/services/ChloroP/.     

LabCaS: Labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields

The calpain family of Ca²⁺‐dependent cysteine proteases plays a vital role in many important biological processes which is closely related with a variety of pathological states. Activated calpains selectively cleave relevant substrates at specific cleavage sites, yielding multiple fragments that can have different functions from the intact substrate protein. Until now, our knowledge about the calpain functions and their substrate cleavage mechanisms are limited because the experimental determination and validation on calpain binding are usually laborious and expensive. In this work, we aim to develop a new computational approach (LabCaS) for accurate prediction of the calpain substrate cleavage sites from amino acid sequences. To overcome the imbalance of negative and positive samples in the machine‐learning training which have been suffered by most of the former approaches when splitting sequences into short peptides, we designed a conditional random field algorithm that can label the potential cleavage sites directly from the entire sequences. By integrating the multiple amino acid features and those derived from sequences, LabCaS achieves an accurate recognition of the cleave sites for most calpain proteins. In a jackknife test on a set of 129 benchmark proteins, LabCaS generates an AUC score 0.862. The LabCaS program is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/LabCaS . Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Signal peptidases and signal peptide hydrolases

Signal peptidases, the endoproteases that remove the amino-terminal signal sequence from many secretory proteins, have been isolated from various sources. Seven signal peptidases have been purified, two fromE. coli, two from mammalian sources, and three from mitochondrial matrix. The mitochondrial enzymes are soluble and function as a heterogeneous dimer. The mammalian enzymes are isolated as a complex and share a common glycosylated subunit. The bacterial enzymes are isolated as monomers and show no sequence homology with each other or the mammalian enzymes. The membrane-bound enzymes seem to require a substrate containing a consensus sequence following the –3, –1 rule of von Heijne at the cleavage site; however, processing of the substrate is strongly influenced by the hydrophobic region of the signal peptide. The enzymes appear to recognize an unknown three-dimensional motif rather than a specific amino acid sequence around the cleavage site. The matrix mitochondrial enzymes are metallo-endopeptidases; however, the other signal peptidases may belong to a unique class of proteases as they are resistant to chelators and most protease inhibitors. There are no data concerning the substrate binding site of these enzymes. In vivo, the signal peptide is rapidly degraded. Three different enzymes inEscherichia coli that can degrade a signal peptidein vitro have been identified. The intact signal peptide is not accumulated in mutants lacking these enzymes, which suggests that these peptidases individually are not responsible for the degredation of an intact signal peptidein vivo. It is speculated that signal peptidases and signal peptide hydrolases are integral components of the secretory pathway and that inhibition of the terminal steps can block translocation.     

Dichroic statistical model for prediction and analysis of peptide helicity

Traditional statistical models for the prediction of peptide helicity are written in terms of the mean fractional helicity of the peptide residues. Far ultraviolet circular dichroic measurements of peptide solutions are converted to mean fractional helicity by partitioning the observed ellipticity between that of a perfect helix and a random coil. This partition does not adequately represent the ensemble of peptide molecules present in solution that populate imperfect helical conformations of quite variable lengths. A new dichroic statistical model has been written in terms of ellipticity rather than fractional helical content that recognizes (1) the source of ellipticity, peptide bond adsorption; (2) the differential ellipticity of peptide bonds in the terminal and interior helical turns; and (3) the contributions of each participant in a conformational ensemble to the observed ellipticity. Comparative analyses of host/guest peptides indicates that significant differences are obtained between residue w and n weights and ellipticity values using the traditional and dichroic statistical models. Proteins 28:467–480, 1997. © 1997 Wiley-Liss, Inc.     

The effect of peptide length on the cleavage kinetics of 2-chlorotrityl resin-bound ethers.

Different characteristics of cleavage kinetics of resin-bound amino alcohols and their peptide derivatives were observed in acid containing protic and aprotic solvent mixtures. The hydrolysis reactions are hindered by steric crowding around the cleaving C--O bond and accelerated by the special solvation effect of CF(3)CH(2)OH on the peptide chain as well as the increase of the strength and concentration of the acid. In trifluoroacetic acid containing mixtures, trifluoroacetylation of the peptide alcohols was detected. The appearance of O-trifluoroacetyl serine and threonine derivatives is detected in cleavage mixtures containing trifluoroacetic acid in anhydrous solvent.     

Characterization of the cleavage of signal peptide at the C-terminus of hepatitis C virus core protein by signal peptide peptidase

Production of hepatitis C virus (HCV) core protein requires the cleavages of polyprotein by signal peptidase and signal peptide peptidase (SPP). Cleavage of signal peptide at the C-terminus of HCV core protein by SPP was characterized in this study. The spko mutant (mutate a.a. 189–193 from ASAYQ to PPFPF) is more efficient than the A/F mutant (mutate a.a 189 and 191 from A to F) in blocking the cleavage of signal peptide by signal peptidase. The cleavage efficiency of SPP is inversely proportional to the length of C-terminal extension of the signal peptide: the longer the extension, the less efficiency the cleavage is. Thus, reducing the length of C-terminal extension of signal peptide by signal peptidase cleavage could facilitate further cleavage by SPP. The recombinant core protein fused with signal peptide from the C-terminus of p7 protein, but not those from the C-termini of E1 and E2, could be cleaved by SPP. Therefore, the sequence of the signal peptide is important but not the sole determinant for its cleavage by SPP. Replacement of the HCV core protein E.R.-associated domain (a.a. 120–150) with the E.R.-associated domain (a.a.1–50) of SARS-CoV membrane protein results in the failure of cleavage of this recombinant protein by SPP, though this protein still is E.R.-associated. This result suggests that not only E.R.-association but also specific protein sequence is important for the HCV core protein signal peptide cleavage by SPP. Thus, our results suggest that both sequences of the signal peptide and the E.R.-associated domain are important for the signal peptide cleavage of HCV core protein by SPP. Electronic Supplementary MaterialThe online version of this article (doi: ) contains supplementary material, which is available to authorized users.     

A combined transmembrane topology and signal peptide prediction method

An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at     

Computational prediction of proteotypic peptides

Evaluation of: Mallick P, Schirle M, Chen SS et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25(1), 125–131 (2007).

Mass spectrometry, the driving analytical force behind proteomics, is primarily used to identify and quantify as many proteins in a complex biological mixture as possible. While there are many ways to prepare samples, one aspect that is common to a vast majority of bottom-up proteomic studies is the digestion of proteins into tryptic peptides prior to their analysis by mass spectrometry. As correctly highlighted by Mallick and colleagues, only a few peptides are repeatedly and consistently identified for any given protein within a complex mixture. While the existence of these proteotypic peptides (to borrow the authors’ terminology) is well known in the proteomics community, there has never been an empirical method to recognize which peptides may be proteotypic for a given protein. In this study, the investigators discovered over 16,000 proteotypic peptides from a collection of over 600,000 peptide identifications obtained from four different analytical platforms. The study examined a number of physicochemical parameters of these peptides to determine which properties were most relevant in defining a proteotypic peptide. These characteristic properties were then used to develop computational tools to predict proteotypic peptides for any given protein within an organism.     

Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria

A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. The strength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with their HMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminating secreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit scores for secretory proteins. The signal peptide with the maximum bit score strongly directs proteins secretion.     

A computational method for the analysis and prediction of protein:phosphopeptide-binding sites

Phosphopeptide-binding domains, including the FHA, SH2, WW, WD40, MH2, and Polo-box domains, as well as the 14-3-3 proteins, exert control functions in important processes such as cell growth, division, differentiation, and apoptosis. Structures and mechanisms of phosphopeptide binding are generally diverse, revealing few general principles. A computational method for analysis of phosphopeptide-binding domains was therefore developed to elucidate the physical and chemical nature of phosphopeptide binding, given this lack of structural similarity. The surfaces of nine phosphopeptide-binding proteins, representing seven distinct classes of phosphopeptide-binding modules, were discretized, and encoded with information about amino acid identity, surface curvature, and electrostatic potential at every point on the surface in order to identify local surface properties enriched in phosphoresidue contact sites. Cross-validation indicated that propensities corresponding to this enrichment calculated from a subset of the training data could be used to predict the phosphoresidue contact site on proteins not used in training with no false negative results, and with few unconfirmed positive predictions. The locations of phosphoresidue contact sites were then predicted on the surfaces of the checkpoint kinase Chk1 and the BRCA1 BRCT repeat domain, and these predictions are consistent with recent experimental evidence.     

Feature selection for the prediction of translation initiation sites

Translation initiation sites （TISs） are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance： the nature of the cDNA sequence sets, the relevant features selected. and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons. the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree. naive Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful. while the experiments showed promising results.     

Automated prediction of ligand-binding sites in proteins

We present a method, termed AutoLigand, for the prediction of ligand-binding sites in proteins of known structure. The method searches the space surrounding the protein and finds the contiguous envelope with the specified volume of atoms, which has the largest possible interaction energy with the protein. It uses a full atomic representation, with atom types for carbon, hydrogen, oxygen, nitrogen and sulfur (and others, if desired), and is designed to minimize the need for artificial geometry. Testing on a set of 187 diverse protein-ligand complexes has shown that the method is successful in predicting the location and approximate volume of the binding site in 73% of cases. Additional testing was performed on a set of 96 protein-ligand complexes with crystallographic structures of apo and holo forms, and AutoLigand was able to predict the binding site in 80% of the apo structures.     

Optimization of the hydroxylamine cleavage of an expressed fusion protein to produce a recombinant antimicrobial peptide

Hydroxylamine was used to cleave the Asn-Gly peptide bond between the fusion partner and the antimicrobial peptide of interest, a magainin derivative (MSI-344). The efficiency of reaction depended on the hydroxylamine concentration, denaturant, pH, and the fused protein concentration. The optimal cleavage solution consisted of guanidine HCl as the denaturant, pH 8.1, and 6.7 mg ml^–1 of fused MSI-344. This optimized cleavage solution resulted in a high yield (95% ) of MSI-344 from a cultivation of E. coli. This result suggests potential applications for using hydroxylamine to cleave basic peptides produced from fusion proteins.     

Monoclonal antibodies targeting the synthetic peptide corresponding to the polybasic cleavage site on H5N1 influenza hemagglutinin

Background

Avian influenza H5N1 virus is highly pathogenic partially because its H5 hemagglutinin contains a polybasic cleavage site that can be processed by proteases in multiple organs.

Methods

Monoclonal antibodies (mAb) specific to the synthetic peptide of hemagglutinin polybasic cleavage site of H5N1 virus were raised and tested for their neutralizing potential.

Results

Purified mAb showed suppression of H5N1 pseudovirus infection on Madin-Darby Canine Kidney (MDCK) cells but the efficacy was less than 50%. Since those mAb are specific to the intact uncut polybasic cleavage site of hemagglutinin, their efficacy depends on the extent of hemagglutinin cleavage on the viral surface.

Conclusions

Proteolytic analysis suggests the low efficacy associated with those mAb may be due to proteolytic cleavage already present on the majority of hemagglutinin prior to the infection of virus.