Feature-based reappraisal of the Bacillus subtilis exoproteome

Proteomics-based verification of computer-assisted predictions on bacterial protein export have indicated that problems occur with the distinction between (Sec-type) signal peptides that govern protein secretion, and lipoprotein signal peptides or amino-terminal membrane anchors that cause protein retention in the membrane. Therefore, the main aim of this study was to investigate whether feature-based predictions by the SecretomeP (SecP) algorithm will aid the proteomics-based analysis of protein export in Bacillus subtilis. The SecP algorithm is trained to recognize features such as secondary structure and disordered regions, which are generally present in secreted proteins. The results showed that membrane-retained proteins receive, in general, high SecP scores, similar to the scores of secretory proteins. Importantly, the SecP algorithm aided in the re-evaluation of a class of previously identified proteins that remain attached to the membrane despite the presence of an apparent Sec-type signal peptide. These so-called 'Sec-attached' proteins receive on average a lower SecP score, and several of these proteins could be unmasked as transmembrane proteins by combined SecP and signal peptide analyses. Finally, the present study suggests that feature-based outlier analysis may provide leads towards the discovery of novel special-purpose pathways for bacterial protein export.     

An analysis of the helix-to-strand transition between peptides with identical sequence

An analysis of peptide segments with identical sequence but that differ significantly in structure was performed over non-redundant databases of protein structures. We focus on those peptides, which fold into an alpha-helix in one protein but a beta-strand in another. While the study shows that many such structurally ambivalent peptides contain amino acids with a strong helical preference collocated with amino acids with a strong strand preference, the results overwhelmingly indicate that the peptide's environment ultimately dictates its structure. Furthermore, the first naturally occurring structurally ambivalent nonapeptide from evolutionary unrelated proteins is described, highlighting the intrinsic plasticity of peptide sequences. We even find seven proteins that show structural ambivalence under different conditions. Finally, a computer algorithm has been implemented to identify regions in a given sequence where secondary structure prediction programs are likely to make serious mispredictions.     

Optimization of the methods for small peptide solution structure determination by NMR spectroscopy

NMR spectroscopy was recognized as a method of protein structure determination in solution. However, determination of the conformation of small peptides, which undergo fast molecular motions, remains a challenge. This is mainly caused by the impossibility to collect the required quantity of the distance and dihedral angle restraints from NMR spectra. At the same time, short charged peptides play an important role in a number of biological processes, in particular in pathogenesis of neurodegenerative diseases including Alzheimer’s disease. Therefore, development of a method for structure simulation of small peptides in aqueous environment using the most realistic force fields seems to be of current importance. Such algorithm has been developed using the Amber-03 force field and Gromacs program after modification of its code. Calculation algorithm has been verified on a model peptide with a known solution structure and a metal-binding fragment of rat β-amyloid, whose structure has been determined by alternative methods. The developed algorithm substantially increases structure quality, in particular Ramachandran plot statistics, and decreases RMSD of atomic coordinates inside the calculated family. The described protocol can be used for determination of conformation of short peptides, and also for optimization of structure of larger proteins containing poorly structured fragments.     

Clustering of protein structural fragments reveals modular building block approach of nature

Structures of peptide fragments drawn from a protein can potentially occupy a vast conformational continuum. We co-ordinatize this conformational space with the help of geometric invariants and demonstrate that the peptide conformations of the currently available protein structures are heavily biased in favor of a finite number of conformational types or structural building blocks. This is achieved by representing a peptides' backbone structure with geometric invariants and then clustering peptides based on closeness of the geometric invariants. This results in 12,903 clusters, of which 2207 are made up of peptides drawn from functionally and/or structurally related proteins. These are termed "functional" clusters and provide clues about potential functional sites. The rest of the clusters, including the largest few, are made up of peptides drawn from unrelated proteins and are termed "structural" clusters. The largest clusters are of regular secondary structures such as helices and beta strands as well as of beta hairpins. Several categories of helices and strands are discovered based on geometric differences. In addition to the known classes of loops, we discover several new classes, which will be useful in protein structure modeling. Our algorithm does not require assignment of secondary structure and, therefore, overcomes the limitations in loop classification due to ambiguity in secondary structure assignment at loop boundaries.     

Utility of characteristic QTOF MS/MS fragmentation for MHC class I peptides

Systematic investigation of cellular process by mass spectrometric detection of peptides obtained from proteins digestion or directly from immuno-purification can be a powerful tool when used appropriately. The true sequence of these peptides is defined by the interpretation of spectral data using a variety of available algorithms. However peptide match algorithm scoring is typically based on some, but not all, of the mechanisms of peptide fragmentation. Although algorithm rules for soft ionization techniques generally fit very well to tryptic peptides, manual validation of spectra is often required for endogenous peptides such as MHC class I molecules where traditional trypsin digest techniques are not used. This study summarizes data mining and manual validation of hundreds of peptide sequences from MHC class I molecules in publically available data files. We herein describe several important features to improve and quantify manual validation for these endogenous peptides--post automated algorithm searching. Important fragmentation patterns are discussed for the studied MHC Class I peptides. These findings lead to practical rules that are helpful when performing manual validation. Furthermore, these observations may be useful to improve current peptide search algorithms or development of novel software tools.     

Accurate Prediction of Peptide Binding Sites on Protein Surfaces

Many important protein–protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another. Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available (either known experimentally or readily modeled) but where no structure of the protein–peptide complex is known. To address this gap, we present an approach that can accurately predict peptide binding sites on protein surfaces. For peptides known to bind a particular protein, the method predicts binding sites with great accuracy, and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind. We used known protein–peptide complexes to derive preferences, in the form of spatial position specific scoring matrices, which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides. We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence. The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides, including RG-rich proteins with SMN domains, Epstein-Barr virus LMP1 with TRADD domains, DBC1 with Sir2, and the Ago hook with Argonaute PIWI domain. The method, and associated statistics, is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology.     

Development of a bacteriophage-based system for the selection of structured peptides

Short structured peptides can provide scaffolds for protease-resistant peptide therapeutics, serve as useful building blocks in biomedical and biotechnological applications, and shed light on the role of secondary structure elements in protein folding. It is well known that directed evolution is a powerful method for creating proteins and peptides with novel properties, and a system for the selection of short peptides based on structure from a randomized library would be an important advancement. In this study, phage particles monovalently displaying a short peptide and an N-terminal 6×His tag on their P3 coat protein were bound to nickel agarose resin and were subsequently challenged with a protease that specifically cleaves at a site within the peptide. The extent to which phage is proteolytically released from the resin was found to be dependent on the structural properties of the inserted peptide sequences. As proofs-of-concept, a structured peptide has been isolated from a pool of flexible peptides using a trypsin selection, and a flexible peptide has been isolated from a pool of structured peptides using a chymotrypsin selection. This selection system will be a strong technological platform for the creation of short peptides with interesting structural properties using directed evolution.     

Lipid-destabilising properties of a peptide with structural plasticity

The Chameleon peptide (Cham) is a peptide designed from two regions of the GB1 protein, one folded as an alpha-helix and the other as a beta structure. Depending on the environment, the Cham peptide adopts an alpha or a beta conformation when inserted in different locations of GB1. This environment dependence is also observed for tilted peptides. These short protein fragments, able to destabilise organised system, are mainly folded in beta structure in water and in alpha helix in a hydrophobic environment, like the lipid bilayer. In this paper, we tested whether the Cham peptide can be qualified as a tilted peptide. For this, we have compared the properties of Cham peptide (hydrophobicity, destabilising properties, conformation) to those of tilted peptides. The results suggest that Cham is a tilted peptide. Our study, together the presence of tilted fragments in transconformational proteins, suggests a relationship between tilted peptides and structural lability.     

Using peptide strategy for study functions and structure of signal proteins with enzymatic activity

The peptide strategy, a new direction of molecular endocrinology, includes the synthesis of peptides corresponding to functional regions of signal proteins, the use of the peptides for study of the molecular mechanisms of transduction of hormonal signal into cell ant the development of selective regulators of hormonal signaling systems on the basis of these peptides. The peptide strategy is used for study a wide spectrum of the proteins, components of signal systems, the proteins possessing the catalytic activity in particular, such as tyrosine kinases receptors, the enzymes generating the second messengers, serine/threonine protein kinase, phosphatases. In the first time in the review the data concerning the synthetic peptides, derivatives of the primary structure of proteins with the enzymatic activity, their application for study of the structural-functional organization and the molecular mechanisms of action of signal proteins, and the construction of regulators of fundamental cell processes on the basis of these peptides are analyzed and summarized.     

Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC‐MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four‐protein mixture, the same four‐protein mixture spiked into a complex biological background, and a variety of other “system” type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.     

Algorithm for identification of fusion proteins via mass spectrometry

Identification of fusion proteins has contributed significantly to our understanding of cancer progression, yielding important predictive markers and therapeutic targets. While fusion proteins can be potentially identified by mass spectrometry, all previously found fusion proteins were identified using genomic (rather than mass spectrometry) technologies. This lack of MS/MS applications in studies of fusion proteins is caused by the lack of computational tools that are able to interpret mass spectra from peptides covering unknown fusion breakpoints (fusion peptides). Indeed, the number of potential fusion peptides is so large that the existing MS/MS database search tools become impractical even in the case of small genomes. We explore computational approaches to identifying fusion peptides, propose an algorithm for solving the fusion peptide identification problem, and analyze the performance of this algorithm on simulated data. We further illustrate how this approach can be modified for human exons prediction.     

Isolation and structure elucidation of a novel 5-kDa peptide from neurohaemal lobes of the corpora cardiaca of Locusta migratoria (Insecta, Orthoptera)

Two predominant peptides have been isolated from neurohaemal lobes of corpora cardiaca of 8000 adults of Locusta migratoria. Both peptides have been unambiguously characterized by automated peptide microsequencing and liquid secondary-ion mass spectrometry as a 50-residue peptide (5K peptide) and a 48-residue isologue (5K' peptide). Computer search of sequence data banks did not reveal any significant similarity with other identified proteins. The 5K peptides are remarkably rich in alanine residues (25%) and contain a stretch of five consecutive alanines. This structure suggests that these molecules could correspond to spacer peptides. This assumption is corroborated in the accompanying paper [Lagueux et al. (1990) Eur. J. Biochem. 187, 249-254] on the molecular cloning of the precursor protein which attributes to the 5K peptides a role analogous to that of the C peptides of insulins.     

Exploring conformational space using a mean field technique with MOLS sampling

The computational identification of all the low energy structures of a peptide given only its sequence is not an easy task even for small peptides, due to the multiple-minima problem and combinatorial explosion. We have developed an algorithm, called the MOLS technique, that addresses this problem, and have applied it to a number of different aspects of the study of peptide and protein structure. Conformational studies of oligopeptides, including loop sequences in proteins have been carried out using this technique. In general the calculations identified all the folds determined by previous studies, and in addition picked up other energetically favorable structures. The method was also used to map the energy surface of the peptides. In another application, we have combined the MOLS technique, using it to generate a library of low energy structures of an oligopeptide, with a genetic algorithm to predict protein structures. The method has also been applied to explore the conformational space of loops in protein structures. Further, it has been applied to the problem of docking a ligand in its receptor site, with encouraging results.     

A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry

MOTIVATION: Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing. RESULTS: We developed a hierarchical statistical model (HSM) that jointly models the uncertainty of the identified peptides and proteins and can be applied to any scoring system. With data sets of a standard mixture and the yeast proteome, we demonstrate that the HSM offers a reliable or at least conservative false discovery rate (FDR) estimate for peptide and protein identifications. The probability measure of HSM also offers a powerful discriminating score for peptide identification. AVAILABILITY: The algorithm is available upon request from the authors.     

Structure-function studies of chemokine-derived carboxy-terminal antimicrobial peptides

Recent reports which show that several chemokines can act as direct microbicidal agents have drawn renewed attention to these chemotactic signalling proteins. Here we present a structure-function analysis of peptides derived from the human chemokines macrophage inflammatory protein-3α (MIP-3α/CCL20), interleukin-8 (IL-8), neutrophil activating protein-2 (NAP-2) and thrombocidin-1 (TC-1). These peptides encompass the C-terminal α-helices of these chemokines, which have been suggested to be important for the direct antimicrobial activities. Far-UV CD spectroscopy showed that the peptides are unstructured in aqueous solution and that a membrane mimetic solvent is required to induce a helical secondary structure. A co-solvent mixture was used to determine solution structures of the peptides by two-dimensional ¹H-NMR spectroscopy. The highly cationic peptide, MIP-3α_51-70, had the most pronounced antimicrobial activity and displayed an amphipathic structure. A shorter version of this peptide, MIP-3α_59-70, remained antimicrobial but its structure and mechanism of action were unlike that of the former peptide. The NAP-2 and TC-1 proteins differ in their sequences only by the deletion of two C-terminal residues in TC-1, but intact TC-1 is a very potent antimicrobial while NAP-2 is inactive. The corresponding C-terminal peptides, NAP-2_50-70 and TC-1_50-68, had very limited and no bactericidal activity, respectively. This suggests that other regions of TC-1 contribute to its bactericidal activity. Altogether, this work provides a rational structural basis for the biological activities of these peptides and proteins and highlights the importance of experimental characterization of peptide fragments as distinct entities because their activities and structural properties may differ substantially from their parent proteins.     

Structural prediction of peptides binding to MHC class I molecules

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.     

Predicted calmodulin-binding sequence in the gamma subunit of phosphorylase b kinase

A basic, amphiphilic alpha helix is a structural feature common to a variety of inhibitors of calmodulin and to the calmodulin-binding domains of myosin light chain kinases. To aid in recognizing this structural feature in sequences of peptides and proteins we have developed a computer algorithm which searches for sequences of appropriate length, hydrophobicity, helical hydrophobic moment, and charge to be considered as potential calmodulin-binding sequences. Such sequences occurred infrequently in proteins of known crystal structure. This algorithm was used to find the most likely site in the catalytic (gamma) subunit of phosphorylase b kinase for interaction with calmodulin (the delta subunit). A peptide corresponding to this site (residues 341-361 of the gamma subunit) was synthesized and found to bind calmodulin with approximately an 11 nM dissociation constant. A variant of this peptide in which an aspartic acid at position 7 in its sequence (347 of the gamma subunit) was replaced with an asparagine was found to bind calmodulin with approximately a 3 nM dissociation constant.     

Direct activation of GTP-binding proteins by venom peptides that contain cationic clusters within their alpha-helical structures

Direct interactions of venom peptides that contained a cysteine-stabilized alpha-helical motif within their internal molecules with alpha beta gamma-trimeric GTP-binding proteins (G proteins) were studied in reconstituted phospholipid vesicles. Mast cell-degranulating (MCD) peptide stimulated the steady-state rate of GTP hydrolysis catalyzed by the reconstituted G proteins. Synthetic D-MCD peptide, the optical isomer of MCD peptide, was also effective in the activation of G proteins as L-MCD peptide. The stimulations by L- and D-peptides were both abolished in G proteins that had been ADP-ribosylated by pertussis toxin. Charybdotoxin also stimulated, though slightly, the GTPase activity of G proteins. Such a stimulation was, however, not observed upon the incubation of G proteins with other venom peptides such as apamin, sarafotoxin and endothelin. Thus, in comparison of the amino acid sequences of their venom peptides, the extent of the activation of G proteins appeared to be correlated with the number of basic amino acid residues around the alpha-helix. These results suggest that cationic clusters at one side of the alpha-helical surface are more important in the direct activation of G proteins than a specific, alpha-helical structure.     

A Heuristic method for assigning a false-discovery rate for protein identifications from Mascot database search results

MS/MS and database searching has emerged as a valuable technology for rapidly analyzing protein expression, localization, and post-translational modifications. The probability-based search engine Mascot has found widespread use as a tool to correlate tandem mass spectra with peptides in a sequence database. Although the Mascot scoring algorithm provides a probability-based model for peptide identification, the independent peptide scores do not correlate with the significance of the proteins to which they match. Herein, we describe a heuristic method for organizing proteins identified at a specified false-discovery rate using Mascot-matched peptides. We call this method PROVALT, and it uses peptide matches from a random database to calculate false-discovery rates for protein identifications and reduces a complex list of peptide matches to a nonredundant list of homologous protein groups. This method was evaluated using Mascot-identified peptides from a Trypanosoma cruzi epimastigote whole-cell lysate, which was separated by multidimensional LC and analyzed by MS/MS. PROVALT was then compared with the two traditional methods of protein identification when using Mascot, the single peptide score and cumulative protein score methods, and was shown to be superior to both in regards to the number of proteins identified and the inclusion of lower scoring nonrandom peptide matches.     

Versatile modes of peptide recognition by the AAA+ adaptor protein SspB

Energy-dependent proteases often rely on adaptor proteins to modulate substrate recognition. The SspB adaptor binds peptide sequences in the stress-response regulator RseA and in ssrA-tagged proteins and delivers these molecules to the AAA+ ClpXP protease for degradation. The structure of SspB bound to an ssrA peptide is known. Here, we report the crystal structure of a complex between SspB and its recognition peptide in RseA. Notably, the RseA sequence is positioned in the peptide-binding groove of SspB in a direction opposite to the ssrA peptide, the two peptides share only one common interaction with the adaptor, and the RseA interaction site is substantially larger than the overlapping ssrA site. This marked diversity in SspB recognition of different target proteins indicates that it is capable of highly flexible and dynamic substrate delivery.