A classification of disulfide patterns and its relationship to protein structure and function

Lasso Proteins-Unifying Cysteine Knots and Miniproteins

Complex lasso proteins are a recently identified class of biological compounds that are present in considerable fraction of proteins with disulfide bridges. In this work, we look at complex lasso proteins as a generalization of well-known cysteine knots and miniproteins (lasso peptides). In particular, we show that complex lasso proteins with the same crucial topological features-cysteine knots and lasso peptides-are antimicrobial proteins, which suggests that they act as a molecular plug. Based on an analysis of the stability of the lasso piercing residue, we also introduce a method to determine which lasso motif is potentially functional. Using this method, we show that the lasso motif in antimicrobial proteins, as well in that in cytokines, is functionally relevant. We also study the evolution of lasso motifs, their conservation, and the usefulness of the lasso fingerprint, which extracts all topologically non-triviality concerning covalent loops. The work is completed by the presentation of extensive statistics on complex lasso proteins to analyze, in particular, the strange propensity for "negative" piercings. We also identify 21 previously unknown complex lasso proteins with an ester and a thioester bridge.

The crystal structure of N,N′-(Disulfanediyldi-2,1-phenylene)di(6′-methylpyridine)-2-carboxamide, C26H22N4O2S2

C26H22N4O2S2, triclinic, P 1 ‾ $P‾{1}$ (no. 2), a = 7.9495(7) Å, b = 11.2494(12) Å, c = 13.7829(13) Å, α = 81.433(2)°, β = 76.536(1)°, γ = 82.641(2)°, V = 1179.7(2) Å3, Z = 2, R gt (F) = 0.0727, wR ref(F 2) = 0.2189, T = 298(2) K.

Secreted Cysteine-Rich Repeat Proteins "SCREPs": A Novel Multi-Domain Architecture

Peptide toxins isolated from animal venom secretions have proven to be useful pharmacological tools for probing the structure and function of a number of molecular receptors. Their molecular structures are stabilized by posttranslational formation of multiple disulfide bonds formed between sidechain thiols of cysteine residues, resulting in high thermal and chemical stability. Many of these peptides have been found to be potent modulators of ion channels, making them particularly influential in this field. Recently, several peptide toxins have been described that have an unusual tandem repeat organization, while also eliciting a unique pharmacological response toward ion channels. Most of these are two-domain peptide toxins from spider venoms, such as the double-knot toxin (DkTx), isolated from the Earth Tiger tarantula (Haplopelma schmidti). The unusual pharmacology of DkTx is its high avidity for its receptor (TRPV1), a property that has been attributed to a bivalent mode-of-action. DkTx has subsequently proven a powerful tool for elucidating the structural basis for the function of the TRPV1 channel. Interestingly, all tandem repeat peptides functionally characterized to date share this high avidity to their respective binding targets, suggesting they comprise an unrecognized structural class of peptides with unique structural features that result in a characteristic set of pharmacological properties. In this article, we explore the prevalence of this emerging class of peptides, which we have named Secreted, Cysteine-rich REpeat Peptides, or “SCREPs.” To achieve this, we have employed data mining techniques to extract SCREP-like sequences from the UniProtKB database, yielding approximately sixty thousand candidates. These results indicate that SCREPs exist within a diverse range of species with greatly varying sizes and predicted fold types, and likely include peptides with novel structures and unique modes of action. We present our approach to mining this database for discovery of novel ion-channel modulators and discuss a number of “hits” as promising leads for further investigation. Our database of SCREPs thus constitutes a novel resource for biodiscovery and highlights the value of a data-driven approach to the identification of new bioactive pharmacological tools and therapeutic lead molecules.

Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23

BACKGROUND

Disulfide-rich peptides (DRPs) are found throughout nature. They are suitable scaffolds for drug development due to their small cores, whose disulfide bonds impart extraordinary chemical and biological stability. A challenge in developing a DRP therapeutic is to engineer binding to a specific target. This challenge can be overcome by (i) sampling the large sequence space of a given scaffold through a phage display library and by (ii) panning multiple libraries encoding structurally distinct scaffolds. Here, we implement a protocol for defining these diverse scaffolds, based on clustering structurally defined DRPs according to their conformational similarity.

RESULTS

We developed and applied a hierarchical clustering protocol based on DRP structural similarity, followed by two post-processing steps, to classify 806 unique DRP structures into 81 clusters. The 20 most populated clusters comprised 85% of all DRPs. Representative scaffolds were selected from each of these clusters; the representatives were structurally distinct from one another, but similar to other DRPs in their respective clusters. To demonstrate the utility of the clusters, phage libraries were constructed for three of the representative scaffolds and panned against interleukin-23. One library produced a peptide that bound to this target with an IC₅₀ of 3.3 μM.

CONCLUSIONS

Most DRP clusters contained members that were diverse in sequence, host organism, and interacting proteins, indicating that cluster members were functionally diverse despite having similar structure. Only 20 peptide scaffolds accounted for most of the natural DRP structural diversity, providing suitable starting points for seeding phage display experiments. Through selection of the scaffold surface to vary in phage display, libraries can be designed that present sequence diversity in architecturally distinct, biologically relevant combinations of secondary structures. We supported this hypothesis with a proof-of-concept experiment in which three phage libraries were constructed and panned against the IL-23 target, resulting in a single-digit μM hit and suggesting that a collection of libraries based on the full set of 20 scaffolds increases the potential to identify efficiently peptide binders to a protein target in a drug discovery program.

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

MOTIVATION

Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g., >3 bonds, is too low to effectively assist structure assembly simulations.

RESULTS

We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins.

AVAILABILITY AND IMPLEMENTATION

http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/

CONTACT

zhng@umich.edu or hbshen@sjtu.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification and Characterization of a Novel Family of Cysteine-Rich Peptides (MgCRP-I) from Mytilus galloprovincialis

We report the identification of a novel gene family (named MgCRP-I) encoding short secreted cysteine-rich peptides in the Mediterranean mussel Mytilus galloprovincialis. These peptides display a highly conserved pre-pro region and a hypervariable mature peptide comprising six invariant cysteine residues arranged in three intramolecular disulfide bridges. Although their cysteine pattern is similar to cysteines-rich neurotoxic peptides of distantly related protostomes such as cone snails and arachnids, the different organization of the disulfide bridges observed in synthetic peptides and phylogenetic analyses revealed MgCRP-I as a novel protein family. Genome- and transcriptome-wide searches for orthologous sequences in other bivalve species indicated the unique presence of this gene family in Mytilus spp. Like many antimicrobial peptides and neurotoxins, MgCRP-I peptides are produced as pre-propeptides, usually have a net positive charge and likely derive from similar evolutionary mechanisms, that is, gene duplication and positive selection within the mature peptide region; however, synthetic MgCRP-I peptides did not display significant toxicity in cultured mammalian cells, insecticidal, antimicrobial, or antifungal activities. The functional role of MgCRP-I peptides in mussel physiology still remains puzzling.

A word of caution about biological inference - Revisiting cysteine covalent state predictions

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High prediction accuracy is often believed to validate implicit biological assumptions.

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Cys redox state predictions assume a local sequence environmental effect.

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Removing local sequence signals did not reduce prediction accuracy.

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Cys redox state predictions apparently correlate with subcellular localization.

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Subcellular localization depends on global sequence composition.

The success of methods for predicting the redox state of cysteine residues from the sequence environment seemed to validate the basic assumption that this state is mainly determined locally. However, the accuracy of predictions on randomized sequences or of non-cysteine residues remained high, suggesting that these predictions rather capture global features of proteins such as subcellular localization, which depends on composition. This illustrates that even high prediction accuracy is insufficient to validate implicit assumptions about a biological phenomenon. Correctly identifying the relevant underlying biochemical reasons for the success of a method is essential to gain proper biological insights and develop more accurate and novel bioinformatics tools.

Amino acid patterns around disulfide bonds

Disulfide bonds provide an inexhaustible source of information on molecular evolution and biological specificity. In this work, we described the amino acid composition around disulfide bonds in a set of disulfide-rich proteins using appropriate descriptors, based on ANOVA (for all twenty natural amino acids or classes of amino acids clustered according to their chemical similarities) and Scheffé (for the disulfide-rich proteins superfamilies) statistics. We found that weakly hydrophilic and aromatic amino acids are quite abundant in the regions around disulfide bonds, contrary to aliphatic and hydrophobic amino acids. The density distributions (as a function of the distance to the center of the disulfide bonds) for all defined entities presented an overall unimodal behavior: the densities are null at short distances, have maxima at intermediate distances and decrease for long distances. In the end, the amino acid environment around the disulfide bonds was found to be different for different superfamilies, allowing the clustering of proteins in a biologically relevant way, suggesting that this type of chemical information might be used as a tool to assess the relationship between very divergent sets of disulfide-rich proteins.

Prostate stem cell antigen: a Jekyll and Hyde molecule?

Prostate stem cell antigen (PSCA) is a glycosylphosphatidylinositol (GPI)-anchored cell surface protein. Although PSCA is thought to be involved in intracellular signaling, much remains unknown about its physiological function and regulatory mechanism in normal and cancer cells. It is up-regulated in several major cancers including prostate, bladder, and pancreatic cancers. The expression of PSCA is positively correlated with advanced clinical stage and metastasis in prostate cancers and is also associated with malignant progression of premalignant prostate lesions. Therefore, PSCA has been proposed as a biomarker of diagnosis and prognosis, as well as a target of therapy for these cancers. In addition, PSCA has also shown clinical potential in immunotherapy as a prostate-specific antigen, which, when presented by dendritic cells, may elicit strong tumor-specific immunity. In contrast, PSCA is down-regulated in esophageal and gastric cancer and may have a tumor-suppressing function in the gastric epithelium. Recent exciting findings that genetic variations of PSCA conferred increased risks of gastric cancer and bladder cancer have opened up a new avenue of research about the pathological function of PSCA. PSCA seems to be a Jekyll and Hyde molecule that plays differential roles, tumor promoting or suppressing, depending on the cellular context.

Predicting disulfide bond connectivity in proteins by correlated mutations analysis

MOTIVATION

Prediction of disulfide bond connectivity facilitates structural and functional annotation of proteins. Previous studies suggest that cysteines of a disulfide bond mutate in a correlated manner.

RESULTS

We developed a method that analyzes correlated mutation patterns in multiple sequence alignments in order to predict disulfide bond connectivity. Proteins with known experimental structures and varying numbers of disulfide bonds, and that spanned various evolutionary distances, were aligned. We observed frequent variation of disulfide bond connectivity within members of the same protein families, and it was also observed that in 99% of the cases, cysteine pairs forming non-conserved disulfide bonds mutated in concert. Our data support the notion that substitution of a cysteine in a disulfide bond prompts the substitution of its cysteine partner and that oxidized cysteines appear in pairs. The method we developed predicts disulfide bond connectivity patterns with accuracies of 73, 69 and 61% for proteins with two, three and four disulfide bonds, respectively.

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure

MOTIVATION

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications.

RESULTS

We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects.

AVAILABILITY

The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide

The disulfide bond pattern of salmon egg lectin 24K from the Chinook salmon Oncorhynchus tshawytscha

The disulfide bonds in the galactose-specific lectin SEL 24K from the egg of the Chinook salmon Oncorhynchus tshawytscha were determined by mass spectrometry. Four predictive in silico tools were used to determine the oxidation state of cysteines in the sequence and possible location of the disulfide bonds. A combination of tryptic digestion, HPLC separation, and chemical modifications were used to establish the location of seven disulfide bonds and one pair of free cysteines. After proteolysis, peptides containing one or two disulfide bonds were identified by reduction and mass spectral comparison. MALDI mass spectrometry was supported by chemical modification (iodoacetamide) and in silico digestion. The assignments of disulfide bonds were further confirmed by mass spectral fragmentation studies including in-source dissociation (ISD) and collision-induced dissociation (CID). The experimentally determined disulfide bonds and free Cys residues were only partially consistent with those generated by several automated public-domain algorithms.

Analysis of disulphide bond connectivity patterns in protein tertiary structure

The analysis of disulphide bond containing proteins in the Protein Data Bank (PDB) revealed that out of 27,209 protein structures analyzed, 12,832 proteins contain at least one intra-chain disulphide bond and 811 proteins contain at least one inter-chain disulphide bond. The intra-chain disulphide bond containing proteins can be grouped into 256 categories based on the number of disulphide bonds and the disulphide bond connectivity patterns (DBCPs) that were generated according to the position of half-cystine residues along the protein chain. The PDB entries corresponding to these 256 categories represent 509 unique SCOP superfamilies. A simple web-based computational tool is made freely available at the website that allows flexible queries to be made on the database in order to retrieve useful information on the disulphide bond containing proteins in the PDB. The database is useful to identify the different SCOP superfamilies associated with a particular disulphide bond connectivity pattern or vice versa. It is possible to define a query based either on a single field or a combination of the following fields, i.e., PDB code, protein name, SCOP superfamily name, number of disulphide bonds, disulphide bond connectivity pattern and the number of amino acid residues in a protein chain and retrieve information that match the criterion. Thereby, the database may be useful to select suitable protein structural templates in order to model the more distantly related protein homologs/analogs using the comparative modeling methods.

Structural classification of small, disulfide-rich protein domains

Disulfide-rich domains are small protein domains whose global folds are stabilized primarily by the formation of disulfide bonds and, to a much lesser extent, by secondary structure and hydrophobic interactions. Disulfide-rich domains perform a wide variety of roles functioning as growth factors, toxins, enzyme inhibitors, hormones, pheromones, allergens, etc. These domains are commonly found both as independent (single-domain) proteins and as domains within larger polypeptides. Here, we present a comprehensive structural classification of approximately 3000 small, disulfide-rich protein domains. We find that these domains can be arranged into 41 fold groups on the basis of structural similarity. Our fold groups, which describe broader structural relationships than existing groupings of these domains, bring together representatives with previously unacknowledged similarities; 18 of the 41 fold groups include domains from several SCOP folds. Within the fold groups, the domains are assembled into families of homologs. We define 98 families of disulfide-rich domains, some of which include newly detected homologs, particularly among knottin-like domains. On the basis of this classification, we have examined cases of convergent and divergent evolution of functions performed by disulfide-rich proteins. Disulfide bonding patterns in these domains are also evaluated. Reducible disulfide bonding patterns are much less frequent, while symmetric disulfide bonding patterns are more common than expected from random considerations. Examples of variations in disulfide bonding patterns found within families and fold groups are discussed.

Limited tendency of α-helical residues to form disulfide bridges: a structural explanation

Disulfide bridges have an enormous impact on the structure of a large number of proteins and polypeptides. Understanding the structural basis that regulates their formation may be important for the design of novel peptide-based molecules with a specific fold and stability. Here we report a statistical analysis of the relationships between secondary structure and disulfide bond formation, carried out using a large database of protein structures. Our analyses confirm the observation sporadically reported in previous investigations that cysteine residues located in alpha-helices display a limited tendency to form disulfide bridges. The very low occurrence of the disulfide bond in all alpha-chains compared to all beta-chains indicates that this property is also evident when proteins with different topologies are investigated. Taking advantage of the large database that endorsed the analysis on relatively rare motifs, we demonstrate that cysteine residues embedded in 3(10) helices present a good tendency to form disulfide bonds. This result is somewhat surprising since 3(10) helices are commonly assimilated into alpha-helices. A plausible structural explanation for the observed data has been derived combining analyses of disulfide bond sequence separation and of the length of the different secondary structure elements.

The two cysteine-rich head domains of minicollagen from Hydra nematocysts differ in their cystine framework and overall fold despite an identical cysteine sequence pattern

Synthetic replicates of naturally occurring cysteine-rich peptides such as hormones, neurotransmitters, growth factors, enzyme inhibitors, defensins and toxins often can be oxidatively folded in high yields to their native structure in simple redox buffers. Thereby, identical cysteine patterns in the sequence were found to generate identical disulfide connectivities and homologous spatial structures despite significant variability in the non-cysteine positions. Minicollagen-1 from the nematocysts of Hydra is a trimeric protein that contains cysteine-rich domains at the N and C termini, which are involved in the assembly of an intermolecular disulfide network. Determination of the three-dimensional structures of peptides corresponding to the N-terminal and C-terminal domains by NMR spectroscopy revealed a remarkable exception from the general rule. Despite an identical cysteine pattern, the two domains of minicollagen-1 form different disulfide bridges and exhibit distinctly different folds, both of which are not found in the current structural databases. To our knowledge, this is the first case where two relatively short peptides with the abundant cysteine residues in identical sequence positions fold uniquely and with high yields into defined, but differing, structures. Therefore, the cysteine-rich domains of minicollagen constitute ideal model systems for studies of the interplay between folding and oxidation in proteins.