Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant

The conotoxin proteins are disulfide rich small peptides that target ion channels and G protein coupled receptors. And they provide promising application in treating some chronic pain, epilepsy, cardiovascular diseases, and so on. Conotoxins may be classified into 11 superfamilies: A, D, I1, I2, J, L, M, O, P, S, and T according to the disulfide connectivity, highly conserved N-terminal precursor sequence and similar mode of actions. Successful prediction mature conotoxin superfamily peptide has important signification for the biological and pharmacological functions of the toxins. In this study, a new algorithm of increment of diversity combined with modified Mahalanobis discriminant is presented to predict five superfamilies by using the pseudo amino acid composition. The results of jackknife cross-validation test show that the overall prediction sensitivity and specificity are 88% and 91%, respectively. The predictive algorithm is also used to predict three O-conotoxin families. The 72% sensitivity and 78% specificity are obtained. These results indicate that the conotoxin superfamily peptides correlate with their amino acid compositions.     

Conotoxin GI: disulfide bridges, synthesis, and preparation of iodinated derivatives

The 13 amino acid toxic peptide from the marine snail Conus geographus, conotoxin GI, blocks the acetylcholine receptor at the neuromuscular junction. In this report, we describe a method for analyzing disulfide bonding in nanomole amounts of small cystine-rich peptides. The procedure involves partial reduction and a double-label alkylation of cysteine residues. Using this method, we show that the natural conotoxin GI has a (2-7, 3-13) disulfide configuration. The structure of conotoxin GI has been confirmed by chemical synthesis. The preparation and purification of molecularly homogeneous, iodinated derivatives of this toxin are also described. All derivatives, including the [diiodohistidine,diiodotyrosine]conotoxin GI, retained at least half of the biological activity of unmodified toxin. Since the tetraiodinated toxin, which is greater than 25% by weight iodine, retains considerable toxicity, unmodified histidine and tyrosine residues in conotoxin GI are not crucial for biological activity.     

The A-superfamily of conotoxins: structural and functional divergence

The generation of functional novelty in proteins encoded by a gene superfamily is seldom well documented. In this report, we define the A-conotoxin superfamily, which is widely expressed in venoms of the predatory cone snails (Conus), and show how gene products that diverge considerably in structure and function have arisen within the same superfamily. A cDNA clone encoding alpha-conotoxin GI, the first conotoxin characterized, provided initial data that identified the A-superfamily. Conotoxin precursors in the A-superfamily were identified from six Conus species: most (11/16) encoded alpha-conotoxins, but some (5/16) belong to a family of excitatory peptides, the kappaA-conotoxins that target voltage-gated ion channels. alpha-Conotoxins are two-disulfide-bridged nicotinic antagonists, 13-19 amino acids in length; kappaA-conotoxins are larger (31-36 amino acids) with three disulfide bridges. Purification and biochemical characterization of one peptide, kappaA-conotoxin MIVA is reported; five of the other predicted conotoxins were previously venom-purified. A comparative analysis of conotoxins purified from venom, and their precursors reveal novel post-translational processing, as well as mutational events leading to polymorphism. Patterns of sequence divergence and Cys codon usage define the major superfamily branches and suggest how these separate branches arose.     

Definition of the M-conotoxin superfamily: characterization of novel peptides from molluscivorous Conus venoms

Most of the >50,000 different pharmacologically active peptides in Conus venoms belong to a small number of gene superfamilies. In this work, the M-conotoxin superfamily is defined using both biochemical and molecular criteria. Novel excitatory peptides purified from the venoms of the molluscivorous species Conus textile and Conus marmoreus all have a characteristic pattern of Cys residues previously found in the mu-, kappaM-, and psi-conotoxins (CC-C-C-CC). The new peptides are smaller (12-19 amino acids) than the mu-, kappaM-, and psi-conotoxins (22-24 amino acids). One peptide, mr3a, was chemically synthesized in a biologically active form. Analysis of the disulfide bridges of a natural peptide tx3c from C. textile and synthetic peptide mr3a from C. marmoreus showed a novel pattern of disulfide connectivity, different from that previously established for the mu- and psi-conotoxins. Thus, these peptides belong to a new group of structurally and pharmacologically distinct conotoxins that are particularly prominent in the venoms of mollusc-hunting Conus species. Analysis of cDNA clones encoding the novel peptides as well as those encoding mu-, kappaM-, and psi-conotoxins revealed highly conserved amino acid residues in the precursor sequences; this conservation in both amino acid sequence and in the Cys pattern defines a gene superfamily, designated the M-conotoxin superfamily. The peptides characterized can be provisionally assigned to four distinct groups within the M-superfamily based on sequence similarity within and divergence between each group. A notable feature of the superfamily is that two distinct structural frameworks have been generated by changing the disulfide connectivity on an otherwise conserved Cys pattern.     

Remote protein homology detection using recurrence quantification analysis and amino acid physicochemical properties

Remote homology detection refers to the detection of structure homology in evolutionarily related proteins with low sequence similarity. Supervised learning algorithms such as support vector machine (SVM) are currently the most accurate methods. In most of these SVM-based methods, efforts have been dedicated to developing new kernels to better use the pairwise alignment scores or sequence profiles. Moreover, amino acids’ physicochemical properties are not generally used in the feature representation of protein sequences. In this article, we present a remote homology detection method that incorporates two novel features: (1) a protein's primary sequence is represented using amino acid's physicochemical properties and (2) the similarity between two proteins is measured using recurrence quantification analysis (RQA). An optimization scheme was developed to select different amino acid indices (up to 10 for a protein family) that are best to characterize the given protein family. The selected amino acid indices may enable us to draw better biological explanation of the protein family classification problem than using other alignment-based methods. An SVM-based classifier will then work on the space described by the RQA metrics. The classification scheme is named as SVM-RQA. Experiments at the superfamily level of the SCOP1.53 dataset show that, without using alignment or sequence profile information, the features generated from amino acid indices are able to produce results that are comparable to those obtained by the published state-of-the-art SVM kernels. In the future, better prediction accuracies can be expected by combining the alignment-based features with our amino acids property-based features. Supplementary information including the raw dataset, the best-performing amino acid indices for each protein family and the computed RQA metrics for all protein sequences can be downloaded from http://ym151113.ym.edu.tw/svm-rqa.     

Amino acids/peptides conjugated heterocycles: A tool for the recent development of novel therapeutic agents

Amino acids/peptide conjugated heterocycles represent an important class of therapeutical agents. Biologically active heterocycles are conjugated with amino acids or peptides to increase the drug resistance. Furthermore, the amino acid/peptide based drugs have low toxicity, ample bioavailability and permeability, modest potency and good metabolic and pharmacokinetic properties. Synthetic amino acid/peptides based heterocyclic conjugates constitute a promising choice for the development of new, less toxic and safer conventional pharmaceutical drugs in the near future. In this review, we discuss and highlight the recent findings of the structural features that encourage biological applications of amino acid/peptides based conjugates.     

Alpha S-conotoxin RVIIIA: a structurally unique conotoxin that broadly targets nicotinic acetylcholine receptors

We report the purification and characterization of a new conotoxin from the venom of Conus radiatus. The peptide, alphaS-conotoxin RVIIIA (alphaS-RVIIIA), is biochemically unique with respect to its amino acid sequence, post-translational modification, and molecular targets. In comparison to other nicotinic antagonists from Conus venoms, alphaS-RVIIIA exhibits an unusually broad targeting specificity for nicotinic acetylcholine receptor (nAChR) subtypes, as assayed by electrophysiology. The toxin is paralytic to mice and fish, consistent with its nearly irreversible block of the neuromuscular nAChR. Similar to other antagonists of certain neuronal nAChRs, the toxin also elicits seizures in mice upon intracranial injection. The only previously characterized conotoxin from the S superfamily, sigma-conotoxin GVIIIA, is a specific competitive antagonist of the 5-HT3 receptor; thus, alphaS-RVIIIA defines a novel family of nicotinic antagonists within the S superfamily. All previously characterized competitive conotoxin nAChR antagonists have been members of the A superfamily of conotoxins. Our working hypothesis is that the particular group of fish-hunting Conus species that includes Conus radiatus uses the alphaS-conotoxin family to target the muscle nAChR and paralyze prey.     

A Time-Series-Based Feature Extraction Approach for Prediction of Protein Structural Class

This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a wavelet-based time-series technique is proposed for extracting features from mapped amino acid sequence and a fixed length feature vector for classification is constructed. The proposed feature space summarizes the variance information of ten different biological properties of amino acids. Finally, an optimized support vector machine model is constructed for prediction of each protein structural class. The proposed approach is evaluated using leave-one-out cross-validation tests on two standard datasets. Comparison of our result with existing approaches shows that overall accuracy achieved by our approach is better than exiting methods.     

Solution conformation of conotoxin GI determined by 1H nuclear magnetic resonance spectroscopy and distance geometry calculations

Conformational analysis of conotoxin GI, one of the neurotoxic peptides produced by a marine snail, genus Conus, was performed by a combination of nuclear magnetic resonance spectroscopy (NMR) and distance geometry calculations. The resulting conformers on minimization of the target function were classified into two groups. The difference in the structures of the conformers is mainly due to the difference in the orientation of the side chain of the tyrosyl residue. The results show that the solution structure of conotoxin GI satisfies the conformational requirements for the biological activity of an antagonist toward nicotinic cholinergic receptors elucidated in a series of studies on alkaloids. The structure is discussed on the basis of the results of comparison of the atomic arrangements of the active sites of snake venom peptides and molecular models based on the results of secondary structure prediction.     

A novel conotoxin inhibitor of Kv1.6 channel and nAChR subtypes defines a new superfamily of conotoxins

Using assay-directed fractionation of the venom from the vermivorous cone snail Conus planorbis, we isolated a new conotoxin, designated pl14a, with potent activity at both nicotinic acetylcholine receptors and a voltage-gated potassium channel subtype. pl14a contains 25 amino acid residues with an amidated C-terminus, an elongated N-terminal tail (six residues), and two disulfide bonds (1-3, 2-4 connectivity) in a novel framework distinct from other conotoxins. The peptide was chemically synthesized, and its three-dimensional structure was demonstrated to be well-defined, with an alpha-helix and two 3(10)-helices present. Analysis of a cDNA clone encoding the prepropeptide precursor of pl14a revealed a novel signal sequence, indicating that pl14a belongs to a new gene superfamily, the J-conotoxin superfamily. Five additional peptides in the J-superfamily were identified. Intracranial injection of pl14a in mice elicited excitatory symptoms that included shaking, rapid circling, barrel rolling, and seizures. Using the oocyte heterologous expression system, pl14a was shown to inhibit both a K+ channel subtype (Kv1.6, IC50 = 1.59 microM) and neuronal (IC50 = 8.7 microM for alpha3beta4) and neuromuscular (IC50 = 0.54 microM for alpha1beta1 epsilondelta) subtypes of the nicotinic acetylcholine receptor (nAChR). Similarities in sequence and structure are apparent between the middle loop of pl14a and the second loop of a number of alpha-conotoxins. This is the first conotoxin shown to affect the activity of both voltage-gated and ligand-gated ion channels.     

Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets.     

Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets.     

Statistical potential-based amino acid similarity matrices for aligning distantly related protein sequences

Aligning distantly related protein sequences is a long-standing problem in bioinformatics, and a key for successful protein structure prediction. Its importance is increasing recently in the context of structural genomics projects because more and more experimentally solved structures are available as templates for protein structure modeling. Toward this end, recent structure prediction methods employ profile-profile alignments, and various ways of aligning two profiles have been developed. More fundamentally, a better amino acid similarity matrix can improve a profile itself; thereby resulting in more accurate profile-profile alignments. Here we have developed novel amino acid similarity matrices from knowledge-based amino acid contact potentials. Contact potentials are used because the contact propensity to the other amino acids would be one of the most conserved features of each position of a protein structure. The derived amino acid similarity matrices are tested on benchmark alignments at three different levels, namely, the family, the superfamily, and the fold level. Compared to BLOSUM45 and the other existing matrices, the contact potential-based matrices perform comparably in the family level alignments, but clearly outperform in the fold level alignments. The contact potential-based matrices perform even better when suboptimal alignments are considered. Comparing the matrices themselves with each other revealed that the contact potential-based matrices are very different from BLOSUM45 and the other matrices, indicating that they are located in a different basin in the amino acid similarity matrix space.     

Identification and characterization of a novel O‐superfamily conotoxin from Conus litteratus

A novel conotoxin named lt6c, an O‐superfamily conotoxin, was identified from the cDNA library of venom duct of Conus litteratus. The full‐length cDNA contains an open reading frame encoding a predicted 22‐residue signal peptide, a 22‐residue proregion and a mature peptide of 28 amino acids. The signal peptide sequence of lt6c is highly conserved in O‐superfamily conotoxins and the mature peptide consists of six cysteines arranged in the pattern of C? C? CC? C? C that is defined the O‐superfamily of conotoxins. The mature peptide fused with thioredoxin, 6‐His tag, and a Factor Xa cleavage site was successfully expressed in Escherichia coli. About 12 mg lt6c was purified from 1L culture. Under whole‐cell patch‐clamp mode, lt6c inhibited sodium currents on adult rat dorsal root ganglion neurons. Therefore, lt6c is a novel O‐superfamily conotoxin that is able to block sodium channels. Copyright © 2008 European Peptide Society and John Wiley & Sons, Ltd.     

Novel excitatory Conus peptides define a new conotoxin superfamily

A new class of Conus peptides, the I-superfamily of conotoxins, has been characterized using biochemical, electrophysiological and molecular genetic methods. Peptides in this superfamily have a novel pattern of eight Cys residues. Five peptides that elicited excitatory symptomatology, r11a, r11b, r11c, r11d and r11e, were purified from Conus radiatus venom; four were tested on amphibian peripheral axons and shown to elicit repetitive action potentials, consistent with being members of the 'lightning-strike cabal' of toxins that effect instant immobilization of fish prey. A parallel analysis of Conus cDNA clones revealed a new class of conotoxin genes that was particularly enriched (with 18 identified paralogues) in a Conus radiatus venom duct library; several C. radiatus clones encoded the excitatory peptides directly characterized from venom. The remarkable diversity of related I-superfamily peptides within a single Conus species is unprecedented. When combined with the excitatory effects observed on peripheral circuitry, this unexpected diversity suggests a corresponding molecular complexity of the targeted signaling components in peripheral axons; the I-conotoxin superfamily should provide a rich lode of pharmacological tools for dissecting and understanding these. Thus, the I-superfamily conotoxins promise to provide a significant new technology platform for dissecting the molecular components of axons.     

Conotoxin TVIIA, a novel peptide from the venom of Conus tulipa 1. Isolation, characterization and chemical synthesis.

A novel conotoxin belonging to the 'four-loop' structural class has been isolated from the venom of the piscivorous cone snail Conus tulipa. It was identified using a chemical-directed strategy based largely on mass spectrometric techniques. The new toxin, conotoxin TVIIA, consists of 30 amino-acid residues and contains three disulfide bonds. The amino-acid sequence was determined by Edman analysis as SCSGRDSRCOOVCCMGLMCSRGKCVSIYGE where O = 4-transL-hydroxyproline. Two under-hydroxylated analogues, [Pro10]TVIIA and [Pro10,11]TVIIA, were also identified in the venom of C. tulipa. The sequences of TVIIA and [Pro10]TVIIA were further verified by chemical synthesis and coelution studies with native material. Conotoxin TVIIA has a six cysteine/four-loop structural framework common to many peptides from Conus venoms including the omega-, delta- and kappa-conotoxins. However, TVIIA displays little sequence homology with these well-characterized pharmacological classes of peptides, but displays striking sequence homology with conotoxin GS, a peptide from Conus geographus that blocks skeletal muscle sodium channels. These new toxins and GS share several biochemical features and represent a distinct subgroup of the four-loop conotoxins.     

Characterization of D-amino-acid-containing excitatory conotoxins and redefinition of the I-conotoxin superfamily

Post-translational isomerization of l-amino acids to d-amino acids is a subtle modification, not detectable by standard techniques such as Edman sequencing or MS. Accurate predictions require more sequences of modified polypeptides. A 46-amino-acid-long conotoxin, r11a, belonging to the I-superfamily was previously shown to have a d-Phe residue at position 44. In this report, we characterize two related peptides, r11b and r11c, with d-Phe and d-Leu, respectively, at the homologous position. Electrophysiological tests show that all three peptides induce repetitive activity in frog motor nerve, and epimerization of the single amino acid at the third position from the C-terminus attenuates the potency of r11a and r11b, but not that of r11c. Furthermore, r11c (but neither r11a nor r11b) also acts on skeletal muscle. We identified more cDNA clones encoding conopeptide precursors with Cys patterns similar to r11a/b/c. Although the predicted mature toxins have the same cysteine patterns, they belong to two different gene superfamilies. A potential correlation between the identity of the gene superfamily to which the I-conotoxin belongs and the presence or absence of a d-amino acid in the primary sequence is discussed. The great diversity of I-conopeptide sequences provides a rare opportunity for defining parameters that may be important for this most stealthy of all post-translational modifications. Our results indicate that neither the chemical nature of the side chain nor the precise vicinal sequence around the modified residue seem to be critical, but there may be favored loci for isomerization to a d-amino acid.     

Protein location prediction using atomic composition and global features of the amino acid sequence

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectively used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.     

Calcium binding properties of gamma-crystallin: calcium ion binds at the Greek key beta gamma-crystallin fold

The beta- and gamma-crystallins are closely related lens proteins that are members of the betagamma-crystallin superfamily, which also include many non-lens members. Although beta-crystallin is known to be a calcium-binding protein, this property has not been reported in gamma-crystallin. We have studied the calcium binding properties of gamma-crystallin, and we show that it binds 4 mol eq of calcium with a dissociation constant of 90 microm. It also binds the calcium-mimic spectral probes, terbium and Stains-all. Calcium binding does not significantly influence protein secondary and tertiary structures. We present evidence that the Greek key crystallin fold is the site for calcium ion binding in gamma-crystallin. Peptides corresponding to Greek key motif of gamma-crystallin (42 residues) and their mutants were synthesized and studied for calcium binding. These peptides adopt beta-sheet conformation and form aggregates producing beta-sandwich. Our results with peptides show that, in Greek key motif, the amino acid adjacent to the conserved aromatic corner in the "a" strand and three amino acids of the "d" strand participate in calcium binding. We suggest that the betagamma superfamily represents a novel class of calcium-binding proteins with the Greek key betagamma-crystallin fold as potential calcium-binding sites. These results are of significance in understanding the mechanism of calcium homeostasis in the lens.