DiANNA: a web server for disulfide connectivity prediction

Correctly predicting the disulfide bond topology in a protein is of crucial importance for the understanding of protein function and can be of great help for tertiary prediction methods. The web server outputs the disulfide connectivity prediction given input of a protein sequence. The following procedure is performed. First, PSIPRED is run to predict the protein's secondary structure, then PSIBLAST is run against the non-redundant SwissProt to obtain a multiple alignment of the input sequence. The predicted secondary structure and the profile arising from this alignment are used in the training phase of our neural network. Next, cysteine oxidation state is predicted, then each pair of cysteines in the protein sequence is assigned a likelihood of forming a disulfide bond—this is performed by means of a novel architecture (diresidue neural network). Finally, Rothberg's implementation of Gabow's maximum weighted matching algorithm is applied to diresidue neural network scores in order to produce the final connectivity prediction. Our novel neural network-based approach achieves results that are comparable and in some cases better than the current state-of-the-art methods

Prediction of Oxidation States of Cysteines and Disulphide Connectivity

Knowledge on cysteine oxidation state and disulfide bond connectivity is of great importance to protein chemistry and 3-D structures. This research is aimed at finding the most relevant features in prediction of cysteines oxidation states and the disulfide bonds connectivity of proteins. Models predicting the oxidation states of cysteines are developed with machine learning techniques such as Support Vector Machines (SVMs) and Associative Neural Networks (ASNNs). A record high prediction accuracy of oxidation state, 95%, is achieved by incorporating the oxidation states of N-terminus cysteines, flanking sequences of cysteines and global information on the protein chain (number of cysteines, length of the chain and amino acids composition of the chain etc.) into the SVM encoding. This is 5% higher than the current methods. This indicates to us that the oxidation states of amino terminal cysteines infer the oxidation states of other cysteines in the same protein chain. Satisfactory prediction results are also obtained with the newer and more inclusive SPX dataset, especially for chains with higher number of cysteines. Compared to literature methods, our approach is a one-step prediction system, which is easier to implement and use. A side by side comparison of SVM and ASNN is conducted. Results indicated that SVM outperform ASNN on this particular problem. For the prediction of correct pairings of cysteines to form disulfide bonds, we first study disulfide connectivity by calculating the local interaction potentials between the flanking sequences of the cysteine pairs. The obtained interaction potential is further adjusted by the coefficients related to the binding motif of enzymes during disulfide formation and also by the linear distance between the cysteine pairs. Finally, maximized weight matching algorithm is applied and performance of the interaction potentials evaluated. Overall prediction accuracy is unsatisfactory compared with the literature. SVM is used to predict the disulfide connectivity with the assumption that oxidation states of cysteines on the protein are known. Information on binding region during disulfide formation, distance between cysteine pairs, global information of the protein chain and the flanking sequences around the cysteine pairs are included in the SVM encoding. Prediction results illustrate the advantage of using possible anchor region information

DISULFIND: a disulfide bonding state and cysteine connectivity prediction server

DISULFIND is a server for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone. Optionally, disulfide connectivity can be predicted from sequence and a bonding state assignment given as input. The output is a simple visualization of the assigned bonding state (with confidence degrees) and the most likely connectivity patterns. The server is available at

DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines

The proper prediction of the location of disulfide bridges is efficient in helping to solve the protein folding problem. Most of the previous works on the prediction of disulfide connectivity pattern use the prior knowledge of the bonding state of cysteines. The DBCP web server provides prediction of disulfide bonding connectivity pattern without the prior knowledge of the bonding state of cysteines. The method used in this server improves the accuracy of disulfide connectivity pattern prediction (Qp) over the previous studies reported in the literature. This DBCP server can be accessed at http://120.107.8.16/dbcp or http://140.120.14.136/dbcp

DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification

DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architecture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine classification problem—to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case, DiANNA predicts the ligand among iron, zinc, cadmium and carbon. Available at:

A simplified approach to disulfide connectivity prediction from protein sequences

<p>Abstract</p> <p>Background</p> <p>Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on different machine learning algorithms have been applied to solve this problem and public domain prediction services exist. These methods are however still potentially subject to significant improvements both in terms of prediction accuracy and overall architectural complexity.</p> <p>Results</p> <p>We introduce new methods for predicting disulfide bridges from protein sequences. The methods take advantage of two new decomposition kernels for measuring the similarity between protein sequences according to the amino acid environments around cysteines. Disulfide connectivity is predicted in two passes. First, a binary classifier is trained to predict whether a given protein chain has at least one intra-chain disulfide bridge. Second, a multiclass classifier (plemented by 1-nearest neighbor) is trained to predict connectivity patterns. The two passes can be easily cascaded to obtain connectivity prediction from sequence alone. We report an extensive experimental comparison on several data sets that have been previously employed in the literature to assess the accuracy of cysteine bonding state and disulfide connectivity predictors.</p> <p>Conclusion</p> <p>We reach state-of-the-art results on bonding state prediction with a simple method that classifies chains rather than individual residues. The prediction accuracy reached by our connectivity prediction method compares favorably with respect to all but the most complex other approaches. On the other hand, our method does not need any model selection or hyperparameter tuning, a property that makes it less prone to overfitting and prediction accuracy overestimation.</p

A Protocol to Detect Local Affinities Involved in Proteins Distant Interactions

The tridimensional structure of a protein is constrained or stabilized by some local interactions between distant residues of the protein, such as disulfide bonds, electrostatic interactions, hydrogen links, Wan Der Waals forces, etc. The correct prediction of such contacts should be an important step towards the whole challenge of tridimensional structure prediction. The in silico prediction of the disulfide connectivity has been widely studied: most results were based on few amino-acids around bonded and non-bonded cysteines, which we call local environments of bonded residues. In order to evaluate the impact of such local information onto residue pairing, we propose a machine learning based protocol, independent from the type of contact, to detect affinities between local environments which would contribute to residues pairing. This protocol requires that learning methods are able to learn from examples corrupted by class-conditional classification noise. To this end, we propose an adapted version of the perceptron algorithm. Finally, we experiment our protocol with this algorithm on proteins that feature disulfide or salt bridges. The results show that local environments contribute to the formation of salt bridges. As a by-product, these results prove the relevance of our protocol. However, results on disulfide bridges are not significantly positive. There can be two explanations: the class of linear functions used by the perceptron algorithm is not enough expressive to detect this information, or cysteines local environments do not contribute significantly to residues pairing

Purification and characterization of DR_2577 (SlpA) a major S-layer protein from Deinococcus radiodurans

The protein DR_2577 is a major Surface layer component of the radio-resistant bacterium Deinococcus radiodurans. In the present study DR_2577 has been purified and its oligomeric profile characterized by means of size exclusion chromatography and gel electrophoresis. DR_2577 was found to be organized into three hierarchical orders characterized by monomers, stable dimers formed by the occurrence of disulfide bonds, and hexamers resulting from a combination of dimers. The structural implications of these findings are discussed providing new elements for a more integrated model of this S-layer

In silico analysis of lipopolysaccharide and β-1, 3-glucan binding protein (LGBP) gene from the haemocytes of Indian white shrimp Fenneropenaeus indicus

Lipopolysaccharide and β-1,3-glucan binding protein (LGBP) gene are involved in the pattern recognition mechanism of invertebrates, it induces the cell and humoral mediated immune responses like encapsulation, phagocytosis, nodule formation, clotting, synthesis of antimicrobial peptides and activation of the prophenoloxidase (proPO) system. The current study focuses to model the three-dimensional structure of novel immune related gene LGBP from the Indian white shrimp Fenneropeneaus indicus (F.indicus) by in silico homology modeling and its motif prediction. Fenneropeneaus indicus lipopolysaccharide and β-1,3-glucan binding protein (Fein-LGBP) consists of glycosylated regions which come under the glucanase family. Two conserved putative integrin-binding motif (cell adhesion sites), bacterial glucanase motif (GM) and two polysaccharide recognition motifs for the polysaccharide binding motif (PsBM) and β- glucan recognition motif (β-GRM) were conserved in the novel sequences of Fein-LGBP. Prediction of motifs, patterns, disulfide bridges and secondary structure were performed for functional characterization of the Fein-LGBP. Three dimensional structure of the Fein-LGBP was generated by Modeller9V8, Swiss Model and validated using NIH server. Results revealed that the modelled structure of Fein-LGBP was 75.7% of residues in allowed region. Theoretical model of Fein- LGBP facilitates to the discovery of new synthetic immune related peptides, agonists that could be useful to  understand the mechanism of LGBP involvement in the prophenoloxidase activating system of crustaceans. The tertiary structure prediction of the immune related gene Fein- LGBP will assist to explore more knowledge in immune system of crustaceans

Analysis on conservation of disulphide bonds and their structural features in homologous protein domain families

International audienceBackground: Disulphide bridges are well known to play key roles in stability, folding and functions of proteins. Introduction or deletion of disulphides by site-directed mutagenesis have produced varying effects on stability and folding depending upon the protein and location of disulphide in the 3-D structure. Given the lack of complete understanding it is worthwhile to learn from an analysis of extent of conservation of disulphides in homologous proteins. We have also addressed the question of what structural interactions replaces a disulphide in a homologue in another homologue.Results: Using a dataset involving 34,752 pairwise comparisons of homologous protein domains corresponding to 300 protein domain families of known 3-D structures, we provide a comprehensive analysis of extent of conservation of disulphide bridges and their structural features. We report that only 54% of all the disulphide bonds compared between the homologous pairs are conserved, even if, a small fraction of the non-conserved disulphides do include cytoplasmic proteins. Also, only about one fourth of the distinct disulphides are conserved in all the members in protein families. We note that while conservation of disulphide is common in many families, disulphide bond mutations are quite prevalent. Interestingly, we note that there is no clear relationship between sequence identity between two homologous proteins and disulphide bond conservation. Our analysis on structural features at the sites where cysteines forming disulphide in one homologue are replaced by non-Cys residues show that the elimination of a disulphide in a homologue need not always result in stabilizing interactions between equivalent residues.Conclusion: We observe that in the homologous proteins, disulphide bonds are conserved only to a modest extent. Very interestingly, we note that extent of conservation of disulphide in homologous proteins is unrelated to the overall sequence identity between homologues. The non-conserved disulphides are often associated with variable structural features that were recruited to be associated with differentiation or specialisation of protein function