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

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

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

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

Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines

Predicting the disulfide bonding state of each cysteine is a step towards location of disulphide bridges in proteins. The solution proposed here is a refinement of a recently introduced method based on a two-stage combination of multiple classifiers. In our approach, the first stage operates at the protein level and attempts to predict whether all, none, or some of the cysteines in the sequence are oxidized. The second stage is a binary classifier that refines the previous prediction by exploiting local context (a fixed-size window of residues flanking the target cysteine). The main contribution of this paper is the (combined) use of a spectrum kernel and the introduction of evolutionary information in the first stage of the system. We show that both implements allow to improve prediction accuracy. Our best system correctly classifies 85% cysteines as measured by 5-fold cross validation, on a set of 716 proteins from the September 2001 PDB Select dataset

Improving Structural Features Prediction in Protein Structure Modeling

Proteins play a vital role in the biological activities of all living species. In nature, a protein folds into a specific and energetically favorable three-dimensional structure which is critical to its biological function. Hence, there has been a great effort by researchers in both experimentally determining and computationally predicting the structures of proteins. The current experimental methods of protein structure determination are complicated, time-consuming, and expensive. On the other hand, the sequencing of proteins is fast, simple, and relatively less expensive. Thus, the gap between the number of known sequences and the determined structures is growing, and is expected to keep expanding. In contrast, computational approaches that can generate three-dimensional protein models with high resolution are attractive, due to their broad economic and scientific impacts. Accurately predicting protein structural features, such as secondary structures, disulfide bonds, and solvent accessibility is a critical intermediate step stone to obtain correct three-dimensional models ultimately. In this dissertation, we report a set of approaches for improving the accuracy of structural features prediction in protein structure modeling. First of all, we derive a statistical model to generate context-based scores characterizing the favorability of segments of residues in adopting certain structural features. Then, together with other information such as evolutionary and sequence information, we incorporate the context-based scores in machine learning approaches to predict secondary structures, disulfide bonds, and solvent accessibility. Furthermore, we take advantage of the emerging high performance computing architectures in GPU to accelerate the calculation of pairwise and high-order interactions in context-based scores. Finally, we make these prediction methods available to the public via web services and software packages

On the structure differences of short fragments and amino acids in proteins with and without disulfide bonds

Of the 20 standard amino acids, cysteines are the only amino acids that have a reactive sulphur atom, thus enabling two cysteines to form strong covalent bonds known as disulfide bonds. Even though almost all proteins have cysteines, not all of them have disulfide bonds. Disulfide bonds provide structural stability to proteins and hence are an important constraint in determining the structure of a protein. As a result, disulfide bonds are used to study various protein properties, one of them being protein folding. Protein structure prediction is the problem of predicting the three-dimensional structure of a protein from its one-dimensional amino acid sequence. Ab initio methods are a group of methods that attempt to solve this problem from first principles, using only basic physico-chemical properties of proteins. These methods use structure libraries of short amino acid fragments in the process of predicting the structure of a protein. The protein structures from which these structure libraries are created are not classified in any other way apart from being non-redundant. In this thesis, we investigate the structural dissimilarities of short amino acid fragments when occurring in proteins with disulfide bonds and when occurring in those proteins without disulfide bonds. We are interested in this because, as mentioned earlier, the protein structures from which the structure libraries of ab initio methods are created, are not classified in any form. This means that any significant structural difference in amino acids and short fragments when occurring in proteins with and without disulfide bonds would remain unnoticed as these structure libraries have both fragments from proteins with disulfide bonds and without disulfide bonds together. Our investigation of structural dissimilarities of amino acids and short fragments is done in four phases. In phase one, by statistically analysing the phi and psi backbone dihedral angle distributions we show that these fragments have significantly different structures in terms of dihedral angles when occurring in proteins with and without disulfide bonds. In phase two, using directional statistics we investigate how structurally different are the 20 different amino acids and the short fragments when occurring in proteins with and without disulfide bonds. In phase three of our work, we investigate the differences in secondary structure preference of the 20 amino acids in proteins with and without disulfide bonds. In phase four, we further investigate and show that there are significant differences within the same secondary structure region of amino acids when they occur in proteins with and without disulfide bonds. Finally, we present the design and implementation details of a dihedral angle and secondary structure database of short amino acid fragments (DASSD) that is publicly available. Thus, in this thesis we show previously unknown significant structure differences in terms of backbone dihedral angles and secondary structures in amino acids and short fragments when they occur in proteins with and without disulfide bonds

Characterization of antimicrobial peptides deriving from insects and their application in the biomedical field

Antibiotics are the current drugs used to treat pathogenic bacteria, but their prolonged use contributes to the development and spread of drug-resistant microorganisms. The antibiotic resistance issue led to the need to find new alternative molecules, which should be less prone to bacterial resistance. Antimicrobial peptides (AMPs) aroused great interest as potential next-generation antibiotics. AMPs are involved in several defence-related processes such as the binding and neutralization of endotoxins, the modulation of the immune responses to infection and the killing of pathogens. Antimicrobial peptides are small molecules with an amino acid composition ranging from 10 to 100 residues and are biosynthesized by all living organisms but it is known that the class of insects represents the largest source of these molecules. This aspect is related to insect’s biodiversity and their ability to live in hostile environments rich of pathogens. Most insect AMPs are cationic molecules due to the presence of basic residues and according to their amino acid sequences and structures, they can be classified in four different groups: cysteine-rich peptides (e.g., defensins), the α-helical peptides (e.g., cecropins), glycine-rich proteins (e.g., attacins) and proline-rich peptides (e.g., drosocins). Insect AMPs have demonstrated to be useful in several applications concerning the pharmaceutical as well as the agricultural fields. Moreover, insect AMPs aroused great interest for their biomedical application thanks to the increasing number of peptides that can inhibit human pathogens. For this reason, this Ph.D. project aimed to the identification of antimicrobial peptides deriving from insects, particularly from the Black Soldier Fly Hermetia illucens (L.) (Diptera: Stratiomyidae). Through a combination of transcriptomics and bioinformatics analysis, 57 antimicrobial peptides have been identified from H. illucens insect. Through an in silico analysis, the biological activity have been predicted and the physio-chemical properties have been calculated for all the identified peptides. Based on the bioinformatics results, the in vitro production of the most promising sequences has been performed through molecular cloning strategies in order to evaluate the antibacterial activity in vitro. Particularly, some of the identified peptides (C16571, C46948, C16634, and C7985) showed the ability to inhibit E. coli growth at a concentration value of 3 μM. For the C15867 peptide, recombinantly produced and expressed, a MIC (Minimum Inhibitory Concentration) value of 18 μM has been determined. Moreover, an in vivo approach was carried out for the identification of antimicrobial peptides by extracting the hemolymph from the H. illucens larvae, recovering then the peptides fraction from the larvae’s plasma and its antibacterial activity has been evaluated against both Gram-positive and Gram-negative bacteria. The performed analysis showed that a small amount (7.5/15 μL) of the peptide fraction recovered from the larvae’s plasma was able to inhibit the cell growth of different bacterial strains