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

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

A recursive connectionist approach for predicting disulfide connectivity in proteins

We are interested in the prediction of disulfide bridges in proteins, a structural feature that conveys important information about the protein conformation and that can therefore help towards the solution of the folding problem. We assume here that the disulfide bonding state of cysteines is known and we focus on the subsequent problem of disulfide bridges pairings assignment. In this paper, disulfide connectivity is modeled by undirected graphs. A graphspace search algorithm is employed to explore alternative disulfide bridges patterns and prediction consists of selecting the ‘best ’ graph in the search space. The core of the proposed method is a recursive neural network architecture trained to score candidate graphs. We report experiments on previously published data showing that our algorithm outperforms the known alternative methods for most proteins. Furthermore, we assess the generalization capabilities testing the model on previously unpublished data

A Recursive Connectionist Approach for Predicting Disulfide Connectivity In Proteins

We are interested in the prediction of disulfide bridges in proteins, a structural feature that conveys important information about the protein conformation and that can therefore help towards the solution of the folding problem. We assume here that the disulfide bonding state of cysteines is known and we focus on the subsequent problem of disulfide bridges pairings assignment. In this paper, disulfide connectivity is modeled by undirected graphs. A graphspace search algorithm is employed to explore alternative disulfide bridges patterns and prediction consists of selecting the `best' graph in the search space. The core of the proposed method is a recursive neural network architecture trained to score candidate graphs. We report experiments on previously published data showing that our algorithm outperforms the known alternative methods for most proteins. Furthermore, we assess the generalization capabilities testing the model on previously unpublished data