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Revealing the 3D structure of proteins
10/14/13

Calculating the probability of the formation of disulfide bridges

Small protein predictionCysteines are the only types of residues that can form bridges themselves, from one cysteine to another. “The three-dimensional structure of the protein will eventually be significantly constrained by these bridges. So, when we look for the cysteines that are connected to each other, we can already get a good idea of the structure of the protein, Becker said. The main thing is to find a means of predicting the formation of disulfide bridges between different cysteines present in a protein. “In this kind of learning, we try to represent every object, in this case the pairs of cysteines, as a vector of numerical values between zero and one. My idea attempts to use a series of ways of representing a pair of residues and then to see which of these is the most successful at predicting disulfide bridges”. Becker took into account criteria such as the length of the protein (that is, the number of residues), the number of cysteines, their relative position, and so on. Then he developed an algorithm that could select the best descriptions, up to the point where adding a description would not improve the predictive tool’s performance. “Before applying this algorithm, we tested three major families of learning machines in order to choose the one that works best for the application of our algorithm”.

Disordered regions of proteins: a new challenge...

The work of Julien Becker led to the creation of a predictive tool called x3CysBridges, that allows researchers to calculate the probability that the cysteines of a single protein will form disulfide bridges. The principle is simple: researchers using the tool enter a sequence of amino acids that they want to test and the predictor carries out the necessary calculations. Following this, the researchers are informed about the predictor’s answer, that is, which cysteines will form bridges, via e-mail. The results of this work have been published in the Journal PLoS ONE (1). “We have already had several requests from biologists who wanted to use this service, ” Becker said.

Today Becker is working on a new challenge: “I am in the process of repurposing this method to do automatic learning with regard to disordered regions, ” he said. Within a protein, there are regions called “ordered” whose three-dimensional structure is stable; but there are also disordered regions in which there is a great deal of variability. These protein segments do not adopt a stable structure, and in general they are used to “attract” metabolites within or outside a cell. “This is the same kind of study that we went through for disulfide bridges; I try to learn a representation in the form of numerical values for each one of the residues of a protein in order to construct a predictive tool for disordered regions”. Little by little, the performance of predictive tools is improved, and so this young scientist pursues the goal of a single automatic predictive tool that will allow him to predict the three-dimensional structure of proteins.

(1) Becker J, Maes F, Wehenkel L. On the relevance of sophisticated structural annotations for disulfide connectivity pattern prediction. PLoS One. 2013;8(2):e56621. doi: 10.1371/journal.pone.0056621. Epub 2013 Feb 15.

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