NDLI: A recursive connectionist approach for predicting disulfide connectivity in proteins

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A recursive connectionist approach for predicting disulfide connectivity in proteins

Content Provider	ACM Digital Library
Author	Vullo, Alessandro Frasconi, Paolo
Abstract	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 Furthermore, we assess the generalization capabilities testing the model on previously unpublished data.
Starting Page	67
Ending Page	71
Page Count	5
File Format	PDF
ISBN	1581136242
DOI	10.1145/952532.952550
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-03-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Disulfide connectivity prediction Protein folding Recursive neural networks
Content Type	Text
Resource Type	Article

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