title: Disulfide connectivity prediction using recursive neural networks and evolutionary information.
abstract: BACKGROUND
  We focus on the prediction of disulfide bridges in proteins starting from their amino acid sequence and from the knowledge of the disulfide bonding state of each cysteine. The location of disulfide bridges is a structural feature that conveys important information about the protein main chain conformation and can therefore help towards the solution of the folding problem. Existing approaches based on weighted graph matching algorithms do not take advantage of evolutionary information. Recursive neural networks (RNN), on the other hand, can handle in a natural way complex data structures such as graphs whose vertices are labeled by real vectors, allowing us to incorporate multiple alignment profiles in the graphical representation of disulfide connectivity patterns.
  
  
  RESULTS
  The core of the method is the use of machine learning tools to rank alternative disulfide connectivity patterns. We develop an ad-hoc RNN architecture for scoring labeled undirected graphs that represent connectivity patterns. In order to compare our algorithm with previous methods, we report experimental results on the SWISS-PROT 39 dataset. We find that using multiple alignment profiles allows us to obtain significant prediction accuracy improvements, clearly demonstrating the important role played by evolutionary information.
  
  
  BACKGROUND
  The Web interface of the predictor is available at http://neural.dsi.unifi.it/cysteines
authors.affiliation: Department of Systems and Computer Science, Università di Firenze Via di S. Marta 3, 50139-I Firenze, Italy. vullo@dsi.unifi.it
authors.forename: Alessandro
authors.initials: A
authors.lastname: Vullo
authors.forename: Paolo
authors.initials: P
authors.lastname: Frasconi
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites; D003545:Cysteine / Q000737:chemistry / Q000235:genetics; D004220:Disulfides / Q000737:chemistry*; D019143:Evolution, Molecular; D020869:Gene Expression Profiling / Q000379:methods*; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D004220:Disulfides; D011506:Proteins; D003545:Cysteine