Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)

Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation conditional random fields (SCRFs), is proposed as an effective solution to this problem. In contrast to traditional graphical models, such as the hidden Markov model (HMM), SCRFs follow a discriminative approach. Therefore, it is flexible to include any features in the model, such as overlapping or long-range interaction features over the whole sequence. The model also employs a convex optimization function, which results in globally optimal solutions to the model parameters. On the other hand, the segmentation setting in SCRFs makes their graphical structures intuitively similar to the protein 3-D structures and more importantly provides a framework to model the long-range interactions between secondary structures directly. Our model is applied to predict the parallel -helix fold, an important fold in bacterial...

• Type: Journal
• Year: 2006
• Where: JCB
• Authors: Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, Vanathi Gopalakrishnan
• Keywords: Graphical Models, JCB 2006, Model, Protein