Transmembrane helix prediction using amino acid property features and latent semantic analysis

Background: Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often restricted to the generally accepted topology "cytoplasmictransmembrane-extracellular" and cannot adapt to membrane proteins that do not conform to this topology. Recent crystal structures of channel proteins have revealed novel architectures showing that the above topology may not be as universal as previously believed. Thus, there is a need for methods that can better predict TM helices even in novel topologies and families. Results: Here, we describe a new method "TMpro" to predict TM helices with high accuracy. To avoid overfitting to existing topologies, we have collapsed cytoplasmic and extracellular labels to a single state, non-TM. TMpro is a binary classifier which...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Madhavi Ganapathiraju, Narayanas Balakrishnan, Raj Reddy, Judith Klein-Seetharaman
• Keywords: BMCBI 2008, Free Parameters, Proteins, Sufficient Training Data