A Machine Text-Inspired Machine Learning Approach for Identification of Transmembrane Helix Boundaries

In this paper, we adapt a statistical learning approach, inspired by automated topic segmentation techniques in speech-recognized documents to the challenging protein segmentation problem in the context of G-protein coupled receptors (GPCR). Each GPCR consists of 7 transmembrane helices separated by alternating extracellular and intracellular loops. Viewing the helices and extracellular and intracellular loops as 3 different topics, the problem of segmenting the protein amino acid sequence according to its secondary structure is analogous to the problem of topic segmentation. The method presented involves building an n-gram language model for each ‘topic’ and comparing their performance in predicting the current amino acid, to determine whether a boundary occurs at the current position. This presents a distinctly different approach to protein segmentation from the Markov models that have been used previously and its commendable results is evidence of the benefit of applying machine...

• Type: Conference
• Year: 2005
• Where: ISMIS
• Authors: Betty Yee Man Cheng, Jaime G. Carbonell, Judith Klein-Seetharaman
• Keywords: ISMIS 2005, Protein Segmentation, Protein Segmentation Problem, Topic Segmentation