MemBrain: improving the accuracy of predicting transmembrane helices.

ABSTRACT: Prediction of transmembrane helices (TMH) in alpha helical membrane proteins provides valuable information about the protein topology when the high resolution structures are not available. Many predictors have been developed based on either amino acid hydrophobicity scale or pure statistical approaches. While these predictors perform reasonably well in identifying the number of TMHs in a protein, they are generally inaccurate in predicting the ends of TMHs, or TMHs of unusual length. To improve the accuracy of TMH detection, we developed a machine-learning based predictor, MemBrain, which integrates a number of modern bioinformatics approaches including sequence representation by multiple sequence alignment matrix, the optimized evidence-theoretic K-nearest neighbor prediction algorithm, f

SUBMITTER: Shen H 

PROVIDER: S-EPMC2396505 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature