Digital Commons Network^™

Mutual Information Without The Influence Of Phylogeny Or Entropy Dramatically Improves Residue Contact Prediction, Stanley Dunn, Lindi Wahl, Gregory Gloor

Stanley D Dunn

Motivation: Compensating alterations during the evolution of protein families give rise to coevolving positions that contain important structural and functional information. However, a high background composed of random noise and phylogenetic components interferes with the identification of coevolving positions.

Results: We have developed a rapid, simple and general method based on information theory that accurately estimates the level of background mutual information for each pair of positions in a given protein family. Removal of this background results in a metric, MIp, that correctly identifies substantially more coevolving positions in protein families than any existing method. A significant fraction of these …

A Hidden Markov Model Capable Of Predicting And Discriminating Β-Barrel Outer Membrane Proteins, Pantelis G. Bagos, Theodore D. Liakopoulos, Ioannis C. Spyropoulos, Stavros J. Hamodrakas

Pantelis Bagos

BACKGROUND: Integral membrane proteins constitute about 20-30% of all proteins in the fully sequenced genomes. They come in two structural classes, the alpha-helical and the beta-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the alpha-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the beta-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained …