Memsat




Transmembrane helix and topology prediction

ebisu group

chemogenomix © 2012

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Overview

MEMSAT3 is a program which predicts the secondary structure and topology of all-helix integral membrane proteins based on the recognition of topological models. The original MEMSAT method employed a set of statistical tables (log likelihood ratios) compiled from well- characterized membrane protein data, and a novel dynamic programming algorithm to recognize membrane topology models by expectation maximization. These statistical tables showed definite biases towards certain amino acid species on the inside, middle and outside of a cellular membrane.

MEMSAT3 was released in January 2007 and now employs a neural network to determine which residues are on the cytoplasmic side of the membrane and which residues are within transmembrane helices. The same dynamic programming algorithm is used to calculate the most likely overall topology.

MEMSAT3 predicting transmembrane protein topology from sequence profiles On a standard data set of 184 transmembrane proteins the method was found to predict both the correct topology and the locations of transmembrane segments for 80% of the test set. Also, by using a second neural network specifically to discriminate transmembrane from globular proteins, a very low overall false positive rate (0.5%) was achieved in detecting transmembrane proteins. The system has many parameters that can be set appropriately for ones research. For instance if you already know that your protein is a transmembrane protein you can concentrate on the predicted locations and topology in more detail.

Memsat3 is described in the following reference:
Jones D.T. (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics. In press. PUBMED

The original method is described in the following reference:
Jones, D.T., Taylor, W.R. and Thornton, J. M. (1994) Biochemistry. 33:3038-3049. PUBMED

MEMSAT3 can be run from the PSIPRED prediction server


The information described here is taken from the Bioinformatics group web site run by Prof David Jones at University College London

http://bioinf.cs.ucl.ac.uk