Centrum för Genomik och Bioinformatik (CGB) / Center for Genomics Research
Abstract
Transmembrane proteins make up a large and important class of proteins.
About 20% of all genes encode transmembrane proteins. They control both
substances and information going in and out of a cell. Yet basic
knowledge about membrane insertion and folding is sparse, and our ability
to identify, over-express, purify, and crystallize transmembrane proteins
lags far behind the field of water-soluble proteins.
It is diffcult to determine the three dimensional structures of
transmembrane proteins. erefore, researchers normally attempt to
determine their topology, i.e. which parts of the protein are buried in
the membrane, and on what side of the membrane are the other parts
located.
Proteins aimed for export have an N-terminal sequence known as a signal
peptide that is inserted into the membrane and cleaved off. The same
mechanism that inserts transmembrane proteins into their membranes also
handles the export of protein with signal peptides. Transmembrane helices
and signal peptides thus have many features in common.
In silico methods for predicting transmembrane topology and methods for
predicting signal peptides from amino acid sequence are a fast and
relatively accurate alternative to biochemical experiments. A methodology
called hidden Markov models (HMMs) has proved particularly useful for
these and other prediction tasks.
In this thesis, properties of transmembrane topology predictors and
signal peptide predictors are investigated. It includes three novel HMM
based prediction methods.
i) A combined transmembrane topology and signal peptide predictor,
Phobius. The paper shows that cross predictions, i.e. signal peptides
predicted as transmembrane helices and vice versa, are a common problem.
About 10% of the genes in E.coli have overlapping signal peptide and
transmembrane helix predictions by conventional predictors. We were able
to dramatically lower these false cross predictions.
ii)Amethod for detecting remote G protein-coupled receptor (GPCR)
families,GPCRHMM. GPCRs are a very large and divergent superfamily of
transmembrane proteins. We designed a hidden Markov model based on the
topological regions of the superfamily. We searched five genomes and
predicted 120 previously not annotated sequences as possible GPCRs. e
majority of these predictions (102) were in C. elegans, but 4 were found
in human and 7 in mouse. We as well conclude that a family of odorant
receptors in Drosophila are not GPCRs.
iii)Amethod to improve predictions with HMMs of generic sequence features
(such as transmembrane segments or signal peptides) by including
homologs. We show that the performance of Phobius using this decoder was
significantly better than with other decoders.
We also assessed the difficulty of benchmark sets used in transmembrane
topology prediction. By studying the level of agreement between different
predictors applied to typical benchmark sets andwhole proteome sets,we
concluded that the benchmark sets are far easier to predict than reality.
In other words, the accuracies reported in benchmark studies are
exaggerated.
Thesis also includes a paper presenting a hypothesis of the transmembrane
topology of presenilin, a protein involved in the development of
Alzheimer's disease. By comparing the output of several transmembrane
topology predictors with experimental results from previous studies, a
novel nine-transmembrane topology with an extracellular C-terminus was
elucidated