Alpha-helical transmembrane protein fold
prediction using residue contacts
Timothy Nugent and David Jones
Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London WC1E 6BT
Despite significant efforts to predict alpha-helical transmembrane protein topology, relatively little attention has been
directed towards developing a method to pack the helices together. We present a novel approach that uses predicted lipid
exposure, residue contacts, sequence statistics and a force-directed algorithm to find the optimal helix packing
arrangement.
Introduction
Method
Alpha-helical transmembrane (TM) proteins constitute roughly 30% of a typical
genome and are involved in a wide variety of important biological processes
including cell signaling, transport of membrane-impermeable molecules and
cell recognition. Many are also prime drug targets, and it has been estimated
that more than half of all drugs currently on the market target membrane
proteins. Despite significant efforts to predict TM protein topology, little attention
has been directed toward developing a method to pack the helices together.
Since the membrane-spanning region is predominantly composed of alphahelices with a common alignment this task should, in principle, be easier than
predicting the fold of globular proteins. However, structural features including
re-entrant, tilted and kinked helices render simple lattice models that may work
for regularly packed proteins unable to model the diverse packing
arrangements now present in structural databases. We present a novel method
to predict lipid exposure, residue-residue contacts, helix-helix contacts and
finally the helical packing arrangement of TM proteins, benchmarked with full
cross-validation on a data set of 74 sequences, each containing at least two
TM helices, and all with crystal structures available.
Predicting lipid exposure
In order to predict lipid exposure, PSI-BLAST profile data with a sliding window
approach was used to train a support vector machine (SVM) classifier. To label
training examples, we used the Course Grain Database, a repository of
molecular dynamics simulation data, for which the proportion of simulation time
each residue is exposed to lipid is available. All residues within the membrane
that were exposed to lipid for more than half of simulation time were therefore
labelled as positive training examples. Under stringent cross validation using a
jack knife test, we were able to predict lipid exposure for each residue with 70%
accuracy and a Mathew's correlation coefficient (MCC) of 0.39. This compares
favourably with result from the LIPS server when benchmarked on the same
test set (62% accuracy, MCC 0.23).
Contact
Precision
FPR
FNR
MCC Accuracy
5.5 Å
5.5 Å
0.93
0.49
0.015
0.37
0.78
0.63
0.33
0.022
63.2%
52.3%
TM Contact Predictor
TM Contact Predictor+
TMHIT L5
6Å
6Å
6Å
0.98
0.91
0.77
0.0037
0.047
0.12
0.79
0.34
0.47
0.35
0.65
0.45
66.7%
82.6%
73.2%
TM Contact Predictor
SVMCon
SVMCon L5
ProfCon
ProfCon L5
8Å
8Å
8Å
8Å
8Å
0.98
0.57
0.82
0.43
0.72
0.0037
0.090
0.0074
0.83
0.043
0.79
0.84
0.95
0.16
0.84
0.36
0.11
0.13
0.017
0.19
66.7%
59.3%
59.5%
45.4%
62.0%
TM Contact Predictor
TMHCon L5
Table 2. Helix-helix interaction results. + No cross validation on 41 sequences common
to TMHIT training set.
Results show a significant improvement in accuracy and MCC scores against all
methods other than TMHIT. TMHIT was however trained on 41 sequences which
were common to our test set, so this score is likely to be an overestimate as we
were unable to cross-validate TMHIT results. In the absence of cross-validation
for these 41 sequences, our method performs substantially better. Full cross
validation on a smaller test set of 14 sequences resulted in scores of 68.4%
accuracy for our method and 66.5% for TMHIT.
A graph-based approach to find the optimal helix packing arrangement
To find the optimal helix packing arrangement the structure is represented as a
graph with helices forming vertices and helix-helix interactions forming edges.
By employing a force-directed algorithm the method attempts to minimise edge
crossing while maintaining uniform edge length, attributes common in native
structures. Once the helix positions are determined, a genetic algorithm is used
to rotate all helices such that the sum of predicted residue-residue contact
distances in minimised. Results for Halorhodopsin are shown in figure 1.
Predicting residue contacts and helix-helix interactions
Using topology information derived from crystal structures, we analysed
interactions between residues on different TM helices. For this study, we only
considered interactions within a single chain, rather than between chains. To
define residue-residue contacts, and compare our approach with other
methods, three contact definitions were used to label data: (i) backbone/side
chain heavy atoms are within 5.5 Å, (ii) C-beta atoms are within 6 Å or the
distance between interacting pair is less than the sum of their VDW radii + 0.6
Å and (iii) C-beta atoms are within 8 Å (C-alpha for glycine). To predict residue
contacts, we also used an SVM classifier. Features included: a 7 residue
window centred on each residue in the interacting pair (again using PSI-BLAST
profiles), predicted lipid exposure for each residue, and a number of sequence
based statistics. These included a binary vector representing distance between
residues and values representing the relative position of residues in each TM
helix – equivalent to a Z coordinate. SVM training files were roughly balanced
producing a positive/negative ratio of 1:1.25. Results are shown in table 1.
Method
Contact
Precision
FPR
FNR
MCC
TM Contact Predictor
TMHCon L5
5.5 Å
5.5 Å
0.65
0.089
0.0014
0.0021
0.86
0.99
0.29
0.024
TM Contact Predictor
TMHIT L5
6Å
6Å
0.64
0.57
0.001
0.0012
0.86
0.88
0.30
0.26
TM Contact Predictor
SVMCon
SVMCon L5
ProfCon
ProfCon L5
8Å
8Å
8Å
8Å
8Å
0.64
0.062
0.086
0.025
0.057
0.0017
0.0083
0.0003
0.46
0.0018
0.85
0.97
1.00
0.41
0.99
0.30
0.029
0.0089
0.035
0.012
Table 1. Residue-residue contact prediction results. L5 = top L/5 scoring predictions
assessed, where L is the combined length of all TM helices. Our method is labelled 'TM
Contact Predictor'.
In order to assess helix-helix interactions one pair of residue-residue contacts
was required to be correctly predicted. Our data set therefore contained 593
interacting helices and 815 non-interacting helices. Helix-helix prediction results
are shown in table 2.
Figure 1. Predicted packing arrangement for Halorhodopsin [PDB: 1E1K] (above) and
PDB structure with observed helix-helix interactions labelled (below).
Conclusions
Our results demonstrate that the use of predicted lipid exposure data combined
with evolutionary information and sequence-based statistics can be used to
accurately predict the packing arrangement of TM proteins. This method can be
used to gain insights into TM protein folding and direct further experimental
work.
This work was funded by the Biotechnology and Biological Sciences Research Council, and supported by the BioSapiens project, which is funded by the European
Commission within its FP6 Programme, under the thematic area "Life sciences, genomics and biotechnology for health,"contract number LSHG-CT-2003-503265.