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1.  Environment specific substitution tables improve membrane protein alignment 
Bioinformatics  2011;27(13):i15-i23.
Motivation: Membrane proteins are both abundant and important in cells, but the small number of solved structures restricts our understanding of them. Here we consider whether membrane proteins undergo different substitutions from their soluble counterparts and whether these can be used to improve membrane protein alignments, and therefore improve prediction of their structure.
Results: We construct substitution tables for different environments within membrane proteins. As data is scarce, we develop a general metric to assess the quality of these asymmetric tables. Membrane proteins show markedly different substitution preferences from soluble proteins. For example, substitution preferences in lipid tail-contacting parts of membrane proteins are found to be distinct from all environments in soluble proteins, including buried residues. A principal component analysis of the tables identifies the greatest variation in substitution preferences to be due to changes in hydrophobicity; the second largest variation relates to secondary structure. We demonstrate the use of our tables in pairwise sequence-to-structure alignments (also known as ‘threading’) of membrane proteins using the FUGUE alignment program. On average, in the 10–25% sequence identity range, alignments are improved by 28 correctly aligned residues compared with alignments made using FUGUE's default substitution tables. Our alignments also lead to improved structural models.
Availability: Substitution tables are available at:
PMCID: PMC3117371  PMID: 21685065
2.  MEDELLER: homology-based coordinate generation for membrane proteins 
Bioinformatics  2010;26(22):2833-2840.
Motivation: Membrane proteins (MPs) are important drug targets but knowledge of their exact structure is limited to relatively few examples. Existing homology-based structure prediction methods are designed for globular, water-soluble proteins. However, we are now beginning to have enough MP structures to justify the development of a homology-based approach specifically for them.
Results: We present a MP-specific homology-based coordinate generation method, MEDELLER, which is optimized to build highly reliable core models. The method outperforms the popular structure prediction programme Modeller on MPs. The comparison of the two methods was performed on 616 target–template pairs of MPs, which were classified into four test sets by their sequence identity. Across all targets, MEDELLER gave an average backbone root mean square deviation (RMSD) of 2.62 Å versus 3.16 Å for Modeller. On our ‘easy’ test set, MEDELLER achieves an average accuracy of 0.93 Å backbone RMSD versus 1.56 Å for Modeller.
Availability and Implementation:; Implemented in Python, Bash and Perl CGI for use on Linux systems; Supplementary data are available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2971581  PMID: 20926421
3.  How threshold behaviour affects the use of subgraphs for network comparison 
Bioinformatics  2010;26(18):i611-i617.
Motivation: A wealth of protein–protein interaction (PPI) data has recently become available. These data are organized as PPI networks and an efficient and biologically meaningful method to compare such PPI networks is needed. As a first step, we would like to compare observed networks to established network models, under the aspect of small subgraph counts, as these are conjectured to relate to functional modules in the PPI network. We employ the software tool GraphCrunch with the Graphlet Degree Distribution Agreement (GDDA) score to examine the use of such counts for network comparison.
Results: Our results show that the GDDA score has a pronounced dependency on the number of edges and vertices of the networks being considered. This should be taken into account when testing the fit of models. We provide a method for assessing the statistical significance of the fit between random graph models and biological networks based on non-parametric tests. Using this method we examine the fit of Erdös–Rényi (ER), ER with fixed degree distribution and geometric (3D) models to PPI networks. Under these rigorous tests none of these models fit to the PPI networks. The GDDA score is not stable in the region of graph density relevant to current PPI networks. We hypothesize that this score instability is due to the networks under consideration having a graph density in the threshold region for the appearance of small subgraphs. This is true for both geometric (3D) and ER random graph models. Such threshold behaviour may be linked to the robustness and efficiency properties of the PPI networks.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2935432  PMID: 20823329
4.  Functionally guided alignment of protein interaction networks for module detection 
Bioinformatics  2009;25(23):3166-3173.
Motivation: Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.
Results: Here we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity-based network alignment display little overlap (<15%) with sequence similarity-based alignment. Our combined approach integrating sequence and function-based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.
Availability: Program binaries and source code is freely available at
Supplementary Information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2778333  PMID: 19797409
5.  iMembrane: homology-based membrane-insertion of proteins 
Bioinformatics  2009;25(8):1086-1088.
Summary: iMembrane is a homology-based method, which predicts a membrane protein's position within a lipid bilayer. It projects the results of coarse-grained molecular dynamics simulations onto any membrane protein structure or sequence provided by the user. iMembrane is simple to use and is currently the only computational method allowing the rapid prediction of a membrane protein's lipid bilayer insertion. Bilayer insertion data are essential in the accurate structural modelling of membrane proteins or the design of drugs that target them.
Availability: iMembrane is available under a non-commercial open-source licence, upon request.
Supplementary information: Supplementary data are available at Bioinformatics online and at
PMCID: PMC2666813  PMID: 19237449

Results 1-5 (5)