Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and 
				modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method 
				based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed α-helical topology prediction. This method has accuracies of 77.4% 
				for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and 
				offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications

Attwood, Teresa K

Flower, Darren R

Taylor, Paul D

Toseland, Christopher P

English

PubMed

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications

Kent Academic Repository

Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach.

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address beta-barrel topology prediction. The beta-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology

Beta barrel trans-membrane proteins: Enhanced prediction using a Bayesian approach.

Attwood, TK

Taylor, PD

Toseland, CP

Flower, DR

The University of Manchester - Institutional Repository

Alpha Helical Transmembrane Proteins: Enhanced prediction using a Bayesian approach.

Taylor, Paul D.

Toseland, Christopher P.

Attwood, Teresa K.

Flower, Darren R.

Aston Publications Explorer

Bioinformation by Biomedical Informatics Publishing Group                            open access www.bioinformation.net                     Prediction Model __________________________________________________________________________________________________ ISSN 0973-2063 Bioinformation 1(6): 231-233 (2006)  Bioinformation, an open access forum © 2006 Biomedical Informatics Publishing Group  231Beta barrel trans-membrane proteins: Enhanced prediction using a Bayesian approach  Paul D. Taylor1, Christopher P. Toseland2, Teresa K. Attwood3 and Darren R. Flower1* 1The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; 2National Institute for Medical Research, The Ridgeway, Mill Hill, London, NW7 1AA, UK; 3Faculty of Life Sciences & School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PT, UK;  Darren R. Flower* - Email: darren.flower@jenner.ac.uk; Phone: +44 1635 577954; Fax: +44 1635 577908;                       * Corresponding author received October 04, 2006; accepted October 06, 2006; published online October 07, 2006  Abstract: Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address β-barrel topology prediction. The β-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.  Keywords: beta barrel transmembrane protein; prokaryotic membrane proteins; Bayesian Networks; prediction method; sub-cellular location  Background: Accurate and reliable prediction of protein structure and function remains a challenge. Of particular importance is the prediction of membrane proteins, as, unlike soluble and fibrous proteins, membrane proteins remain poorly tractable targets for the principal experimental methods of structure determination: X-ray crystallography and multidimensional nuclear magnetic resonance (NMR) spectroscopy. [1] This problem is highlighted by the observation that 20% of most genomes encode membrane proteins [2], yet the number of solved membrane protein structures is approximately 2% of the RCSB Protein Data Bank (PDB). [3, 4]   Membrane proteins fall into two structural classes: α-helical and β-barrel. At present, the only known location for TM β-barrels is the outer membrane of Gram-negative bacteria. [5] Although there is very strong evidence for their presence in mitochondrial and chloroplast membranes (e.g., the voltage-dependent anion channel (VDAC), the long-standing homologue candidate in the outer mitochondrial membrane). The SCOP database classifies TM β-barrels into 6 structural superfamiles: OmpA-like, OmpT-like, OmpLA, porins, TolC and Leukocidin (α Haemolysin) [6]. β-barrels have been shown to have a variety of functions, including the passive transport of ions and small hydrophilic molecules, the export of xenobiotics, import of siderophore-bound iron, and a role in bacterial pathogenicity. [7-10] Despite these widely different functions, these proteins show a remarkable degree of structural similarity, which has led Schulz to identify 8 rules summarising β-barrel construction. [5] Of these, two are of particular importance when attempting to predict TM β-barrel topology: rule two states that both the N- and C-termini are at the periplasmic end of the barrel, restricting the strand number to even values; and rule 4, that external β-strand connections are long loops (termed L1, L2, etc.), whereas the periplasmic strand connections are generally short (T1, T2, etc.).  Although the 8 rules defined by Schulz characterise β -barrel construction well, the prediction of barrel topology from sequence remains a difficult task owing to several complicating factors. First, identifying potential TM strands as stretches of sequence where residues alternate between polar and non-polar grossly over-simplifies the problem, as this pattern is frequently broken by non-polar residues on the interior of the barrel. Second, the average length of β-strands is seldom more than half a dozen residues; they are therefore much harder to distinguish than longer TM α-helices. [11] Finally, the most significant hindrance to β-barrel topology prediction is probably the lack of solved structures on which to train predictive methods. [12]   This paper describes the construction of a predictor for a beta-barrel membrane protein topology, based on machine learning Bayesian Networks (BNs). BNs are considered especially suited to computational biology, as they provide a flexible and powerful framework for statistical inference, and learn model parameters from data. [13]  Methodology: Data-set A dataset of TM proteins of experimentally verified α-helical topology were required to train the method. The data-set was obtained from the TMPDB database (release 6.2) [12], in which topologies have been determined using X-ray crystallography, NMR, gene fusion, substituted cysteine accessibility, N-linked glycosylation experiment and other biochemical methods. Non-redundant subsets of TMPDB were used, and hence sequence similarity between proteins was less than 30%. The β-barrel data-set (TMPDB_β_non-redundant) consisted of 15 proteins.  Bioinformation by Biomedical Informatics Publishing Group                            open access www.bioinformation.net                     Prediction Model __________________________________________________________________________________________________ ISSN 0973-2063 Bioinformation 1(6): 231-233 (2006)  Bioinformation, an open access forum © 2006 Biomedical Informatics Publishing Group  232Beta-Barrel Transmembrane Protein Topology Predictor A static full Bayesian model was used as it best fulfils the requirements expected of the network. The main advantage of such a model, compared with its naïve counterpart, is that the output probability is not a product of probabilities from each descriptor, but a model which associates one probability with each combination of descriptors. Thus, overall performance is typically better, but never worse, than that of the best individual descriptor.   Descriptors of amino acid properties were used to characterise amino acids. Each descriptor was averaged, using a sliding-window, to produce a set of meta-descriptors. Each meta-descriptor represents one node, the state of which influences, probabilistically, the state of the output node. There are two main facets to the descriptor-based sliding-window methodology: the use of appropriate descriptors that will provide inferences about whether a residue is TM or not; and the use of a sliding window as a mechanism for taking into consideration the surrounding environment of a residue.   The descriptors used were the 434 amino acid property scales in the AAIndex database (release 6.0). [14] The scales provide a large range of amino acid properties, including: size, charge, hydrophobicity and more recondite propensities (such as membrane buried preference parameter). The initial step in the creation of the methods was the production of a temporary meta-training-set. For every residue in the training-set, the 434 sliding-window-averaged values (one for each scale) are calculated, and whether or not the residue is TM located is recorded. The BN is trained on this meta-data-set. The optimal shape of the sliding window was found to be trapezoid (data not shown), with 50% less weighting of the two residues at either end – this is because these residues are further from the residue being analysed, and hence have less influence. However, this may not account for possible tertiary conformations that may bring residues close together that are far apart in the sequence. For TM β-strands, a window size of 7 residues was used. The network was then trained on the sliding-window values and residue location. During training, the network attempts to find which descriptor values best correlate with the residue occurring in a TM region.  When presented with a test sequence, prediction initially follows the same process as training, with each residue assigned a sliding-window-averaged value for each descriptor. The network then moves through the sequence, and determines whether the values encountered are typical of a TM-located residue. As prediction is made on an individual-residue basis, there is a requirement for post-network processing to translate the prediction from single residues to TM regions. This is done with reference to knowledge of TM-region tendencies observed in solved and/or well characterised structures. Accordingly, post-network processing imposes the following rules upon possible TM regions: β-strands must have a minimum length of 6 and a maximum length of 25 residues. Short strand predictions are disregarded and long predictions are split at the central residue and the two residues either side of the split are designated non-transmembrane.  Results and Discussion:  A BN, predictive of β-strands in TM proteins, was constructed and its accuracy assessed using 15 proteins taken from a non-redundant data-set of experimentally verified topology. The β-strand predictor initially appeared to produce disappointing results when considering overall protein topology accuracy (42.7%). When considering individual β-strands, however, the accuracy of prediction was found to be 88.6%: much higher than the relatively low topology accuracy. This clearly shows that the method can accurately distinguish strands from non-membranous regions of the protein; a task that, as discussed earlier, presents many challenges, owing to the short and variable nature of β-strands. In common with problems reported for other predictors [15], the predictions were a little shorter than the actual strand, the predicted length being on average 92.6% of the actual value.   The β-strand predictor showed low overall topology accuracy, but high strand accuracy. The major observed failing of the method was to predict two separate strands as one combined or double strand. These false double strands fell below the maximum 25-residue strand length threshold and were thus not split into two separate strands. The problem arises only between strands separated by short intra-cellular turns. The method assumes the short span of turn residues to be part of a strand, as they are surrounded by longer stretches of strand residues. This failing may, in part, be caused by the sliding-window method, which takes into account the surrounding environment and is therefore less sensitive to anomalous short regions. The topological consequence of such combined strands is barrel predictions with odd strand numbers. As all β-barrels have even numbers of strands, this error is easily spotted yet is surprisingly difficult to correct. Algorithms attempting to identify “double strands” are unable to distinguish them easily from long strands. This problem is exemplified by the Ferrichrome iron receptor (FHUA) protein from E. coli, which has the shortest and longest strand lengths in the data-set: 7 and 24 residues respectively. If we predict an odd strand number for this protein, we are required to distinguish a double strand, which may be as short as 17 residues, from a long strand of up to 24 residues. This problem arises, in part, from the very small training-set used to train the network. Unusually long or unusually short strands, as compared with the average strand length, are relatively infrequent; thus, any network would have few training examples on which to base its predictions. Although the accuracy of the β-strand predictor suffers from the use of a small data-set, and would clearly benefit from re-training as more structures become available, its overall predictive power nevertheless compares very favourably with methods developed using other artificial intelligence techniques. Bioinformation by Biomedical Informatics Publishing Group                            open access www.bioinformation.net                     Prediction Model __________________________________________________________________________________________________ ISSN 0973-2063 Bioinformation 1(6): 231-233 (2006)  Bioinformation, an open access forum © 2006 Biomedical Informatics Publishing Group  233Conclusion: The method described here represents an important advance in the computational determination of membrane protein structural class and topology. The beta-barrel TM protein topology predictor has good accuracy. The method described offers a useful and complementary tool for the analysis of membrane proteins for a wide range of possible applications. Acknowledgement: PDT wishes to thank the MRC for a priority area studentship. The Jenner Institute (Formally, The Edward Jenner Institute for Vaccine Research) wishes to thank its sponsors: GlaxoSmithKline, the Medical Research Council, the Biotechnology and Biological Sciences Research Council, and the UK Department of Health.  References:[01] A. Arora & L. K. Tamm, Curr. Opin. Struct. Biol., 11:540 (2001) [PMID: 11785753] [02] J. Liu & B. Rost, Protein Sci., 10:1970 (2001) [PMID: 11567088] [03] H. M. Berman, et al., Acta. Crystallogr. D. Biol. Crystallogr., 58:899 (2002) [PMID: 12037327] [04] R. Casadio, et al., Brief Bioinform., 4:341 (2003) [PMID: 14725347] [05] G. E. Schulz, Curr. Opin. Struct. Biol., 10:443 (2000) [PMID: 10981633] [06] A. G Murzin, et al., J. Mol. Biol., 247:536 (1995) [PMID: 7723011] [07] G. E. Schulz, Curr. Opin. Struct. Biol., 6:485 (1996) [PMID: 8794162] [08] V. Koronakis, et al., Nature, 405:914 (2000)                [PMID: 10879525] [09] A. Pautsch & G. E. Schulz, Nat. Struct. Biol., 5:1013 (1998) [PMID: 9808047] [10] J. Vogt & G. E. Schulz, Structure Fold Des., 7: 1301 (1999) [PMID: 10545325] [11] Y. Mandel-Gutfreund & L. M. Gregoret, J. Mol. Biol., 323:453 (2002) [PMID: 12381301] [12] M. Ikeda, et al., Nucleic Acids Res., 31:406 (2003) [PMID: 12520035]  [13] Pearl, J, Probablistic Reasoning in Intellegent Systems: Networks of Plausible Inference. (1988), San Mateo, California: Morgan Kaufman. [14] J. L. Gardy, et al., Nucleic Acids Res., 31:3613 (2003) [PMID: 12824378] [15] D. M. Lao, et al., Bioinformatics, 18:1562 (2002) [PMID:12490439] Edited by P. Kangueane Citation: Taylor et al., Bioinformation 1(6): 231-233 (2006) License statement: This is an open-access article, which permits unrestricted use, distribution, and reproduction in  any medium, for non-commercial purposes, provided the original author and source are credited.  

Beta barrel trans-membrane proteins:enhanced prediction using a Bayesian approach

Beta Barrel Transmembrane Proteins: Enhanced prediction using a Bayesian approach.

Bioinformation by Biomedical Informatics Publishing Group open access www.bioinformation.net                         Prediction Model                ____________________________________________________________________________________________ ISSN 0973-2063 Bioinformation 1(6): 234-236 (2006)  Bioinformation, an open access forum © 2006 Biomedical Informatics Publishing Group    234Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach  Paul D. Taylor1, Christopher P. Toseand2, Teresa K. Attwood3 and DarrenR.Flower1* 1. The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; 2. National Institute for Medical Research, The Ridgeway, Mill Hill, London, NW7 1AA, UK; 3. Faculty of Life Sciences & School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PT, UK;  Darren R. Flower* - Email: darren.flower@jenner.ac.uk; Phone: +44 1635 577954; Fax: +44 1635 577908;  * Corresponding author                       received October 06, 2006; accepted November 01, 2006; published online November 14, 2006  Abstract: Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed α-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.  Keywords: trans-membrane protein; alpha helix; static full Bayesian model; prediction; amino acid descriptors   Background: Membrane proteins, which are poorly tractable targets for the main experimental methods of structure determination - X-ray crystallography and multidimensional nuclear magnetic resonance (NMR) spectroscopy - yet form approximately 20% of most genomes [1-4], fall into two structural classes: α-helical and β-barrel. α-helical membrane proteins are responsible for interactions between most cells and their environment. [5] Trans-membrane (TM) helices are typically encoded by stretches of 17-25 residues [6], which provide sufficient length to cross the membrane. [7] A compositional bias towards hydrophobic residues is apparent in the TM helices, as they must make complementary interactions with the hydrophobic lipid bilayer. [8] α-helical proteins vary in topology, from single TM regions to “serpentine” structures consisting of over 20 TM helices, which are separated by hydrophilic regions that loop alternately in and out of the extra-cellular space and the cytoplasm. [9] The functions that have been observed for α-helical proteins are as varied as their topologies, including signal recognition, receptors, transfer of molecules and ions across the membrane, and energy translocation and conservation. [10-12] The function of membrane proteins with multiple α-helices, where TM domains often combine to form a tightly-coupled structure, is dependent on their final 3-dimensional conformation. [13] Consequently, the number and arrangement of TM domains is often conserved within a protein family.   We describe here construction of a predictor of α-helical membrane proteins. This method is based on Bayesian Networks (BNs). BNs are considered especially suited to computational biology, as they provide a flexible and powerful framework for statistical inference, and learn model parameters from data. [14]       Methodology: Data-sets A dataset of TM proteins of confirmed α-helical topology was required for training the method. This data-set was taken from the TMPDB database (release 6.2) [15], in which topologies have been determined using a variety of experimental techniques such as X-ray crystallography. Non-redundant subsets of TMPDB were created where sequence similarity between proteins was less than 30%. The data-set (TMPDB_α_non-redundant) comprised 231 proteins, of which 93 were from eukaryotic and 138 from prokaryotic organisms.  Predictor A static full Bayesian model was used for the network. The principal advantage of a static full Bayesian model, compared with a naïve model, is that the output probability is not a product of probabilities from each descriptor. Rather, a full Bayesian model associates one probability with each combination of descriptors. Thus, overall performance is at least as good as that of the best individual descriptor.  434 amino acid property scales were used as descriptors during model building. The descriptors were obtained from the AAIndex database. [16] The scales provide a large range of amino acid properties, including: size, charge, hydrophobicity and propensities (such as membrane buried preference parameter and surface accessibility). Each descriptor was averaged, using a sliding-window, to produce a set of meta-descriptors. The environment surrounding the residue is taken into Bioinformation by Biomedical Informatics Publishing Group open access www.bioinformation.net                         Prediction Model                ____________________________________________________________________________________________ ISSN 0973-2063 Bioinformation 1(6): 234-236 (2006)  Bioinformation, an open access forum © 2006 Biomedical Informatics Publishing Group    235account through this method. The structure of the network requires that each meta-descriptor represents one node, the state of which influences, probabilistically, the state of the output node. Thus our methodology seeks to identify appropriate descriptors correlated with trans-membrane burial and to exploit a sliding window which implicitly considers neighbourhood effects from the surrounding environment.   The initial step in our method development cycle was the production of a temporary meta-training-set. For every residue in the training-set, the 434 sliding-window-averaged values (one for each scale) were calculated, and whether or not the residue was part of a TM region was recorded. The BN was then trained with this meta-training-set. A window size of 13 residues was used. The optimal shape of the sliding window for these particular predictors was found to be trapezoid (data not shown), with 50% less weighting of the two residues at either end. This may result from the reduced influence of distal residues relative to the immediate environment. Naturally, this does not account for possible tertiary conformations that may bring residues close together that are far apart in the sequence. During training, the network attempts to find which descriptor values best correlate with residues in a TM region.  Prediction by the network, when presented with a test sequence, initially follows the same process as training, each residue in the sequence being assigned sliding-window-averaged values for all the descriptors. The network then moves through the sequence, and determines whether the values for the descriptors are typical of a TM-located residue. As prediction is made on an individual-residue basis, there is a requirement for post-network processing to translate the prediction from single residues to TM regions. This is done with reference to knowledge of TM-region tendencies observed in well characterised structures. Accordingly, post-network processing imposes the following rules: α-helices cannot be shorter than 14 residues or longer than 40 residues. Thus short helix predictions are disregarded and those >40 residues are split into two at the most hydrophilic of the central 5 residues. Three residues either side of the split point are labelled as non-trans-membrane.  Results and Discussion: A BN, predictive of α-helices in TM proteins, was constructed and its accuracy assessed using leave-one-out and leave-five-out cross validation with a non-redundant protein data-set consisting of 231 prokaryotic and eukaryotic proteins of known structure. The results are summarised in Table 1. Correct prediction of topology is defined as correct prediction of the number of TM domains, the correct prediction of location of the central residue to an accuracy of 5 residues either side, and the correct prediction of N-terminal location.  Although the results show a good level of topology prediction accuracy, comparable with the best publicly available method, for both eukaryotic and prokaryotic sequences, there is an obvious difference in performance for the two. In order to address this, the average number and length of TM segments for the different sets was analysed. This revealed no significant difference between them, with eukaryotes possessing about five TM 21-residue segments, compared with five 20-residue TM segments for prokaryotes.  The lower accuracy for eukaryotic proteins has several potential explanations. Eukaryotic proteins have an intrinsically more complex and variable range of topologies and thus are harder to predict. Alternatively, prokaryotic proteins may have a less comprehensive representation of the total proteins in vivo. The first explanation was tested by comparing the average number and length of TM regions for eukaryotic and prokaryotic proteins in the data-set: no significant difference was found. When the numbers of species contributing to each set was scrutinised, there initially appeared to be little evidence for the second hypothesis: of the 86 eukaryotic proteins, there are 20 different species represented, compared with 29 for the 130 prokaryotic proteins. However, analysis of individual species contribution reveals a different picture: 55% of the prokaryotic proteins were obtained from Escherichia coli. This is unsurprising, as E. coli is probably the most comprehensively studied of all prokaryotes, owing to its use in biological research. By contrast, the eukaryotic set is more widely spread across species, but is still dominated by 4 organisms, from which 75% of the proteins are derived: human (27%), cattle (24%), rat (15%) and yeast (10%). The biased prokaryotic data-set may increase the accuracy of prediction, as the method would be skewed significantly towards E. coli protein topology prediction. As these proteins obviously constitute the same large proportion of both test- and training-sets, then the predictor would predict them with improved accuracy.   Sequence type LOO cross- validation (%) 5-fold cross-validation (% MCC (%)Prokaryotic 77.4 75.2 0.856 Eukaryotic 61.4 58.6 0.795 Table 1: Performance of the alpha-helical predictor  The small range of organisms with well-characterised TM proteins is, as always, a significant challenge for predictor development. A comprehensive, but non-redundant, data-set is required if reliable and comprehensive predictive methods are to be developed. A predictor trained on the widest possible range of topologies is more likely to make accurate or, at least, more unbiased predictions. In the future it will be Bioinformation by Biomedical Informatics Publishing Group open access www.bioinformation.net                         Prediction Model                ____________________________________________________________________________________________ ISSN 0973-2063 Bioinformation 1(6): 234-236 (2006)  Bioinformation, an open access forum © 2006 Biomedical Informatics Publishing Group    236necessary to continually retrain the α-helical predictor using newly published topologies, as they become available. This will hopefully increase the representation of different types of protein from all organisms.  Conclusion: The method described here represents an important advance in the computational prediction of membrane protein structural class and topology. The α-helical TM protein topology predictor is of comparable accuracy to the best publicly available methods. The method described offers a useful alternative, yet complementary, tool for the analysis of membrane proteins for a wide range of possible applications.  Acknowledgement: PDT wishes to thank the MRC for a priority area studentship. The Jenner Institute (Formally, The Edward Jenner Institute for Vaccine Research) wishes to thank its sponsors: GlaxoSmithKline, the Medical Research Council, the Biotechnology and Biological Sciences Research Council, and the UK Department of Health.  References: [01] A. Arora & L. K. Tamm, Curr. Opin.  Struct. Biol., 11:540 (2001) [PMID: 11785753] [02] J. Liu & B. Rost, Protein Sci., 10:1970 (2001)     [PMID: 11567088] [03] H. M. Berman, et al., Acta. Crystallogr. D. Biol. Crystallogr., 58:899 (2002) [PMID: 12037327] [04] R. Casadio, et al., Brief Bioinform., 4:341 (2003) [PMID: 14725347] [05] D. Frishman & H. W. Mewes, Nat. Struct. Biol., 4:626 (1997) [PMID: 9253410] [06] G. von Heijne, Bioessays, 17:25 (1995) [PMID: 7702590] [07] J. Deisenhofer, et al., Methods Enzymol., 115:303 (1985) [PMID: 4079791] [08] M. B. Ulmschneider & M. S. Sansom, Biochim. Biophys. Acta., 1512:1 (2001) [PMID: 11334619] [09] S. Moller, et al., Bioinformatics, 17:646 (2001) [PMID: 11448883] [10] G. von Heijne, Biochim. Biophys. Acta., 947:307 (1988) [PMID: 3285892] [11] B. Traxler, et al., J Membr. Biol., 132:1 (1993) [PMID: 8459445] [12] M. L. Jennings, Annu. Rev. Biochem., 58:999 (1989) [PMID: 2673027] [13] B. Hille, Ion channels of excitable membranes. (2001), Sunderland, Mass.; [Great Britain]: Sinauer Associates [14] J. Pearl, Probablistic Reasoning in Intellegent Systems: Networks of Plausible Inference. (1988), San Mateo, California: Morgan Kaufman [15] M. Ikeda, et al., Nucleic Acids Res., 31:406 (2003) [PMID: 12520035] [16] J. L. Gardy, et al., Nucleic Acids Res., 31:3613 (2003) [PMID: 12824378]                                                          Edited by P. Kangueane Citation: Taylor et al., Bioinformation 1(6): 234-236 (2006) License statement: This is an open-access article, which permits unrestricted use, distribution, and reproduction in  any medium, for non-commercial purposes, provided the original author and source are credited.   

Alpha helical trans-membrane proteins:enhanced prediction using a Bayesian approach

Ion channels of excitable membranes.

Nucleic Acids Res., 31:3613 (2003) [PMID: 12824378] Edited by P. Kangueane Citation: Taylor et al.,

Probablistic Reasoning in Intellegent Systems: Networks of Plausible Inference.

Protein Sci.,

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1891693

Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach

Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach

Abstract

Similar works

Full text

Available Versions

Kent Academic Repository

Kent Academic Repository

The University of Manchester - Institutional Repository

Aston Publications Explorer

The University of Manchester - Institutional Repository

Aston Publications Explorer