Number references.

docs/inmembrane.markdown

@@ -28,7 +28,7 @@ A common task in bioinformatics is to integrate the results of protein predictio
 
 A number of published software packages exist for global prediction of subcellular localization of bacterial proteins. Most notable is _PSORTb v3.0_ (Yu, et al, 2010) which predicts general subcellular localization for Gram-positive, Gram-negative and Archaeal proteins sequences. CELLO (Yu et al, 2006) is a web accessible Support Vector Machine-based classifier that predicts localization of Gram-positive, Gram-negative and eukaryotic proteins. Some predictors and databases have been developed with a focus solely on Gram-positive surface proteins. Both _Augur_ (Billion et al., 2006) and _LocateP_ (Zhou et al., 2008) are pipelines wrapping existing specific localization predictors, and provide web accessible databases of pre-calculated subcellular localization for Gram-positive proteomes. While the source code for _PSORTb 3.0_ is available under an open source license, the code for the other annotation pipelines discussed is not generally available for download.
 
-An extension to general membrane localization prediction is the analysis of membrane protein topology to identify prominent surface exposed loops. These potentially surface exposed (PSE) proteins are of particular interest since they constitute attractive vaccine candidates. One existing workflow for annotation of PSE proteins is the program SurfG+, which focuses on Gram-positive bacterial proteomes. SurfG+ is a Java program that carries out batch processing of several standard bioinformatic tools to specifically predict proteins that protrude out of the peptidoglycan layer of the bacterium. These predictions are intended to identify a set of proteins that would be accessible in cell-surface protease shaving experiments. SurfG+ itself does not carry out any computationally intensive analysis, but rather leverages the results of a transmembrane helix predictor (*TMMOD*) (Robel et al, 2005), a secretion signal predictor (*SignalP*) (Thomas et al 2011), a lipoprotein signal predictior (*LipoP*) (Agnieszka et al 2003) and a sequence alignment for protein profiles (*HMMER*) (http://hmmer.org).
+An extension to general membrane localization prediction is the analysis of membrane protein topology to identify prominent surface exposed loops. These potentially surface exposed (PSE) proteins are of particular interest since they constitute attractive vaccine candidates. One existing workflow for annotation of PSE proteins is the program SurfG+, which focuses on Gram-positive bacterial proteomes. SurfG+ is a Java program that carries out batch processing of several standard bioinformatic tools to specifically predict proteins that protrude out of the peptidoglycan layer of the bacterium. These predictions are intended to identify a set of proteins that would be accessible in cell-surface protease shaving experiments. SurfG+ itself does not carry out any computationally intensive analysis, but rather leverages the results of a transmembrane helix predictor (*TMMOD*) (Kahsay et al, 2005), a secretion signal predictor (*SignalP*) (Petersen et al 2011), a lipoprotein signal predictior (*LipoP*) (Agnieszka et al 2003) and a sequence alignment for protein profiles (*HMMER*) (http://hmmer.org).
 
 Nevertheless, _SurfG+_ suffers several problems that plague much bioinformatic software. Despite being published in 2009, the URL mentioned in the original reference no longer exists. We were able to find a [source-code repository](https://mulcyber.toulouse.inra.fr/projects/surfgplus) but we were not able to get the program to work, due in part to dependencies that are not longer generally available for download.
 
@@ -115,7 +115,7 @@ Total               1696        1862         1821        1755       1529
 
 In addition to the Gram-positive surface protocol, we have also implemented a protocol for summarizing subcellular localization and topology predictions for Gram-negative bacterial proteomes. Gram-negative bacteria have both a cytoplasmic (inner) membrane, a periplasmic space, a peptidoglycan layer and an outer membrane decorated in lipopolysaccharide (Figure 1). Membrane proteins integral to the inner membrane contain hydrophobic helical transmembrane segments, analogous to the Gram-positive cytoplasmic membrane, while the proteins embedded in the outer membrane form ß-barrels composed of amphipathic ß-strands. Lipoproteins in Gram-negative bacteria can be associated with the inner or the outer membrane.
 
-Potential signal sequences of the general (Sec) secretory pathway are predicted using SignalP. Twin-Arginine translocase (Tat) signals are predicted using TatFind (Rose et al, 2002) and a profile HMM built from the Prosite (Sigrist et al 2002) Tat sequence set ([PS51318](http://prosite.expasy.org/PS51318)). Transmembrane helicies and topologies of inner membrane proteins are predicted using TMHMM and optionally with MEMSAT3. As with the Gram-positive protocol, lipoproteins were predicted using LipoP, however the Gram-negative protocol additionally detects the "Asp+2" inner-membrane retention signal (Masuda et al 2002) to differentiate between lipoproteins transported to the outer membrane (`LIPOPROTEIN(OM)`) and those retained on the periplasmic side of the inner membrane (`LIPOPROTEIN(IM)`). 
+Potential signal sequences of the general (Sec) secretory pathway are predicted using SignalP. Twin-Arginine translocase (Tat) signals are predicted using TatFind (Rose et al, 2002) and a profile HMM built from the Prosite (Sigrist et al 2002) Tat sequence set ([PS51318](http://prosite.expasy.org/PS51318)). Transmembrane helices and topologies of inner membrane proteins are predicted using TMHMM and optionally with MEMSAT3. As with the Gram-positive protocol, lipoproteins were predicted using LipoP, however the Gram-negative protocol additionally detects the "Asp+2" inner-membrane retention signal (Masuda et al 2002) to differentiate between lipoproteins transported to the outer membrane (`LIPOPROTEIN(OM)`) and those retained on the periplasmic side of the inner membrane (`LIPOPROTEIN(IM)`). 
 
 The topology of integral inner membrane proteins is analysed using the same 'potentially surface exposed' loops algorithm as the Gram-positive protocol, however in this case sequences are classified as `IM`, `IM(cyto)`, `IM(peri)` and `IM(cyto+peri)` to indicate proteins with long cytoplasmic and/or periplasmic loops or domains. Experimentally, large periplasmic domains may be accessible to protease shaving when the outer membrane has been disrupted, such as in spheroplasts generated using outer membrane permeabilization agents. Unlike the Gram-positive plasma membrane, the Gram-negative inner membrane is not decorated with LPS and as such periplasmic loops and domains of intergral membrane proteins are expected to be more easily accessed by protease once the outer membrane is permeabilized. We have chosen a length of 30 residues as a conservative threshold (the `internal_exposed_loop_min` setting) for annotating cytoplasmic (`+cyto`) and periplasmic (`+peri`) loops or domains. This should be modified as required to suit the purpose of the user.
 
@@ -228,6 +228,8 @@ Any restrictions to use by non-academics: Use of _inmembrane_ itself is unrestri
 
 Agnieszka S. Juncker, Hanni Willenbrock, Gunnar Von Heijne, Søren Brunak, Henrik Nielsen, And Anders Krogh. (2003) Prediction of lipoprotein signal peptides in Gram-negative bacteria. __Protein Science__ 12:1652–1662. <http://dx.doi.org/10.1110/ps.0303703>
 
+Anantharaman, V, and Aravind, L. (2003) “New Connections in the Prokaryotic Toxin-antitoxin Network: Relationship with the Eukaryotic Nonsense-mediated RNA Decay System.” __Genome Biology__ 4:R81 <http://dx.doi.org/10.1186/gb-2003-4-12-r81>.
+
 Anders Krogh, Björn Larsson, Gunnar von Heijne and Erik L. L. Sonnhammer (2001) Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes. __J. Mol. Biol.__ 305:567-580. <http://dx.doi.org/10.1006/jmbi.2000.4315>
 
 Bagos PG, Liakopoulos TD, Hamodrakas SJ (2005) Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method. BMC bioinformatics 6: 7. <http://dx.doi.org/10.1186/1471-2105-6-7>.
@@ -268,7 +270,7 @@ Ou Y-YY, Gromiha MMM, Chen S-AA, Suwa M (2008) TMBETADISC-RBF: Discrimination of
 
 Petersen TN, Brunak S, von Heijne G, Nielsen H. (2011) __Nature Methods__, 8:785-786. <http://dx.doi.org/10.1038/nmeth.1701>
 
-Robel Y. Kahsay1, Guang Gao1 and Li Liao1. An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes (2005) __Bioinformatics__ 21: 1853-1858.
+Kahsay RY, Gao G, Liao L. (2005) An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes. __Bioinformatics__, 21: 1853–1858.
 
 Rose RW, Brüser T, Kissinger JC, Pohlschröder M. (2002) Adaptation of protein secretion to extremely high salt concentrations by extensive use of the twin arginine translocation pathway. __Mol. Microbiol.__ 5: 943-950 <http://dx.doi.org/10.1046/j.1365-2958.2002.03090.x>