Results of a comparison of the new SVM-based method with the sequence-based prediction method based on the ‘specificity-confering code’ by Stachelhaus

<p><b>Copyright information:</b></p><p>Taken from "Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs)"</p><p>Nucleic Acids Research 2005;33(18):5799-5808.</p><p>Published online 12 Oct 2005</p><p>PMCID:PMC1253831.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> () and Challis . () (For simplicity we refer to the latter as the ‘Stachelhaus method’): of the 1230 adenylation domains (with automatically extracted from the June 2005 version of UniProt) 70% or 858 obtained consistent predictions by both predictors (white sectors). For most of these consistent predictions (54% of the total or 666) the Stachelhaus method was based on an exact match with a known ‘specificity-conferring code’, the others had at least an 70% match. To 2.4% or 29 sequences none of the predictors can assign any specificity (no match ≥70%, diagonal hatches). An 18% or 217 sequences could be classified only by the SVMs and not by the Stachelhaus method (light gray sector), and 18 A domains (1.5%) could not be classified by the SVMs but by the Stachelhaus method (cross-hatched), two of them are rare specificities. The Stachelhaus predictions for the rest are mainly based on 70% matches to known specificity ‘codes’. For 108 sequences (8.8%) the predictions were inconsistent but 38 of them (3% of the total, gray sector) had matches to rare amino acids that were not used for training the SVMs. The remaining 70 incompatible predictions were mainly based on ≤80% identity matches with known ‘specificity-conferring codes’ (black sector)

Analysis of Domain Architecture and Phylogenetics of Family 2 Glycoside Hydrolases (GH2)

<div><p>In this work we report a detailed analysis of the topology and phylogenetics of family 2 glycoside hydrolases (GH2). We distinguish five topologies or domain architectures based on the presence and distribution of protein domains defined in Pfam and Interpro databases. All of them share a central TIM barrel (catalytic module) with two β-sandwich domains (non-catalytic) at the N-terminal end, but differ in the occurrence and nature of additional non-catalytic modules at the C-terminal region. Phylogenetic analysis was based on the sequence of the Pfam Glyco_hydro_2_C catalytic module present in most GH2 proteins. Our results led us to propose a model in which evolutionary diversity of GH2 enzymes is driven by the addition of different non-catalytic domains at the C-terminal region. This model accounts for the divergence of β-galactosidases from β-glucuronidases, the diversification of β-galactosidases with different transglycosylation specificities, and the emergence of bicistronic β-galactosidases. This study also allows the identification of groups of functionally uncharacterized protein sequences with potential biotechnological interest.</p></div

Characterization of the Gut Microbial Community of Obese Patients Following a Weight-Loss Intervention Using Whole Metagenome Shotgun Sequencing

<div><p>Background/Objectives</p><p>Cross-sectional studies suggested that obesity is promoted by the gut microbiota. However, longitudinal data on taxonomic and functional changes in the gut microbiota of obese patients are scarce. The aim of this work is to study microbiota changes in the course of weight loss therapy and the following year in obese individuals with or without co-morbidities, and to asses a possible predictive value of the gut microbiota with regard to weight loss maintenance.</p><p>Subjects/Methods</p><p>Sixteen adult patients, who followed a 52-week weight-loss program comprising low calorie diet, exercise and behavioral therapy, were selected according to their weight-loss course. Over two years, anthropometric and metabolic parameters were assessed and microbiota from stool samples was functionally and taxonomically analyzed using DNA shotgun sequencing.</p><p>Results</p><p>Overall the microbiota responded to the dietetic and lifestyle intervention but tended to return to the initial situation both at the taxonomical and functional level at the end of the intervention after one year, except for an increase in <i>Akkermansia</i> abundance which remained stable over two years (12.7x10³ counts, 95%CI: 322–25100 at month 0; 141x10³ counts, 95%CI: 49-233x10³ at month 24; p = 0.005). The <i>Firmicutes/Bacteroidetes</i> ratio was higher in obese subjects with metabolic syndrome (0.64, 95%CI: 0.34–0.95) than in the “healthy obese” (0.27, 95%CI: 0.08–0.45, p = 0.04). Participants, who succeeded in losing their weight consistently over the two years, had at baseline a microbiota enriched in <i>Alistipes</i>, <i>Pseudoflavonifractor</i> and enzymes of the oxidative phosphorylation pathway compared to patients who were less successful in weight reduction.</p><p>Conclusions</p><p>Successful weight reduction in the obese is accompanied with increased <i>Akkermansia</i> numbers in feces. Metabolic co-morbidities are associated with a higher <i>Firmicutes/Bacteroidetes</i> ratio. Most interestingly, microbiota differences might allow discrimination between successful and unsuccessful weight loss prior to intervention.</p></div

Domain architecture of DA type 5 sequences.

<p>Colored and stripped boxes correspond to domains identified by Pfam and Interpro databases, respectively. Modules with more than 40% sequence identity compared to BIG1 domains identified by Interpro, with a coverage higher than 60%, were tagged as BIG1. Letters (a-i) on the right edge of the figure group sequences with similar DAs. Domain assignment at the I1, I2, and I3 regions (subtype a) was based on the analysis carried out with the β-galactosidase from <i>S</i>. <i>pneumoniae</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168035#pone.0168035.ref030" target="_blank">30</a>]. Numbers on top of non-identified regions indicate approximate number of residues.</p

Sequence alignment around the putative catalytic site of proteins analyzed in Fig 4.

<p>Purple boxes indicate residues potentially involved in the active site, as predicted by docking analysis with β-D-(1,4)-galactosyl-lactose. Positions with more than 50% identity are colored in light blue and those with 100% identity are shown in dark blue color. Bc and Bf correspond to <i>Bacillus circulans</i> and <i>Bifidobacterium bifidum</i> β-galactosidases, respectively.</p

Phylogenetic analysis of the GH2C domain.

<p>The tree was calculated by Maximum Likelihood method based on the JTT matrix-based model [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168035#pone.0168035.ref021" target="_blank">21</a>] condensed at < 50% bootstrap support. The analysis involved a selection of 380 amino acid sequences for the different DAs. The tree was drawn using FigTree software (<a href="http://tree.bio.ed.ac.uk/software/figtree/" target="_blank">http://tree.bio.ed.ac.uk/software/figtree/</a>). Numbers indicate the DA type. The asterisk marks the subtree further analysed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168035#pone.0168035.g004" target="_blank">Fig 4</a>. Sectors corresponding to a single DA type are colored in turquoise (DA type 1), purple (DA type 2), blue (DA type 3), red (DA type 4). Cluster 5* is colored in light grey. Sectors including mixed DA types are colored in dark grey.</p

Domain architectures of GH2 family.

<p>Numbers in parentheses indicate the number of sequences representative of each DA type, included in the Pfam database.</p

Analysis of Domain Architecture and Phylogenetics of Family 2 Glycoside Hydrolases (GH2) - Fig 5

<p><b>Docking of a DA type 5 (A) and a DA type 3 (B) β-galactosidase with their main transglycosylation products.</b> The figure shows the domains that compose the architecture of the enzymes, represented in different colors. The residues potentially interacting with β-D-(1,4)-galactosyl-lactose in <i>Bacillus circulans</i> β-galactosidase (A) or with β-D-(1,3)-galactosyl-lactose in <i>Thermotoga maritima</i> β-galactosidase (B) are highlighted on the right side.</p

Bacterial species changes during weight loss intervention.

<p>Cladogram (based on 16S sequences) displaying the most abundant species from genera influenced by the intervention. Significant changes between T0 and T3 (left column of squares), between T3 and T6 (middle column), and between T6 and T24 (right column) are indicated by a star(p < 0.05). Blue squares indicate a decrease, red an increase in abundance. Species are colored according to the phyla they belong to (blue: Spirochaetae, pink: Bacteroidetes, green: Firmicutes, light pink: Proteobacteria, orange: Actinobacteria, brown: Verrucomicrobia, red: Synergistetes). This tree was created using the free software EvolView [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149564#pone.0149564.ref027" target="_blank">27</a>].</p

Phylogenetic subtree corresponding to the region marked 5* in Fig 3.

<p>Letters on the right edge of the figure group sequences with similar DAs, as indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168035#pone.0168035.g002" target="_blank">Fig 2</a>. Branch numbers indicate bootstrap values. Bc and Bf correspond to <i>Bacillus circulans</i> and <i>Bifidobacterium bifidum</i> β-galactosidases, respectively.</p