Pseudomonas aeruginosa diaminopimelate decarboxylase: evolutionary relationship with other amino acid decarboxylases

The lysA gene encodes meso-diaminopimelate (DAP) decarboxylase (E.C.4.1.1.20), the last enzyme of the lysine biosynthetic pathway in bacteria. We have determined the nucleotide sequence of the lysA gene from Pseudomonas aeruginosa. Comparison of the deduced amino acid sequence of the lysA gene product revealed extensive similarity with the sequences of the functionally equivalent enzymes from Escherichia coli and Corynebacterium glutamicum. Even though both P. aeruginosa and E. coli are Gram-negative bacteria, sequence comparisons indicate a greater similarity between enzymes of P. aeruginosa and the Gram- positive bacterium C. glutamicum than between those of P. aeruginosa and E. coli enzymes. Comparison of DAP decarboxylase with protein sequences present in data bases revealed that bacterial DAP decarboxylases are homologous to mouse (Mus musculus) ornithine decarboxylase (E.C.4.1.1.17), the key enzyme in polyamine biosynthesis in mammals. On the other hand, no similarity was detected between DAP decarboxylases and other bacterial amino acid decarboxylases.      

Recognizing the forest for the trees: testing temporal patterns of cladogenesis using a null model of stochastic diversification

Computer simulations are developed and employed to examine the expected temporal distributions of nodes under a null model of stochastic lineage bifurcation and extinction. These Markovian models of phylogenetic process were constructed so as to permit direct comparisons against empirical phylogenetic trees generated from molecular or other information available solely from extant species. For replicate simulated phylads with n extant species, cumulative distribution functions (cdf's) of branching times were calculated, and compared (using the Kolmogorov-Smirnov test statistic D) to those from three published empirical trees. Molecular phylogenies for columbine plants and avian cranes showed statistically significant departures from the null expectations, in directions indicating recent and ancient species' radiations, respectively, whereas a molecular phylogeny for the Drosophila virilis species group showed no apparent historical clustering of branching events. Effects of outgroup choice and phylogenetic frame of reference were investigated for the columbines and found to have a predictable influence on the types of conclusions to be drawn from such analyses. To enable other investigators to statistically test for nonrandomness in temporal cladogenetic pattern in empirical trees generated from data on extant species, we present tables of mean cdf's and associated probabilities under the null model for expected branching times in phylads of varying size. The approaches developed in this report complement and extend those of other recent methods for employing null models to assess the statistical significance of pattern in evolutionary trees.      

Conformational Changes during Pore Formation by the Perforin-Related Protein Pleurotolysin

Membrane attack complex/perforin-like (MACPF) proteins comprise the largest superfamily of pore-forming proteins, playing crucial roles in immunity and pathogenesis. Soluble monomers assemble into large transmembrane pores via conformational transitions that remain to be structurally and mechanistically characterised. Here we present an 11 Å resolution cryo-electron microscopy (cryo-EM) structure of the two-part, fungal toxin Pleurotolysin (Ply), together with crystal structures of both components (the lipid binding PlyA protein and the pore-forming MACPF component PlyB). These data reveal a 13-fold pore 80 Å in diameter and 100 Å in height, with each subunit comprised of a PlyB molecule atop a membrane bound dimer of PlyA. The resolution of the EM map, together with biophysical and computational experiments, allowed confident assignment of subdomains in a MACPF pore assembly. The major conformational changes in PlyB are a ∼70° opening of the bent and distorted central β-sheet of the MACPF domain, accompanied by extrusion and refolding of two α-helical regions into transmembrane β-hairpins (TMH1 and TMH2). We determined the structures of three different disulphide bond-trapped prepore intermediates. Analysis of these data by molecular modelling and flexible fitting allows us to generate a potential trajectory of β-sheet unbending. The results suggest that MACPF conformational change is triggered through disruption of the interface between a conserved helix-turn-helix motif and the top of TMH2. Following their release we propose that the transmembrane regions assemble into β-hairpins via top down zippering of backbone hydrogen bonds to form the membrane-inserted β-barrel. The intermediate structures of the MACPF domain during refolding into the β-barrel pore establish a structural paradigm for the transition from soluble monomer to pore, which may be conserved across the whole superfamily. The TMH2 region is critical for the release of both TMH clusters, suggesting why this region is targeted by endogenous inhibitors of MACPF function.     

Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.