Ram Samudrala

Pseudomonas aeruginosa uses a cyclic-di-GMP-regulated adhesin to reinforce the biofilm extracellular matrix

Pseudomonas aeruginosa, the principal pathogen of cystic fibrosis patients, forms antibiotic‐resistant biofilms promoting chronic colonization of the airways. The extracellular (EPS) matrix is a crucial component of biofilms that provides the community multiple benefits. Recent work suggests that the secondary messenger, cyclic‐di‐GMP, promotes biofilm formation. An analysis of factors specifically expressed in P. aeruginosa under conditions of elevated c‐di‐GMP, revealed functions involved in the production and maintenance of the biofilm extracellular matrix. We have characterized one of these components, encoded by the PA4625 gene, as a putative adhesin and designated it cdrA. CdrA shares structural similarities to extracellular adhesins that belong to two‐partner secretion systems. The cdrA gene is in a two gene operon that also encodes a putative outer membrane transporter, CdrB. The cdrA gene encodes a 220 KDa protein that is predicted to be rod‐shaped protein harbouring a β‐helix structural motif. Western analysis indicates that the CdrA is produced as a 220 kDa proprotein and processed to 150 kDa before secretion into the extracellular medium. We demonstrated that cdrAB expression is minimal in liquid culture, but is elevated in biofilm cultures. CdrAB expression was found to promote biofilm formation and auto‐aggregation in liquid culture. Aggregation mediated by CdrA is dependent on the Psl polysaccharide and can be disrupted by adding mannose, a key structural component of Psl. Immunoprecipitation of Psl present in culture supernatants resulted in co‐immunoprecipitation of CdrA, providing additional evidence that CdrA directly binds to Psl. A mutation in cdrA caused a decrease in biofilm biomass and resulted in the formation of biofilms exhibiting decreased structural integrity. Psl‐specific lectin staining suggests that CdrA either cross‐links Psl polysaccharide polymers and/or tethers Psl to the cells, resulting in increased biofilm structural stability. Thus, this study identifies a key protein structural component of the P. aeruginosa EPS matrix.

Data access for the 1,000 Plants (1KP) project

The 1,000 plants (1KP) project is an international multi-disciplinary consortium that has generated transcriptome data from over 1,000 plant species, with exemplars for all of the major lineages across the Viridiplantae (green plants) clade. Here, we describe how to access the data used in a phylogenomics analysis of the first 85 species, and how to visualize our gene and species trees. Users can develop computational pipelines to analyse these data, in conjunction with data of their own that they can upload. Computationally estimated protein-protein interactions and biochemical pathways can be visualized at another site. Finally, we comment on our future plans and how they fit within this scalable system for the dissemination, visualization, and analysis of large multi-species data sets.

Genes for the cytoskeletal protein tubulin in the bacterial genus Prosthecobacter

Tubulins, the protein constituents of the microtubule cytoskeleton, are present in all known eukaryotes but have never been found in the Bacteria or Archaea. Here we report the presence of two tubulin-like genes [bacterial tubulin a (btuba) and bacterial tubulin b (btubb)] in bacteria of the genus Prosthecobacter (Division Verrucomicrobia). In this study, we investigated the organization and expression of these genes and conducted a comparative analysis of the bacterial and eukaryotic protein sequences, focusing on their phylogeny and 3D structures. The btuba and btubb genes are arranged as adjacent loci within the genome along with a kinesin light chain gene homolog. RT-PCR experiments indicate that these three genes are cotranscribed, and a probable promoter was identified upstream of btuba. On the basis of comparative modeling data, we predict that the Prosthecobacter tubulins are monomeric, unlike eukaryotic α and β tubulins, which form dimers and are therefore unlikely to form microtubule-like structures. Phylogenetic analyses indicate that the Prosthecobacter tubulins are quite divergent and do not support recent horizontal transfer of the genes from a eukaryote. The discovery of genes for tubulin in a bacterial genus may offer new insights into the evolution of the cytoskeleton.

Accurate Prediction of Secreted Substrates and Identification of a Conserved Putative Secretion Signal for Type III Secretion Systems

The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates—effector proteins—are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

AB INITIO PROTEIN STRUCTURE PREDICTION USING A COMBINED HIERARCHICAL APPROACH

As part of the third Critical Assessment of Structure Prediction meeting (CASP3), we predict the three‐dimensional structures for 13 proteins using a hierarchical approach. First, all possible compact conformations of a protein sequence are enumerated using a highly simplified tetrahedral lattice model. We select a large subset of these conformations using a lattice‐based scoring function and build detailed all‐atom models incorporating predicted secondary structure. A combined all‐atom knowledge‐based scoring function is then used to select three smaller subsets from these all‐atom models. Finally, a consensus‐based distance geometry procedure is used to generate the best conformations from each of the all‐atom subsets. With this method, we are able to predict the global topology/shape for all or a large part of the sequence for six out of the thirteen proteins. For two other proteins, the topology/shape for shorter fragments are predicted. This represents a marked improvement in ab initio prediction since CASP was first instigated in 1994. Proteins Suppl 1999;3:194–198.

A novel knowledge-based approach to design inorganic-binding peptides

MOTIVATION The discovery of solid-binding peptide sequences is accelerating along with their practical applications in biotechnology and materials sciences. A better understanding of the relationships between the peptide sequences and their binding affinities or specificities will enable further design of novel peptides with selected properties of interest both in engineering and medicine. RESULTS A bioinformatics approach was developed to classify peptides selected by in vivo techniques according to their inorganic solid-binding properties. Our approach performs all-against-all comparisons of experimentally selected peptides with short amino acid sequences that were categorized for their binding affinity and scores the alignments using sequence similarity scoring matrices. We generated novel scoring matrices that optimize the similarities within the strong-binding peptide sequences and the differences between the strong- and weak-binding peptide sequences. Using the scoring matrices thus generated, a given peptide is classified based on the sequence similarity to a set of experimentally selected peptides. We demonstrate the new approach by classifying experimentally characterized quartz-binding peptides and computationally designing new sequences with specific affinities. Experimental verifications of binding of these computationally designed peptides confirm our predictions with high accuracy. We further show that our approach is a general one and can be used to design new sequences that bind to a given inorganic solid with predictable and enhanced affinity.

Mouse transcriptome: Neutral evolution of ‘non-coding’ complementary DNAs

(Arising from: Y. Okazaki et al. 420, 563–573 200210.1038/nature01266; Okazaki et al. reply)Okazaki et al. have argued that as many as 15,815 of 33,409 non-redundant mouse complementary DNAs may represent functional RNA genes, on the basis of their findings that some of these cDNAs are confirmed by expressed sequence tagging and are found near CpG islands or polyadenylation signals — although many are expressed at such low levels that they could not be detected by microarray analysis. We show here that conservation of these ‘non-coding’ cDNAs in rats or humans is no better than in an evolutionarily neutral control. Our results indicate that they are either non-functional or, if they are functional, are specific to a given species.

Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design

The prediction of functional sites in newly solved protein structures is a challenge for computational structural biology. Most methods for approaching this problem use evolutionary conservation as the primary indicator of the location of functional sites. However, sequence conservation reflects not only evolutionary selection at functional sites to maintain protein function, but also selection throughout the protein to maintain the stability of the folded state. To disentangle sequence conservation due to protein functional constraints from sequence conservation due to protein structural constraints, we use all atom computational protein design methodology to predict sequence profiles expected under solely structural constraints, and to compute the free energy difference between the naturally occurring amino acid and the lowest free energy amino acid at each position. We show that functional sites are more likely than non-functional sites to have computed sequence profiles which differ significantly from the naturally occurring sequence profiles and to have residues with sub-optimal free energies, and that incorporation of these two measures improves sequence based prediction of protein functional sites. The combined sequence and structure based functional site prediction method has been implemented in a publicly available web server.

PROTINFO: new algorithms for enhanced protein structure predictions

We describe new algorithms and modules for protein structure prediction available as part of the PROTINFO web server. The modules, comparative and de novo modelling, have significantly improved back-end algorithms that were rigorously evaluated at the sixth meeting on the Critical Assessment of Protein Structure Prediction methods. We were one of four server groups invited to make an oral presentation (only the best performing groups are asked to do so). These two modules allow a user to submit a protein sequence and return atomic coordinates representing the tertiary structure of that protein. The PROTINFO server is available at .

Incorporating background frequency improves entropy-based residue conservation measures

BackgroundSeveral entropy-based methods have been developed for scoring sequence conservation in protein multiple sequence alignments. High scoring amino acid positions may correlate with structurally or functionally important residues. However, amino acid background frequencies are usually not taken into account in these entropy-based scoring schemes.ResultsWe demonstrate that using a relative entropy measure that incorporates amino acid background frequency results in improved performance in identifying functional sites from protein multiple sequence alignments.ConclusionOur results suggest that the application of appropriate background frequency information may lead to more biologically relevant results in many areas of bioinformatics.