Simulating star formation in molecular cloud cores IV. The role of turbulence and thermodynamics

We perform SPH simulations of the collapse and fragmentation of low-mass cores having different initial levels of turbulence (alpha_turb=0.05,0.10,0.25). We use a new treatment of the energy equation which captures the transport of cooling radiation against opacity due to both dust and gas (including the effects of dust sublimation, molecules, and H^- ions). We also perform comparison simulations using a standard barotropic equation of state. We find that -- when compared with the barotropic equation of state -- our more realistic treatment of the energy equation results in more protostellar objects being formed, and a higher proportion of brown dwarfs; the multiplicity frequency is essentially unchanged, but the multiple systems tend to have shorter periods (by a factor ~3), higher eccentricities, and higher mass ratios. The reason for this is that small fragments are able to cool more effectively with the new treatment, as compared with the barotropic equation of state. We find that the process of fragmentation is often bimodal. The first protostar to form is usually, at the end, the most massive, i.e. the primary. However, frequently a disc-like structure subsequently forms round this primary, and then, once it has accumulated sufficient mass, quickly fragments to produce several secondaries. We believe that this delayed fragmentation of a disc-like structure is likely to be an important source of very low-mass hydrogen-burning stars and brown dwarfs.Comment: 14 pages, 8 figures. Accepted for publication by A&

The PRINTS database: a fine-grained protein sequence annotation and analysis resource—its status in 2012

The PRINTS database, now in its 21st year, houses a collection of diagnostic protein family ‘fingerprints’. Fingerprints are groups of conserved motifs, evident in multiple sequence alignments, whose unique inter-relationships provide distinctive signatures for particular protein families and structural/functional domains. As such, they may be used to assign uncharacterized sequences to known families, and hence to infer tentative functional, structural and/or evolutionary relationships. The February 2012 release (version 42.0) includes 2156 fingerprints, encoding 12 444 individual motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Here, we report the current status of the database, and introduce a number of recent developments that help both to render a variety of our annotation and analysis tools easier to use and to make them more widely available

LIPPRED: A web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites

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

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

A predictor of membrane class: Discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications

TATPred:a Bayesian method for the identification of twin arginine translocation pathway signal sequences

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Nave-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942

BioCIDER: a Contextualisation InDEx for biological Resources discovery

Summary The vast, uncoordinated proliferation of bioinformatics resources (databases, software tools, training materials etc.) makes it difficult for users to find them. To facilitate their discovery, various services are being developed to collect such resources into registries. We have developed BioCIDER, which, rather like online shopping ‘recommendations’, provides a contextualization index to help identify biological resources relevant to the content of the sites in which it is embedded

Utopia documents: linking scholarly literature with research data

Motivation: In recent years, the gulf between the mass of accumulating-research data and the massive literature describing and analyzing those data has widened. The need for intelligent tools to bridge this gap, to rescue the knowledge being systematically isolated in literature and data silos, is now widely acknowledged