Kinetics of fragmentation-annihilation processes

We investigate the kinetics of systems in which particles of one species undergo binary fragmentation and pair annihilation. In the latter, nonlinear process, fragments react at collision to produce an inert species, causing loss of mass. We analyse these systems in the reaction-limited regime by solving a continuous model within the mean-field approximation. The rate of fragmentation, for a particle of mass $x$ to break into fragments of masses $y$ and $x-y$, has the form $x^{\lambda-1}$ ($\lambda>0$), and the annihilation rate is constant and independent of the masses of the reactants. We find that the asymptotic regime is characterized by the annihilation of small-mass clusters. The results are compared with those for a model with linear mass-loss (i.e.\ with a sink). We also study more complex models, in which the processes of fragmentation and annihilation are controlled by mutually-reacting catalysts. Both pair- and linear-annihilation are considered. Depending on the specific model and initial densities of the catalysts, the time-decay of the cluster-density can now be very unconventional and even non-universal. The interplay between the intervening processes and the existence of a scaling regime are determined by the asymptotic behaviour of the average-mass and of the mass-density, which may either decay indefinitely or tend to a constant value. We discuss further developments of this class of models and their potential applications.Comment: 16 pages(LaTeX), submitted to Phys. Rev.

Density-driven flows in evaporating binary liquid droplets

In the evaporation of microlitre liquid droplets, the accepted view is that surface tension dominates and the effect of gravity is negligible. We report, through the first use of rotating optical coherence tomography, that a change in the flow pattern and speed occurs when evaporating binary liquid droplets are tilted, conclusively showing that gravitational effects dominate the flow. We use gas chromatography to show that these flows are solutal in nature, and we establish a flow phase diagram demonstrating the conditions under which different flow mechanisms occur

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines

Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring matrices (PSSMs) and physico-chemical properties of amino acids. It is a two stage approach involving multiclass support vector machines (SVMs) as classifiers for three different structural conformations, viz., helix, sheet and coil. In the first stage, PSSMs obtained from PSI-BLAST and five specially selected physicochemical properties of amino acids are fed into SVMs as features for sequence-to-structure prediction. Confidence values for forming helix, sheet and coil that are obtained from the first stage SVM are then used in the second stage SVM for performing structure-to-structure prediction. The two-stage cascaded classifiers (PSP_MCSVM) are trained with proteins from RS126 dataset. The classifiers are finally tested on target proteins of critical assessment of protein structure prediction experiment-9 (CASP9). PSP_MCSVM with brainstorming consensus procedure performs better than the prediction servers like Predator, DSC, SIMPA96, for randomly selected proteins from CASP9 targets. The overall performance is found to be comparable with the current state-of-the art. PSP_MCSVM source code, train-test datasets and supplementary files are available freely in public domain at: http://sysbio.icm.edu.pl/secstruct and http://code.google.com/p/cmater-bioinfo

Influence of grazing on structure, composition and dynamics of vegetation in Mediterranean temporary pools (northern Tunisia)

Three temporary pools of Sejenane region (northern Tunisia) have been studied with the aims of characterizing their vegetation, and of specifying the impact of grazing on their structure, composition and dynamics. Permanent transects of quadrats have been surveyed in spring and summer during two (not-grazed pool) to three years (grazed pools). The vegetation of the three pools is organized in three concentric belts related to the topographic gradient. The between-years dynamics is strongly characterized by the alternation of distinct spring and summer vegetations. Grazing appears as the main control of the composition and structure of hydrophytic plant communities. It prevents the colonization by competitive, perturbation-sensitive species, and favours the persistence of annual, light-demanding dwarf plants. In order to protect the biodiversity of these rare habitats in Tunisia, it is necessary to maintain, through an adapted management of grazing, a landscape mosaic of grazed and not-grazed zonesTrois mares temporaires de la région de Sejenane (Tunisie septentrionale) ont été étudiées afin de caractériser leur végétation et de préciser l’influence du pâturage sur sa structure, sa composition et sa dynamique intra- et interannuelle. Des transects de quadrats permanents ont été suivis au printemps et en été durant deux (mare non pâturée) à trois ans (mares pâturées). Le cortège floristique des mares étudiées est organisé en trois ceintures concentriques liées au gradient topographique. La dynamique intra-annuelle de la végétation est nettement marquée par l’alternance de cortèges printaniers et estivaux distincts. Le pâturage apparaît comme le principal facteur contrôlant la composition et la structure des communautés végétales hydrophytiques. Il limite le développement des espèces compétitives sensibles aux perturbations et favorise le maintien d’une flore de petite taille, thérophytique et héliophile. Afin de préserver la biodiversité de ces habitats rares en Tunisie, il apparaît nécessaire de maintenir, par une gestion adaptée du pâturage, une mosaïque paysagère de zones non pâturées et pâturée

PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

<p>Abstract</p> <p>Background</p> <p>Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing α-helices, β-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a powerful technique from the field of nonlinear system identification.</p> <p>Results</p> <p>Using PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs) are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at <url>http://bioinf.sce.carleton.ca/PCISS</url>. In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP) interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input protein sequence data and also to encode the resulting structure prediction in a machine-readable format. To our knowledge, this represents the only publicly available SOAP-interface for a protein secondary structure prediction service with published WSDL interface definition.</p> <p>Conclusion</p> <p>Relative to the 9 contemporary methods included in the comparison cascaded PCI classifiers perform well, however PCI finds greatest application as a consensus classifier. When PCI is used to combine a sequence-to-structure PCI-based classifier with the current leading ANN-based method, PSIPRED, the overall error rate (Q3) is maintained while the rate of occurrence of a particularly detrimental error is reduced by up to 25%. This improvement in BAD score, combined with the machine-readable SOAP web service interface makes PCI-SS particularly useful for inclusion in a tertiary structure prediction pipeline.</p

Structural Model of the Rev Regulatory Protein from Equine Infectious Anemia Virus

Rev is an essential regulatory protein in the equine infectious anemia virus (EIAV) and other lentiviruses, including HIV-1. It binds incompletely spliced viral mRNAs and shuttles them from the nucleus to the cytoplasm, a critical prerequisite for the production of viral structural proteins and genomic RNA. Despite its important role in production of infectious virus, the development of antiviral therapies directed against Rev has been hampered by the lack of an experimentally-determined structure of the full length protein. We have used a combined computational and biochemical approach to generate and evaluate a structural model of the Rev protein. The modeled EIAV Rev (ERev) structure includes a total of 6 helices, four of which form an anti-parallel four-helix bundle. The first helix contains the leucine-rich nuclear export signal (NES). An arginine-rich RNA binding motif, RRDRW, is located in a solvent-exposed loop region. An ERLE motif required for Rev activity is predicted to be buried in the core of modeled structure where it plays an essential role in stabilization of the Rev fold. This structural model is supported by existing genetic and functional data as well as by targeted mutagenesis of residues predicted to be essential for overall structural integrity. Our predicted structure should increase understanding of structure-function relationships in Rev and may provide a basis for the design of new therapies for lentiviral diseases

Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

<p>Abstract</p> <p>Background</p> <p>Many pathogens use a type III secretion system to translocate virulence proteins (called effectors) in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized.</p> <p>Results</p> <p>In this study, we have constructed an accurate approach to predicting secreted virulence effectors from Gram-negative bacteria. This consists of a support vector machine-based discriminant analysis followed by a simple criteria-based filtering. The accuracy was assessed by estimating the average number of true positives in the top-20 ranking in the genome-wide screening. In the validation, 10 sets of 20 training and 20 testing examples were randomly selected from 40 known effectors of <it>Salmonella enterica </it>serovar Typhimurium LT2. On average, the SVM portion of our system predicted 9.7 true positives from 20 testing examples in the top-20 of the prediction. Removal of the N-terminal instability, codon adaptation index and ProtParam indices decreased the score to 7.6, 8.9 and 7.9, respectively. These discrimination features suggested that the following characteristics of effectors had been uncovered: unstable N-terminus, non-optimal codon usage, hydrophilic, and less aliphathic. The secondary filtering process represented by coexpression analysis and domain distribution analysis further refined the average true positive counts to 12.3. We further confirmed that our system can correctly predict known effectors of <it>P. syringae </it>DC3000, strongly indicating its feasibility.</p> <p>Conclusions</p> <p>We have successfully developed an accurate prediction system for screening effectors on a genome-wide scale. We confirmed the accuracy of our system by external validation using known effectors of <it>Salmonella </it>and obtained the accurate list of putative effectors of the organism. The level of accuracy was sufficient to yield candidates for gene-directed experimental verification. Furthermore, new features of effectors were revealed: non-optimal codon usage and instability of the N-terminal region. From these findings, a new working hypothesis is proposed regarding mechanisms controlling the translocation of virulence effectors and determining the substrate specificity encoded in the secretion system.</p

Prediction of backbone dihedral angles and protein secondary structure using support vector machines

<p>Abstract</p> <p>Background</p> <p>The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.</p> <p>Results</p> <p>We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.</p> <p>Conclusions</p> <p>We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at <url>http://comp.chem.nottingham.ac.uk/disspred/</url>.</p