SVMtm: Support vector machines to predict transmembrane segments

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of similar to99%. This method can be used to complement current transmembrane helix prediction methods and can be Used for consensus analysis of entire proteomes . The predictor is located at http://genet.imb.uq.edu.au/predictors/ SVMtm. (C) 2004 Wiley Periodicals, Inc.

Use of the Vector (TM) scaling unit in supportive periodontal therapy: a subjective patient evaluation

Background: Patient discomfort is one reason for poor compliance with supportive periodontal therapy (SPT). The aim of this study was to compare the levels of discomfort during SPT, using the Vector (TM) system and treatment with a conventional ultrasonic scaler. Methods: Forty-six patients with an SPT programme were debrided using both the Vector (TM) system and a conventional piezo-electric scaler (Sirona (TM)) in a split mouth design. A visual analogue scale was used to evaluate of pain scores upon completion of treatment. A verbal response scale(VRS) was used to assess discomfort, vibration and noise associated with the scaling system, as well as the volume and taste of the coolant used by these systems. Results: Patients instrumented with the Vector (TM) system experienced approximately half the amount of pain compared with the conventional ultrasonic scaling system. The VRS showed that the Vector (TM) system caused less discomfort than the conventional ultrasonic scaling system when assessed for pain, vibration, noise and volume of coolant. These findings were all statistically significant. There was, however, no statistically significant difference between the two systems when assessed for taste. Conclusion: During SPT the Vector (TM) system caused reduced discomforting sensations compared with conventional methods and may be useful in improving compliance with SPT programmes.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

Zero-non-zero patterned vector error correction modeling for I(2) cointegrated time series with applications in testing PPP and stock market relationships

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

Vector Error-Correction Models in a consumer packaged goods category forecasting decision support system

A framework for developing marketing category management decision support systems (DSS) based upon the Bayesian Vector Autoregressive (BVAR) model is extended. Since the BVAR model is vulnerable to permanent and temporary shifts in purchasing patterns over time, a form that can correct for the shifts and still provide the other advantages of the BVAR is a Bayesian Vector Error-Correction Model (BVECM). We present the mechanics of extending the DSS to move from a BVAR model to the BVECM model for the category management problem. Several additional iterative steps are required in the DSS to allow the decision maker to arrive at the best forecast possible. The revised marketing DSS framework and model fitting procedures are described. Validation is conducted on a sample problem.

Eph/Ephrin memhrane proteins: A mammalian expression vector pTIg-BOS-Fc allowing rapid protein purification

There is an urgent need for high purity, single chain, fully functional Eph/ephrin membrane proteins. This report outlines the pTIg-BOS-Fc vector and purification approach resulting in rapid increased production of fully functional single chain extracellular proteins that were isolated with high purity and used in structure-function analysis and pre-clinical studies.

Hepatitis B surface antigen vector delivers protective cytotoxic T-lymphocyte responses to disease-relevant foreign epitopes

Although hepatitis B surface antigen (HBsAg) per se is highly immunogenic, its use as a vector for the delivery of foreign cytotoxic T-lymphocyte (CTL) epitopes has met with little success because of constraints on HBsAg stability and secretion imposed by the insertion of foreign sequence into critical hydrophobic/amphipathic regions. Using a strategy entailing deletion of DNA encoding HBsAg-specific CTL epitopes and replacement with DNA encoding foreign CTL epitopes, we have derived chimeric HBsAg DNA immunogens which elicited effector and memory CTL responses in vitro, and pathogen- and tumor-protective responses in vivo, when the chimeric HBsAg DNAs were used to immunize mice. We further show that HBsAg DNA recombinant for both respiratory syncytial virus and human papillomavirus CTL epitopes elicited simultaneous responses to both pathogens. These data demonstrate the efficacy of HBsAg DNA as a vector for the delivery of disease-relevant protective CTL responses. They also suggest the applicability of the approach of deriving chimeric HBsAg DNA immunogens simultaneously encoding protective CTL epitopes for multiple diseases. The DNAs we tested formed chimeric HBsAg virus-like particles (VLPs). Thus, our results have implications for the development of vaccination strategies using either chimeric HBsAg DNA or VLP vaccines. HBsAg is the globally administered vaccine for hepatitis B virus infection, inviting its usage as a vector for the delivery of immunogens from other diseases.

Is the distribution of Fiji leaf gall in Australian sugarcane explained by variation in the vector Perkinsiella saccharicida?

Fiji leaf gall (FLG) is an important virally induced disease in Australian sugarcane. It is confined to southern canegrowing areas, despite its vector, the delphacid planthopper Perkinsiella saccharicida, occurring in all canegrowing areas of Queensland and New South Wales. This disparity between distributions could be a result of successful containment of the disease through quarantine and/or geographical barriers, or because northern Queensland populations of Perkinsiella may be poorer vectors of the disease. These hypotheses were first tested by investigating variation in the ITS2 region of the rDNA fragment among eastern Australian and overseas populations of Perkinsiella. The ITS2 sequences of the Western Australian P. thompsoni and the Fijian P. vitiensis were distinguishable from those of P. saccharicida and there was no significant variation among the 26P. saccharicida populations. Reciprocal crosses of a northern Queensland and a southern Queensland population of P. saccharicida were fertile, so they may well be conspecific. Single vector transmission experiments showed that a population of P. saccharicida from northern Queensland had a higher vector competency than either of two southern Queensland populations. The frequency of virus acquisition in the vector populations was demonstrated to be important in the vector competency of the planthopper. The proportion of infected vectors that transmitted the virus to plants was not significantly different among the populations tested. This study shows that the absence of FLG from northern Queensland is not due to a lack of vector competency of the northern population of P. saccharicida.

Identification of the dominant translation start site in the attB1 sequence of the pET-DEST42 Gateway vector

Gateway technology is a powerful system for converting a single entry vector into a wide variety of expression vectors. We expressed recombinant influenza matrix protein M1 (FMP), a potent antigen for cytotoxic T cells, using the Gateway vector pET-DEST42 containing the FMP cDNA, and purified the expressed FMP as a single 32 kDa recombinant protein. N-terminal and internal protein sequencing, however, showed that the recombinant FMP contained an extra 10 amino acids fused to the N-terminal of native FMP. Further investigation of the DNA sequence adjacent to the 5'-FMP cDNA indicated that the TTG in the attB1 site (30bp upstream of the ATG in the 5'-FMP cDNA) behaved as a dominant translation start site, resulting in a 10 amino acid extension of the recombinant FMP. Thus, it is possible that recombinant proteins produced by this Gateway vector contain unexpected vector-derived peptides, which may affect experimental outcomes. (c) 2006 Elsevier Inc. All rights reserved.

Molecular systematics of the Philippine malaria vector Anopheles flavirostris

Allozyme and molecular sequence data from the malaria vector Anopheles flavirostris (Ludlow) (Diptera: Culicidae) were analysed from 34 sites throughout the Philippines, including the type locality, to test the hypothesis that this taxon is a single panmictic species. A finer-scaled allozyme study, of mainly Luzon samples, revealed no fixed genetic differences in sympatric sites and only low levels of variation. We obtained data from partial sequences for the internal transcribed spacer 2 (ITS2) (483 bp), the third domain (D3) (330 bp) of the 28S ribosomal DNA subunit and cytochrome c oxidase subunit I (COI) of mitochondrial DNA (261 bp). No sequence variation was observed for ITS2, only a one base pair difference was observed between Philippine and Indonesian D3 sequences and An. flavirostris sequences were unique, confirming their diagnostic value for this taxon. Sixteen COI haplotypes were identified, giving 25 parsimony informative sites. Neighbour-Joining, Maximum Parsimony, Maximum Likelihood and Bayesian phylogenetic analysis of COI sequences for An. flavirostris and outgroup taxa revealed strong branch support for the monophyly of An. flavirostris, thus confirming that Philippine populations of this taxon comprise a single separate species within the Minimus Subgroup of the Funestus Group. Variation in the behaviour of An. flavirostris is likely to be intraspecific rather than interspecific in origin. © 2006 The Royal Entomological Society.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Stability for parametric implicit vector equilibrium problems

In this paper, we consider a class of parametric implicit vector equilibrium problems in Hausdorff topological vector spaces where a mapping f and a set K are perturbed by parameters is an element of and lambda respectively. We establish sufficient conditions for the upper semicontinuity and lower semicontinuity of the solution set mapping S : Lambda(1) x A(2) -> 2(X) for such parametric implicit vector equilibrium problems. (c) 2005 Elsevier Ltd. All rights reserved.