J V Hughes

Bio: J V Hughes is an academic researcher. The author has contributed to research in topics: Neutralization & Peptide sequence. The author has an hindex of 1, co-authored 1 publications receiving 1045 citations.

Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide.

Abstract: Comparative surface feature analyses of the VP1 sequences of hepatitis A virus (HAV) and poliovirus type 1 allowed an alignment of the two sequences and an identification of probable HAV neutralization antigenic sites. A synthetic peptide containing the HAV-specific amino acid sequence of one of these sites induced anti-HAV-neutralizing antibodies. It is concluded that a structural homology exists between the two viruses, despite minimal primary sequence conservation.

Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs

Abstract: Scansite identifies short protein sequence motifs that are recognized by modular signaling domains, phosphorylated by protein Ser/Thr- or Tyr-kinases or mediate specific interactions with protein or phospholipid ligands. Each sequence motif is represented as a position-specific scoring matrix (PSSM) based on results from oriented peptide library and phage display experiments. Predicted domain-motif interactions from Scansite can be sequentially combined, allowing segments of biological pathways to be constructed in silico. The current release of Scansite, version 2.0, includes 62 motifs characterizing the binding and/or substrate specificities of many families of Ser/Thr- or Tyr-kinases, SH2, SH3, PDZ, 14-3-3 and PTB domains, together with signature motifs for PtdIns(3,4,5)P(3)-specific PH domains. Scansite 2.0 contains significant improvements to its original interface, including a number of new generalized user features and significantly enhanced performance. Searches of all SWISS-PROT, TrEMBL, Genpept and Ensembl protein database entries are now possible with run times reduced by approximately 60% when compared with Scansite version 1.0. Scansite 2.0 allows restricted searching of species-specific proteins, as well as isoelectric point and molecular weight sorting to facilitate comparison of predictions with results from two-dimensional gel electrophoresis experiments. Support for user-defined motifs has been increased, allowing easier input of user-defined matrices and permitting user-defined motifs to be combined with pre-compiled Scansite motifs for dual motif searching. In addition, a new series of Sequence Match programs for non-quantitative user-defined motifs has been implemented. Scansite is available via the World Wide Web at http://scansite.mit.edu.

Improved method for predicting linear B-cell epitopes.

Abstract: Background: B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes. Results: In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a nonparametric way by constructing ROC-curves. Conclusion: The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at http://www.cbs.dtu.dk/services/BepiPred.

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

Abstract: B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.

TGF-beta receptor-mediated signalling through Smad2, Smad3 and Smad4.

Abstract: Smad family members are newly identified essential intracellular signalling components of the transforming growth factor-beta (TGF-beta) superfamily. Smad2 and Smad3 are structurally highly similar and mediate TGF-beta signals. Smad4 is distantly related to Smads 2 and 3, and forms a heteromeric complex with Smad2 after TGF-beta or activin stimulation. Here we show that Smad2 and Smad3 interacted with the kinase-deficient TGF-beta type I receptor (TbetaR)-I after it was phosphorylated by TbetaR-II kinase. TGF-beta1 induced phosphorylation of Smad2 and Smad3 in Mv1Lu mink lung epithelial cells. Smad4 was found to be constitutively phosphorylated in Mv1Lu cells, the phosphorylation level remaining unchanged upon TGF-beta1 stimulation. Similar results were obtained using HSC4 cells, which are also growth-inhibited by TGF-beta. Smads 2 and 3 interacted with Smad4 after TbetaR activation in transfected COS cells. In addition, we observed TbetaR-activation-dependent interaction between Smad2 and Smad3. Smads 2, 3 and 4 accumulated in the nucleus upon TGF-beta1 treatment in Mv1Lu cells, and showed a synergistic effect in a transcriptional reporter assay using the TGF-beta-inducible plasminogen activator inhibitor-1 promoter. Dominant-negative Smad3 inhibited the transcriptional synergistic response by Smad2 and Smad4. These data suggest that TGF-beta induces heteromeric complexes of Smads 2, 3 and 4, and their concomitant translocation to the nucleus, which is required for efficient TGF-beta signal transduction.

DNASTAR's Lasergene sequence analysis software.

Abstract: Lasergene's eight modules provide tools that enable users to accomplish each step of sequence analysis, from trimming and assembly of sequence data, to gene discovery, annotation, gene product analysis, sequence similarity searches, sequence alignment, phylogenetic analysis, oligonucleotide primer design, cloning strategies, and publication of the results. The Lasergene software suite provides the functions and customization tools needed so that users can perform analyses the software writers never imagined.