The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Richard Simon - One of the best experts on this subject based on the ideXlab platform.
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Candidate epitope identification using Peptide Property models: application to cancer immunotherapy.
Methods (San Diego Calif.), 2004Co-Authors: Myong-hee Sung, Richard SimonAbstract:Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic Peptide-MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding Peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on Peptide Property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for Peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.
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Genomewide conserved epitope profiles of HIV-1 predicted by biophysical properties of MHC binding Peptides.
Journal of computational biology : a journal of computational molecular cell biology, 2004Co-Authors: Myong-hee Sung, Richard SimonAbstract:We propose a new method for predicting MHC binding of Peptides using biophysical parameters of the constituent amino acids. Unlike conventional matrix-based methods, our method does not assume independent binding of the individual side chains and uses a model that simultaneously represents all the residues. The model discovers the quantified 9-mer "Property model" within the longer Peptides that are most common among binders. Prediction for a new Peptide is based on its statistical "distance" from the extracted Peptide Property model. MHC-specific Peptide Property models were constructed from compiled binder/nonbinder data using this method. We report the results of cross-validation of the prediction method and comparison with other methods. The comparison suggests that our method performs substantially better for some MHC class II molecules and equally well for other MHC types. To demonstrate large-scale utility, 30 HIV-1 reference genomes covering diverse subtypes were analyzed. Regions that are likely ...
Myong-hee Sung - One of the best experts on this subject based on the ideXlab platform.
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Candidate epitope identification using Peptide Property models: application to cancer immunotherapy.
Methods (San Diego Calif.), 2004Co-Authors: Myong-hee Sung, Richard SimonAbstract:Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic Peptide-MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding Peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on Peptide Property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for Peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.
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Genomewide conserved epitope profiles of HIV-1 predicted by biophysical properties of MHC binding Peptides.
Journal of computational biology : a journal of computational molecular cell biology, 2004Co-Authors: Myong-hee Sung, Richard SimonAbstract:We propose a new method for predicting MHC binding of Peptides using biophysical parameters of the constituent amino acids. Unlike conventional matrix-based methods, our method does not assume independent binding of the individual side chains and uses a model that simultaneously represents all the residues. The model discovers the quantified 9-mer "Property model" within the longer Peptides that are most common among binders. Prediction for a new Peptide is based on its statistical "distance" from the extracted Peptide Property model. MHC-specific Peptide Property models were constructed from compiled binder/nonbinder data using this method. We report the results of cross-validation of the prediction method and comparison with other methods. The comparison suggests that our method performs substantially better for some MHC class II molecules and equally well for other MHC types. To demonstrate large-scale utility, 30 HIV-1 reference genomes covering diverse subtypes were analyzed. Regions that are likely ...
Rouini Mahsa - One of the best experts on this subject based on the ideXlab platform.
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The identification of gene coding cyclotide from viola species and investigation of antimicrobial activities of extracted cyclotides
2017Co-Authors: Zarabi Mahboube, Roshan Akram, Asgarani Ezat, Pakdel Mona, Rouini MahsaAbstract:Background and aim:Cyclotides are plant polyPeptides characterized by their unique cyclic cysteine knot structural motif. They are present in many plants from the Violaceae, Rubiaceae and Cucurbitaceae families. But they also have variety of biological activities, including anti-HIV, antimicrobial and cytotoxic activities. Because of their exceptional stability, they have attracted interest as a potential starting point material for protein engineering and drug design. Materials and Methods: The aim of this study is identification of genes encoding cyclotides from V. odorata, V. occulta, and V. ignobilis. We also study antimicrobial effect of cyclotides extracted from V. ignobilis. To reach this aim extraction of cyclotide was done by fractionation and solid phase extraction methods. Results: Findings indicate the presence of the cyclotide gene in all three plant species which studied. Examination of antimicrobial effect of partial purified cyclotide, defined S. aureus is the most susceptible bacterium among human pathogenic and X. oryzea is the most susceptible bacterium among all of studied bacteria. Conclusion: In general, it seems that it can be used cyclotide Peptide in most of the viola species because of their anti-Peptide Property in the pharmaceutical industry. Study of cyclotide sequences shows that despite of existence of conserved amino acids in the most of cyclotides, the differences in performance are due to sequence variation
Zarabi Mahboube - One of the best experts on this subject based on the ideXlab platform.
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The identification of gene coding cyclotide from viola species and investigation of antimicrobial activities of extracted cyclotides
2017Co-Authors: Zarabi Mahboube, Roshan Akram, Asgarani Ezat, Pakdel Mona, Rouini MahsaAbstract:Background and aim:Cyclotides are plant polyPeptides characterized by their unique cyclic cysteine knot structural motif. They are present in many plants from the Violaceae, Rubiaceae and Cucurbitaceae families. But they also have variety of biological activities, including anti-HIV, antimicrobial and cytotoxic activities. Because of their exceptional stability, they have attracted interest as a potential starting point material for protein engineering and drug design. Materials and Methods: The aim of this study is identification of genes encoding cyclotides from V. odorata, V. occulta, and V. ignobilis. We also study antimicrobial effect of cyclotides extracted from V. ignobilis. To reach this aim extraction of cyclotide was done by fractionation and solid phase extraction methods. Results: Findings indicate the presence of the cyclotide gene in all three plant species which studied. Examination of antimicrobial effect of partial purified cyclotide, defined S. aureus is the most susceptible bacterium among human pathogenic and X. oryzea is the most susceptible bacterium among all of studied bacteria. Conclusion: In general, it seems that it can be used cyclotide Peptide in most of the viola species because of their anti-Peptide Property in the pharmaceutical industry. Study of cyclotide sequences shows that despite of existence of conserved amino acids in the most of cyclotides, the differences in performance are due to sequence variation
Mircea V. Diudea - One of the best experts on this subject based on the ideXlab platform.
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Peptide Property modeling by Cluj indices.
SAR and QSAR in environmental research, 2001Co-Authors: Dorina M. Opris, Mircea V. DiudeaAbstract:Abstract The novel Cluj Property indices are used for modeling the biological properties of diPeptides: the ACE inhibition activity of a set of 58 diPeptides and the bitter tasting activity of a set of 48 diPeptides, taken from the literature. The results are compared to those reported in some previous works.