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Eunkyoung Jung,
Junhyoung Kim,
Seung-Hoon Choi, Minkyoung Kim,
Hokyoung Rhee,
Jae-Min Shin,
Kihang Choi,
Sang-Kee Kang,
Nam Kyung Lee,
Yun-Jaie Choi,
Dong Hyun Jung
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ABSTRACT: We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.
Journal of Computer-Aided Molecular Design 12/2009; 24(1):49-56. · 3.39 Impact Factor
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ABSTRACT: This study describes the application of a density-based algorithm to clustering small peptide conformations after a molecular dynamics simulation. We propose a clustering method for small peptide conformations that enables adjacent clusters to be separated more clearly on the basis of neighbor density. Neighbor density means the number of neighboring conformations, so if a conformation has too few neighboring conformations, then it is considered as noise or an outlier and is excluded from the list of cluster members. With this approach, we can easily identify clusters in which the members are densely crowded in the conformational space, and we can safely avoid misclustering individual clusters linked by noise or outliers. Consideration of neighbor density significantly improves the efficiency of clustering of small peptide conformations sampled from molecular dynamics simulations and can be used for predicting peptide structures.
Journal of Chemical Information and Modeling 10/2009; 49(11):2528-36. · 4.68 Impact Factor
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Eunkyoung Jung,
Junhyoung Kim, Minkyoung Kim,
Dong Hyun Jung,
Hokyoung Rhee,
Jae-Min Shin,
Kihang Choi,
Sang-Kee Kang,
Min-Kook Kim,
Cheol-Heui Yun,
Yun-Jaie Choi,
Seung-Hoon Choi
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ABSTRACT: Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences.
The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence.
We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties) descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.
BMC Bioinformatics 02/2007; 8:245. · 2.75 Impact Factor
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ABSTRACT: The increase in Candida albicans infections is caused by the increase in therapies resulting in immunocompromised patients. One factor required for C. albicans pathogenicity is the morphological transition from yeast to hypha. The protein profiles of whole extracts from yeasts and hyphae were examined using two-dimensional electrophoresis to identify the proteins related to the morphological transition. Over 900 protein spots were visualized by silver staining and 11 spots were increased more than three-fold reproducibly during hyphal differentiation. Six of the 11 spots were identified by peptide mass fingerprints, of which three represented PRA1, two PHR1 and the last TSA1. Vertical streak patterns of Pra1p and Phr1p indicated that post-translational modifications seem to be caused by variable glycosylation. Comparative proteome analysis between the wild-type and the deletion mutants, CAMB43 (deltapra1) and CAS10 (deltaphr1), further confirmed the identity of PRA1 and PHR1. Interestingly, Pra1p was downregulated in phr1-deleted mutants. Only PHR1 transcription was increased, indicating that PRA1 and TSA1 are controlled at the post-translational level.
Yeast 10/2003; 20(12):1053-60. · 1.89 Impact Factor
