Kyungsook Han

Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor

We propose a sequence-based multiple classifier system, i.e., rotation forest, to infer protein-protein interactions (PPIs). Moreover, Moran autocorrelation descriptor is used to code an interaction protein pair. Experimental results on Saccharomyces cerevisiae and Helicobacter pylori datasets show that our approach outperforms those previously published in literature, which demonstrates the effectiveness of the proposed method.

PSIbase: a database of Protein Structural Interactome map (PSIMAP)

UNLABELLED Protein Structural Interactome map (PSIMAP) is a global interaction map that describes domain-domain and protein-protein interaction information for known Protein Data Bank structures. It calculates the Euclidean distance to determine interactions between possible pairs of structural domains in proteins. PSIbase is a database and file server for protein structural interaction information calculated by the PSIMAP algorithm. PSIbase also provides an easy-to-use protein domain assignment module, interaction navigation and visual tools. Users can retrieve possible interaction partners of their proteins of interests if a significant homology assignment is made with their query sequences. AVAILABILITY http://psimap.org and http://psibase.kaist.ac.kr/

HPID: The Human Protein Interaction Database

UNLABELLED The Human Protein Interaction Database (http://www.hpid.org) was designed (1) to provide human protein interaction information pre-computed from existing structural and experimental data, (2) to predict potential interactions between proteins submitted by users and (3) to provide a depository for new human protein interaction data from users. Two types of interaction are available from the pre-computed data: (1) interactions at the protein superfamily level and (2) those transferred from the interactions of yeast proteins. Interactions at the superfamily level were obtained by locating known structural interactions of the PDB in the SCOP domains and identifying homologs of the domains in the human proteins. Interactions transferred from yeast proteins were obtained by identifying homologs of the yeast proteins in the human proteins. For each human protein in the database and each query submitted by users, the protein superfamilies and yeast proteins assigned to the protein are shown, along with their interacting partners. We have also developed a set of web-based programs so that users can visualize and analyze protein interaction networks in order to explore the networks further. AVAILABILITY http://www.hpid.org.

PseudoViewer3: generating planar drawings of large-scale RNA structures with pseudoknots.

MOTIVATION Pseudoknots in RNA structures make visualization of RNA structures difficult. Even if a pseudoknot itself is represented without a crossing, visualization of the entire RNA structure with a pseudoknot often results in a drawing with crossings between the pseudoknot and other structural elements, and requires additional intervention by the user to ensure that the structure graph is overlap-free. Many programs such as web services prefer to obtain an overlap-free graph in one-shot rather than get a graph with overlaps to be edited. There are few programs for visualizing RNA pseudoknots, and PseudoViewer has been the almost only program that automatically draws RNA secondary structures with pseudoknots. The previous version of PseudoViewer visualizes all the known types of RNA pseudoknots as planar drawings, but visualizes some hypothetical pseudoknots as non-planar drawings. RESULTS We developed a new version of PseudoViewer for efficiently visualizing large RNA structures with any types of pseudoknots, both known and hypothetical, as planar drawings in one-shot. It is about 10 times faster than the previous algorithm, and produces a more compact and aesthetic structure drawing. PseudoViewer3 supports both web services and web applications. AVAILABILITY The new version of PseudoViewer, PseudoViewer3, is available at (http://pseudoviewer.inha.ac.kr).

PseudoViewer: web application and web service for visualizing RNA pseudoknots and secondary structures

Visualizing RNA secondary structures and pseudoknot structures is essential to bioinformatics systems that deal with RNA structures. However, many bioinformatics systems use heterogeneous data structures and incompatible software components, so integration of software components (including a visualization component) into a system can be hindered by incompatibilities between the components of the system. This paper presents an XML web service and web application program for visualizing RNA secondary structures with pseudoknots. Experimental results show that the PseudoViewer web service and web application are useful for resolving many problems with incompatible software components as well as for visualizing large-scale RNA secondary structures with pseudoknots of any type. The web service and web application are available at .

A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.

BackgroundGenetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design.ResultsIn this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in S. cerevisiae to validate our methods ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets.ConclusionsWe developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm

PSEUDOVIEWER2: Visualization of RNA pseudoknots of any type.

Visualizing RNA pseudoknot structures is computationally more difficult than depicting RNA secondary structures, because a drawing of a pseudoknot structure is a graph (and possibly a nonplanar graph) with inner cycles within the pseudoknot, and possibly outer cycles formed between the pseudoknot and other structural elements. We previously developed PSEUDOVIEWER for visualizing H-type pseudoknots. PSEUDOVIEWER2 improves on the first version in many ways: (i) PSEUDOVIEWER2 is more general because it can visualize a pseudoknot of any type, including H-type pseudoknots, as a planar graph; (ii) PSEUDOVIEWER2 computes a drawing of RNA structures much more efficiently and is an order of magnitude faster in actual running time; and (iii) PSEUDOVIEWER2 is a web-based application program. Experimental results demonstrate that PSEUDOVIEWER2 generates an aesthetically pleasing drawing of pseudoknots of any type and that the new representation offered by PSEUDOVIEWER2 ensures uniform and clear drawings, with no edge crossing, for all types of pseudoknots. The PSEUDOVIEWER2 algorithm is the first developed for the automatic drawing of RNA secondary structures, including pseudoknots of any type. PSEUDOVIEWER2 is accessible at http://wilab.inha.ac.kr/pseudoviewer2/.

Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns.

Structural analysis of protein–RNA complexes is labor‐intensive, yet provides insight into the interaction patterns between a protein and RNA. As the number of protein–RNA complex structures reported has increased substantially in the last few years, a systematic method is required for automatically identifying interaction patterns. This paper presents a computational analysis of the hydrogen bonds in the most representative set of protein–RNA complexes. The analysis revealed several interesting interaction patterns. (1) While residues in the β‐sheets favored unpaired nucleotides, residues in the helices showed no preference and residues in turns favored paired nucleotides. (2) The backbone hydrogen bonds were more dominant than the base hydrogen bonds in the paired nucleotides, but the reverse was observed in the unpaired nucleotides. (3) The protein–RNA complexes contained more paired nucleotides than unpaired nucleotides, but the unpaired nucleotides were observed more frequently interacting with the proteins. And (4) Arg–U, Thr–A, Lys–A, and Asn–U were the most frequently observed pairs. The interaction patterns discovered from the analysis will provide us with useful information in predicting the structure of the RNA binding protein and the structure of the protein binding RNA.

Prevention of passively transferred experimental autoimmune myasthenia gravis by an in vitro selected RNA aptamer

Myasthenia gravis (MG) and its animal model, experimental autoimmune MG (EAMG), are mainly caused by autoantibodies directed against acetylcholine receptors (AChR) located in the postsynaptic muscle membrane. Previously, we isolated an RNA aptamer with 2′‐fluoropyrimidines using in vitro selection techniques that acted as an effective decoy against both a rat monoclonal antibody called mAb198, which recognizes the main immunogenic region on the AChR, and a significant fraction of patient autoantibodies with MG. To investigate the therapeutic potential of the RNA, we tested the ability of the RNA aptamer to protect the receptors in vivo from mAb198. Clinical symptoms of EAMG in rats engendered by passive transfer of mAb198 were efficiently inhibited by a truncated RNA aptamer that was modified with polyethylene glycol, but not by control scrambled RNA. Moreover, the loss of AChR in the animals induced by the antibody was also significantly blocked with the modified RNA aptamer. These results suggested that RNA aptamers could be applied for antigen‐specific treatment for autoimmune diseases including MG.

Intracellular expression of the T-cell factor-1 RNA aptamer as an intramer

T-cell factor (TCF)-1 protein forms the transcriptional complex with β-catenin and regulates the expression of diverse target genes during early development and carcinogenesis. We have selected previously an RNA aptamer that binds to the DNA-binding domain of TCF-1 and have shown that it interfered with binding of TCF-1 to its specific DNA recognition sequences in vitro. As an approach to modulate the transcription by TCF/β-catenin complex in the cells, we have developed the RNA expression vector for stable expression of RNA aptamer inside of the mammalian cells. High level of RNA was expressed as an intramer in the fusion with the stable RNA transcript. The RNA intramer inhibited TCF/β-catenin transcription activity as shown by luciferase assay. It also modulated the expression of TCF/β-catenin target genes, such as cyclin D1 and matrix metalloproteinase-7, as predicted to be as an effective inhibitor of the TCF function. In addition, it efficiently reduced the growth rate and tumorigenic potential of HCT116 colon cancer cells. Such RNA intramer could lead to valuable gene therapeutics for TCF/β-catenin-mediated carcinogenesis. [Mol Cancer Ther 2006;5(9):2428–34]