Statistical Models for Detecting Differential Chromatin Interactions Mediated by a Protein

Chromatin interactions mediated by a protein of interest are of great scientific interest. Recent studies show that protein-mediated chromatin interactions can have different intensities in different types of cells or in different developmental stages of a cell. Such differences can be associated with a disease or with the development of a cell. Thus, it is of great importance to detect protein-mediated chromatin interactions with different intensities in different cells. A recent molecular technique, Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET), which uses formaldehyde cross-linking and paired-end sequencing, is able to detect genome-wide chromatin interactions mediated by a protein of interest. Here we proposed two models (One-Step Model and Two-Step Model) for two sample ChIA-PET count data (one biological replicate in each sample) to identify differential chromatin interactions mediated by a protein of interest. Both models incorporate the data dependency and the extent to which a fragment pair is related to a pair of DNA loci of interest to make accurate identifications. The One-Step Model makes use of the data more efficiently but is more computationally intensive. An extensive simulation study showed that the models can detect those differentially interacted chromatins and there is a good agreement between each classification result and the truth. Application of the method to a two-sample ChIA-PET data set illustrates its utility. The two models are implemented as an R package MDM (available at http://www.stat.osu.edu/~statgen/SOFTWARE/MDM).     

Soap-HT-BLAST: high throughput BLAST based on Web services

SUMMARY: A high throughput Basic Local Alignment Search Tool (BLAST) system based on Web services is implemented. It provides an alternative BLAST service and allows users to perform multiple BLAST queries at one run in a distributed, parallel environment through the Internet. AVAILABILITY: It is available at http://mammoth.bii.a-star.edu.sg/webservices/htblast/index.html and at http://www.bii.a-star.edu.sg/jiren/download.html     

Heterogeneity detector: finding heterogeneous positions in Phred/Phrap assemblies

A modification to Phred and program to detect heterogeneous positions, which is particularly useful in the identification of mutations and other abnormalities in Phred/Phrap genome assemblies. AVAILABILITY: The package is made available at http://glscompute.gis.a-star.edu.sg/~charlie/DHetero.html     

BEID: Database for sequence-structure-function information on antigen-antibody interactions

The B-cell Epitope Interaction Database (BEID; http://datam.i2r.a-star.edu.sg/BEID) is an open-access database describing sequence-structure-function information on immunoglobulin (Ig)-antigen interactions. The current version of the database contains 164 antigens, 126 Ig and 189 Ig-antigen complexes extracted from the Protein Data Bank (PDB). Each entry is manually verified, classified, and analyzed for intermolecular interactions between antigens and the corresponding bound Ig molecules. Ig-antigen interaction information that is stored in BEID includes solvent accessibility, hydrogen bonds, non-hydrogen bonds, gap volume, gap index, interface area and contact residues. The database can be searched with a user-friendly search tool and schematic diagrams for Ig-antigen interactions are available for download in PDF format. The ultimate purpose of BEID is to enhance the understanding of the rules of engagement between antigen and the corresponding bound Ig molecules. It is also a precious data source for developing computational predictors for B-cell epitopes.     

Benchmarking Human Protein Complexes to Investigate Drug-Related Systems and Evaluate Predicted Protein Complexes

Protein complexes are key entities to perform cellular functions. Human diseases are also revealed to associate with some specific human protein complexes. In fact, human protein complexes are widely used for protein function annotation, inference of human protein interactome, disease gene prediction, and so on. Therefore, it is highly desired to build an up-to-date catalogue of human complexes to support the research in these applications. Protein complexes from different databases are as expected to be highly redundant. In this paper, we designed a set of concise operations to compile these redundant human complexes and built a comprehensive catalogue called CHPC2012 (Catalogue of Human Protein Complexes). CHPC2012 achieves a higher coverage for proteins and protein complexes than those individual databases. It is also verified to be a set of complexes with high quality as its co-complex protein associations have a high overlap with protein-protein interactions (PPI) in various existing PPI databases. We demonstrated two distinct applications of CHPC2012, that is, investigating the relationship between protein complexes and drug-related systems and evaluating the quality of predicted protein complexes. In particular, CHPC2012 provides more insights into drug development. For instance, proteins involved in multiple complexes (the overlapping proteins) are potential drug targets; the drug-complex network is utilized to investigate multi-target drugs and drug-drug interactions; and the disease-specific complex-drug networks will provide new clues for drug repositioning. With this up-to-date reference set of human protein complexes, we believe that the CHPC2012 catalogue is able to enhance the studies for protein interactions, protein functions, human diseases, drugs, and related fields of research. CHPC2012 complexes can be downloaded from http://www1.i2r.a-star.edu.sg/xlli/CHPC2012/CHPC2012.htm.     

Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins

Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg.     

Investigation of silent information regulator 1 (Sirt1) agonists from Traditional Chinese Medicine

Silent information regulator 1 (Sirt1), a class III nicotinamide adenine dinucleotide dependent histone deacetylases, is important in cardioprotection, neuroprotection, metabolic disease, calorie restriction, and diseases associated with aging. Traditional Chinese Medicine (TCM) compounds from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) were employed for screening potent Sirt1 agonists, and molecular dynamics (MD) simulation was implemented to simulate ligand optimum docking poses and protein structure under dynamic conditions. TCM compounds such as (S)-tryptophan-betaxanthin, 5-O-feruloylquinic acid, and RosA exhibited good binding affinity across different computational methods, and their drug-like potential were validated by MD simulation. Docking poses indicate that the carboxylic group of the three candidates generated H-bonds with residues in the protein chain from Ser441 to Lys444 and formed H-bond, π–cation interactions, or hydrophobic contacts with Phe297 and key active residue, His363. During MD, stable π–cation interactions with residues Phe273 or Arg274 were formed by (S)-tryptophan-betaxanthin and RosA. All candidates were anchored to His363 by stable π- or H-bonds. Hence, we propose (S)-tryptophan-betaxanthin, 5-O-feruloylquinic acid, and RosA as potential lead compounds that can be further tested in drug development process for diseases associated with aging

An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:28     

Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures

SUMMARY: Dragon Promoter Mapper (DPM) is a tool to model promoter structure of co-regulated genes using methodology of Bayesian networks. DPM exploits an exhaustive set of motif features (such as motif, its strand, the order of motif occurrence and mutual distance between the adjacent motifs) and generates models from the target promoter sequences, which may be used to (1) detect regions in a genomic sequence which are similar to the target promoters or (2) to classify other promoters as similar or not to the target promoter group. DPM can also be used for modelling of enhancers and silencers. AVAILABILITY: http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/ CONTACT: vlad@sanbi.ac.za SUPPLEMENTARY INFORMATION: Manual for using DPM web server is provided at http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/html/manual/manual.htm.     

Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling

Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.

Availability and Implementation

Our analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.     

A possible strategy against head and neck cancer: in silico investigation of three-in-one inhibitors

Overexpression of epidermal growth factor receptor (EGFR), Her2, and uroporphyrinogen decarboxylase (UROD) occurs in a variety of malignant tumor tissues. UROD has potential to modulate tumor response of radiotherapy for head and neck cancer, and EGFR and Her2 are common drug targets for the treatment of head and neck cancer. This study attempts to find a possible lead compound backbone from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) for EGFR, Her2, and UROD proteins against head and neck cancer using computational techniques. Possible traditional Chinese medicine (TCM) lead compounds had potential binding affinities with EGFR, Her2, and UROD proteins. The candidates formed stable interactions with residues Arg803, Thr854 in EGFR, residues Thr862, Asp863 in Her2 protein, and residues Arg37, Arg41 in UROD protein, which are key residues in the binding or catalytic domain of EGFR, Her2, and UROD proteins. Thus, the TCM candidates indicated a possible molecule backbone for evolving potential inhibitors for three drug target proteins against head and neck cancer.

An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:35     

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

This is a PLOS Computational Biology Software paper.

     

Systematic deletion analysis of fission yeast protein kinases

Eukaryotic protein kinases are key molecules mediating signal transduction that play a pivotal role in the regulation of various biological processes, including cell cycle progression, cellular morphogenesis, development, and cellular response to environmental changes. A total of 106 eukaryotic protein kinase catalytic-domain-containing proteins have been found in the entire fission yeast genome, 44% (or 64%) of which possess orthologues (or nearest homologues) in humans, based on sequence similarity within catalytic domains. Systematic deletion analysis of all putative protein kinase-encoding genes have revealed that 17 out of 106 were essential for viability, including three previously uncharacterized putative protein kinases. Although the remaining 89 protein kinase mutants were able to form colonies under optimal growth conditions, 46% of the mutants exhibited hypersensitivity to at least 1 of the 17 different stress factors tested. Phenotypic assessment of these mutants allowed us to arrange kinases into functional groups. Based on the results of this assay, we propose also the existence of four major signaling pathways that are involved in the response to 17 stresses tested. Microarray analysis demonstrated a significant correlation between the expression signature and growth phenotype of kinase mutants tested. Our complete microarray data sets are available at http://giscompute.gis.a-star.edu.sg/~gisljh/kinome.     

A mouse protein interactome through combined literature mining with multiple sources of interaction evidence

Protein–protein interactions (PPIs) play crucial roles in a number of biological processes. Recently, protein interaction networks (PINs) for several model organisms and humans have been generated, but few large-scale researches for mice have ever been made neither experimentally nor computationally. In the work, we undertook an effort to map a mouse PIN, in which protein interactions are hidden in enormous amount of biomedical literatures. Following a co-occurrence-based text-mining approach, a probabilistic model—naïve Bayesian was used to filter false-positive interactions by integrating heterogeneous kinds of evidence from genomic and proteomic datasets. A support vector machine algorithm was further used to choose protein pairs with physical interactions. By comparing with the currently available PPI datasets from several model organisms and humans, it showed that the derived mouse PINs have similar topological properties at the global level, but a high local divergence. The mouse protein interaction dataset is stored in the Mouse protein–protein interaction DataBase (MppDB) that is useful source of information for system-level understanding of gene function and biological processes in mammals. Access to the MppDB database is public available at http://bio.scu.edu.cn/mppi.     

Non-Redundant Unique Interface Structures as Templates for Modeling Protein Interactions

Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.     

Expression, purification and structural analysis of the Pyrococcus abyssi RNA binding protein PAB1135

Background

Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions.

Findings

AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in a case-control design. Based on some practical epistatic models, AntEpiSeeker has performed very well.

Conclusions

AntEpiSeeker is a powerful and efficient tool for large-scale association studies and can be downloaded from http://nce.ads.uga.edu/~romdhane/AntEpiSeeker/index.html.     

DNAFSMiner: a web-based software toolbox to recognize two types of functional sites in DNA sequences

SUMMARY: DNAFSMiner (DNA Functional Sites Miner) is a web-based software toolbox to recognize functional sites in nucleic acid sequences. Currently in this toolbox, we provide two software: TIS Miner and Poly(A) Signal Miner. The TIS Miner can be used to predict translation initiation sites in vertebrate DNA/mRNA/cDNA sequences, and the Poly(A) Signal Miner can be used to predict polyadenylation [poly(A)] signals in human DNA sequences. The prediction results are better than those by literature methods on two benchmark applications. This good performance is mainly attributable to our unique learning method. DNAFSMiner is available free of charge for academic and non-profit organizations. AVAILABILITY: http://research.i2r.a-star.edu.sg/DNAFSMiner/ CONTACT: huiqing@i2r.a-star.edu.sg.     

MULTBLAST: A web application for multiple BLAST searches

Basic Local Alignment Search Tool, (BLAST) allows the comparison of a query sequence/s to a database of sequences and identifies those sequences that are similar to the query above a user-defined threshold. We have developed a user friendly web application, MULTBLAST that runs a series of BLAST searches on a user-supplied list of proteins against one or more target protein or nucleotide databases. The application pre-processes the data, launches each individual BLAST search on the University of Nevada, Reno''s-TimeLogic DeCypher® system (available from Active Motif, Inc.) and retrieves and combines all the results into a simple, easy to read output file. The output file presents the list of the query proteins, followed by the BLAST results for the matching sequences from each target database in consecutive columns. This format is especially useful for either comparing the results from the different target databases, or analyzing the results while keeping the identification of each target database separate.

Availability

The application is available at the URLhttp://blastpipe.biochem.unr.edu/     

ELISA: Structure-Function Inferences based on statistically significant and evolutionarily inspired observations

The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, http://romi.bu.edu/elisa) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind.Availability: The database is available at http://romi.bu.edu/elisa.