JAK1 Pseudokinase V666G Mutant Dominantly Impairs JAK3 Phosphorylation and IL-2 Signaling.

Overactive Janus kinases (JAKs) are known to drive leukemia, making them well-suited targets for treatment. We sought to identify new JAK-activating mutations and instead found a JAK1-inactivating pseudokinase mutation, V666G. In contrast to other pseudokinase mutations that canonically lead to an active kinase, the JAK1 V666G mutation led to under-activation seen by reduced phosphorylation.

What Makes GPCRs from Different Families Bind to the Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach.

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC.

GPCR-PEnDB: a database of protein sequences and derived features to facilitate prediction and classification of G protein-coupled receptors.

G protein-coupled receptors (GPCRs) constitute the largest group of membrane receptor proteins in eukaryotes. Due to their significant roles in various physiological processes such as vision, smell and inflammation, GPCRs are the targets of many prescription drugs. However, the functional and sequence diversity of GPCRs has kept their prediction and classification based on amino acid sequence data as a challenging bioinformatics problem.

Predicting mucin-type O-Glycosylation using enhancement value products from derived protein features.

Mucin-type O-glycosylation is one of the most common post-translational modifications of proteins. This glycosylation is initiated in the Golgi by the addition of the sugar N-acetylgalactosamine (GalNAc) onto protein Ser and Thr residues by a family of polypeptide GalNAc transferases. In humans there are 20 isoforms that are differentially expressed across tissues that serve multiple important biological roles.

ISOGlyP: de novo prediction of isoform-specific mucin-type O-glycosylation.

Mucin-type O-glycosylation is one of the most common posttranslational modifications of proteins. The abnormal expression of various polypeptide GalNAc-transferases (GalNAc-Ts) which initiate and define sites of O-glycosylation are linked to many cancers and other diseases. Current O-glycosyation prediction programs utilize O-glycoproteomics data obtained without regard to the transferase isoform (s) responsible for the glycosylation.

Bioinformatic prediction of G protein-coupled receptor encoding sequences from the transcriptome of the foreleg, including the Haller's organ, of the cattle tick, Rhipicephalus australis.

The cattle tick of Australia, Rhipicephalus australis, is a vector for microbial parasites that cause serious bovine diseases. The Haller's organ, located in the tick's forelegs, is crucial for host detection and mating. To facilitate the development of new technologies for better control of this agricultural pest, we aimed to sequence and annotate the transcriptome of the R.

Prediction of G protein-coupled receptor encoding sequences from the synganglion transcriptome of the cattle tick, Rhipicephalus microplus.

The cattle tick, Rhipicephalus (Boophilus) microplus, is a pest which causes multiple health complications in cattle. The G protein-coupled receptor (GPCR) super-family presents a candidate target for developing novel tick control methods. However, GPCRs share limited sequence similarity among orthologous family members, and there is no reference genome available for R.

Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce.

Background: Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole.

Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.

Secondary structures of ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Experimental observations and computing limitations suggest that we can approach the secondary structure prediction problem for long RNA sequences by segmenting them into shorter chunks, predicting the secondary structures of each chunk individually using existing prediction programs, and then assembling the results to give the structure of the original sequence. The selection of cutting points is a crucial component of the segmenting step.

Logic minimization and rule extraction for identification of functional sites in molecular sequences.

Background: Logic minimization is the application of algebraic axioms to a binary dataset with the purpose of reducing the number of digital variables and/or rules needed to express it. Although logic minimization techniques have been applied to bioinformatics datasets before, they have not been used in classification and rule discovery problems. In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sites in proteins.

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction.

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses.

Generation of robust CD8+ T-cell responses against subdominant epitopes in conserved regions of HIV-1 by repertoire mining with mimotopes.

HLA-A 0201-restricted virus-specific CD8(+) CTL do not appear to control HIV effectively in vivo. To enhance the immunogenicity of a highly conserved subdominant epitope, TV9 (TLNAWVKVV, p24 Gag(19-27)), mimotopes were designed by screening a large combinatorial nonapeptide library with TV9-specific CTL primed in vitro from healthy donors. A mimic peptide with a low binding affinity to HLA-A 0201, TV9p6 (KINAWIKVV), was studied further.

A Dynamic Programming Algorithm for Finding the Optimal Segmentation of an RNA Sequence in Secondary Structure Predictions.

In this paper, we present a dynamic programming algorithm that runs in polynomial time and allows us to achieve the optimal, non-overlapping segmentation of a long RNA sequence into segments (chunks). The secondary structure of each chunk is predicted independently, then combined with the structures predicted for the other chunks, to generate a complete secondary structure prediction that is thus a combination of local energy minima. The proposed approach not only is more efficient and accurate than other traditionally used methods that are based on global energy minimizations, but it also allows scientists to overcome computing and storage constraints when trying to predict the secondary structure of long RNA sequences.

A 3' terminal stem-loop structure in Nodamura virus RNA2 forms an essential cis-acting signal for RNA replication.

Nodamura virus (NoV; family Nodaviridae) contains a bipartite positive-strand RNA genome that replicates via negative-strand intermediates. The specific structural and sequence determinants for initiation of nodavirus RNA replication have not yet been identified. For the related nodavirus Flock House virus (FHV) undefined sequences within the 3'-terminal 50 nucleotides (nt) of FHV RNA2 are essential for its replication.

RNAVLab: A virtual laboratory for studying RNA secondary structures based on grid computing technology.

As ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation, their secondary structures have been the focus of many recent studies. Despite the computing power of supercomputers, computationally predicting secondary structures with thermodynamic methods is still not feasible when the RNA molecules have long nucleotide sequences and include complex motifs such as pseudoknots. This paper presents RNAVLab (RNA Virtual Laboratory), a virtual laboratory for studying RNA secondary structures including pseudoknots that allows scientists to address this challenge.

PseudoBase++: an extension of PseudoBase for easy searching, formatting and visualization of pseudoknots.

Pseudoknots have been recognized to be an important type of RNA secondary structures responsible for many biological functions. PseudoBase, a widely used database of pseudoknot secondary structures developed at Leiden University, contains over 250 records of pseudoknots obtained in the past 25 years through crystallography, NMR, mutational experiments and sequence comparisons. To promptly address the growing analysis requests of the researchers on RNA structures and bring together information from multiple sources across the Internet to a single platform, we designed and implemented PseudoBase++, an extension of PseudoBase for easy searching, formatting and visualization of pseudoknots.

AT excursion: a new approach to predict replication origins in viral genomes by locating AT-rich regions.

Background: Replication origins are considered important sites for understanding the molecular mechanisms involved in DNA replication. Many computational methods have been developed for predicting their locations in archaeal, bacterial and eukaryotic genomes. However, a prediction method designed for a particular kind of genomes might not work well for another.

Scoring schemes of palindrome clusters for more sensitive prediction of replication origins in herpesviruses.

Many empirical studies show that there are unusual clusters of palindromes, closely spaced direct and inverted repeats around the replication origins of herpesviruses. In this paper, we introduce two new scoring schemes to quantify the spatial abundance of palindromes in a genomic sequence. Based on these scoring schemes, a computational method to predict the locations of replication origins is developed.

Correlating subjective and objective descriptors of ultra high molecular weight wear particles from total joint prostheses.

A total of 750 images of individual ultra-high molecular weight polyethylene (UHMWPE) particles isolated from periprosthetic failed hip, knee, and shoulder arthroplasties were extracted from archival scanning electron micrographs. Particle size and morphology was subsequently analyzed using computerized image analysis software utilizing five descriptors found in ASTM F1877-98, a standard for quantitative description of wear debris. An online survey application was developed to display particle images, and allowed ten respondents to classify particle morphologies according to commonly used terminology as fibers, flakes, or granules.

Nonrandom clusters of palindromes in herpesvirus genomes.

Palindromes are symmetrical words of DNA in the sense that they read exactly the same as their reverse complementary sequences. Representing the occurrences of palindromes in a DNA molecule as points on the unit interval, the scan statistics can be used to identify regions of unusually high concentration of palindromes. These regions have been associated with the replication origins on a few herpesviruses in previous studies.

Palindromes in SARS and Other Coronaviruses.

With the identification of a novel coronavirus associated with the (SARS), computational analysis of its RNA genome sequence is expected to give useful clues to help elucidate the origin, evolution, and pathogenicity of the virus. In this paper, we study the collective counts of palindromes in the SARS genome along with all the completely sequenced coronaviruses. Based on a Markov-chain model for the genome sequence, the mean and standard deviation for the number of palindromes at or above a given length are derived.