bluuebunny/crossref_metadata_2025_split huggingface dataset:
Moving Forward and Making a Difference: Research Priorities for the Science of Service
Authors: Ostrom Amy L. , Bitner Mary Jo , Brown Stephen W. , Burkhard Kevin A. , Goul Michael , Smith-Daniels Vicki , Demirkan Haluk , Rabinovich Elliot 
Published: January 2010

Given the significant, sustained growth in services experienced worldwide, Arizona State University’s Center for Services Leadership embarked on an 18-month effort to identify and articulate a set of global, interdisciplinary research priorities focused on the science of service. Diverse participation from academics in a variety of disciplines working in institutions around the world—in collaboration with business executives who lead organizations ranging from small startups to Global 1000 companies—formed the basis for development of the priorities. The process led to the identification of the following 10 overarching research priorities: • Fostering service infusion and growth • Improving well-being through transformative service • Creating and maintaining a service culture • Stimulating service innovation • Enhancing service design • Optimizing service networks and value chains • Effectively branding and selling services • Enhancing the service experience through cocreation • Measuring and optimizing the value of service • Leveraging technology to advance service For each priority, several important and more specific topic areas for service research emerged from the process. The intent is that the priorities will spur service research by shedding light on the areas of greatest value and potential return to academia, business, and government. Through academic, business, and government collaboration, we can enhance our understanding of service and create new knowledge to help tackle the most important opportunities and challenges we face today.
Identification of inhibitors of Plasmodium falciparum phosphoethanolamine methyltransferase using an enzyme-coupled transmethylation assay
Authors: Bobenchik April M , Choi Jae-Yeon , Mishra Arunima , Rujan Iulian N , Hao Bing , Voelker Dennis R , Hoch Jeffrey C , Mamoun Choukri Ben 
Published: January 2010

Abstract

Background
The phosphoethanolamine methyltransferase, PfPMT, of the human malaria parasite Plasmodium falciparum, a member of a newly identified family of phosphoethanolamine methyltransferases (PMT) found solely in some protozoa, nematodes, frogs, and plants, is involved in the synthesis of the major membrane phospholipid, phosphatidylcholine. PMT enzymes catalyze a three-step S-adenosylmethionine-dependent methylation of the nitrogen atom of phosphoethanolamine to form phosphocholine. In P. falciparum, this activity is a limiting step in the pathway of synthesis of phosphatidylcholine from serine and plays an important role in the development, replication and survival of the parasite within human red blood cells.

Results
We have employed an enzyme-coupled methylation assay to screen for potential inhibitors of PfPMT. In addition to hexadecyltrimethylammonium, previously known to inhibit PfPMT, two compounds dodecyltrimethylammonium and amodiaquine were also found to inhibit PfPMT activity in vitro. Interestingly, PfPMT activity was not inhibited by the amodiaquine analog, chloroquine, or other aminoquinolines, amino alcohols, or histamine methyltransferase inhibitors. Using yeast as a surrogate system we found that unlike wild-type cells, yeast mutants that rely on PfPMT for survival were sensitive to amodiaquine, and their phosphatidylcholine biosynthesis was inhibited by this compound. Furthermore NMR titration studies to characterize the interaction between amoidaquine and PfPMT demonstrated a specific and concentration dependent binding of the compound to the enzyme.

Conclusion
The identification of amodiaquine as an inhibitor of PfPMT in vitro and in yeast, and the biophysical evidence for the specific interaction of the compound with the enzyme will set the stage for the development of analogs of this drug that specifically inhibit this enzyme and possibly other PMTs.
Prediction of novel miRNAs and associated target genes in Glycine max
Authors: Joshi Trupti , Yan Zhe , Libault Marc , Jeong Dong-Hoon , Park Sunhee , Green Pamela J , Sherrier D Janine , Farmer Andrew , May Greg , Meyers Blake C , Xu Dong , Stacey Gary 
Published: January 2010

Abstract

Background
Small non-coding RNAs (21 to 24 nucleotides) regulate a number of developmental processes in plants and animals by silencing genes using multiple mechanisms. Among these, the most conserved classes are microRNAs (miRNAs) and small interfering RNAs (siRNAs), both of which are produced by RNase III-like enzymes called Dicers. Many plant miRNAs play critical roles in nutrient homeostasis, developmental processes, abiotic stress and pathogen responses. Currently, only 70 miRNA have been identified in soybean.

Methods
We utilized Illumina's SBS sequencing technology to generate high-quality small RNA (sRNA) data from four soybean (Glycine max) tissues, including root, seed, flower, and nodules, to expand the collection of currently known soybean miRNAs. We developed a bioinformatics pipeline using in-house scripts and publicly available structure prediction tools to differentiate the authentic mature miRNA sequences from other sRNAs and short RNA fragments represented in the public sequencing data.

Results
The combined sequencing and bioinformatics analyses identified 129 miRNAs based on hairpin secondary structure features in the predicted precursors. Out of these, 42 miRNAs matched known miRNAs in soybean or other species, while 87 novel miRNAs were identified. We also predicted the putative target genes of all identified miRNAs with computational methods and verified the predicted cleavage sites in vivo for a subset of these targets using the 5' RACE method. Finally, we also studied the relationship between the abundance of miRNA and that of the respective target genes by comparison to Solexa cDNA sequencing data.

Conclusion
Our study significantly increased the number of miRNAs known to be expressed in soybean. The bioinformatics analysis provided insight on regulation patterns between the miRNAs and their predicted target genes expression. We also deposited the data in a soybean genome browser based on the UCSC Genome Browser architecture. Using the browser, we annotated the soybean data with miRNA sequences from four tissues and cDNA sequencing data. Overlaying these two datasets in the browser allows researchers to analyze the miRNA expression levels relative to that of the associated target genes. The browser can be accessed at http://digbio.missouri.edu/soybean_mirna/.
AntiBP2: improved version of antibacterial peptide prediction
Authors: Lata Sneh , Mishra Nitish K , Raghava Gajendra PS 
Published: January 2010

Abstract

Background
Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using Support Vector Machine (SVM).

Results
During analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the N and C terminus. These observation and increased data of antibacterial peptide in APD encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. First, the binary patterns of the 15 N terminus residues were used for predicting antibacterial peptide using SVM and achieved accuracy of 85.46% with 0.705 Mathew's Correlation Coefficient (MCC). Then we used the binary pattern of 15 C terminus residues and achieved accuracy of 85.05% with 0.701 MCC, latter on we developed prediction method by combining N & C terminus and achieved an accuracy of 91.64% with 0.831 MCC. Finally we developed SVM based model using amino acid composition of whole peptide and achieved 92.14% accuracy with MCC 0.843. In this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. We further classify antibacterial peptides according to their sources and achieved an overall accuracy of 98.95%. We further classify antibacterial peptides in their respective family and got a satisfactory result.

Conclusion
Among antibacterial peptides, there is preference for certain residues at N and C terminus, which helps to discriminate them from non-antibacterial peptides. Amino acid composition of antibacterial peptides helps to demarcate them from non-antibacterial peptide and their further classification in source and family. Antibp2 will be helpful in discovering efficacious antibacterial peptide, which we hope will be helpful against antibiotics resistant bacteria. We also developed user friendly web server for the biological community.
Efficient protein alignment algorithm for protein search
Authors: Lu Zaixin , Zhao Zhiyu , Fu Bin 
Published: January 2010

AbstractBackgroundProteins show a great variety of 3D conformations, which can be used to infer their evolutionary relationship and to classify them into more general groups; therefore protein structure alignment algorithms are very helpful for protein biologists. However, an accurate alignment algorithm itself may be insufficient for effective discovering of structural relationships among tens of thousands of proteins. Due to the exponentially increasing amount of protein structural data, a fast and accurate structure alignment tool is necessary to access protein classification and protein similarity search; however, the complexity of current alignment algorithms are usually too high to make a fully alignment-based classification and search practical.ResultsWe have developed an efficient protein pairwise alignment algorithm and applied it to our protein search tool, which aligns a query protein structure in the pairwise manner with all protein structures in the Protein Data Bank (PDB) to output similar protein structures. The algorithm can align hundreds of pairs of protein structures in one second. Given a protein structure, the tool efficiently discovers similar structures from tens of thousands of structures stored in the PDB always in 2 minutes in a single machine and 20 seconds in our cluster of 6 machines. The algorithm has been fully implemented and is accessible online at our webserver, which is supported by a cluster of computers.ConclusionOur algorithm can work out hundreds of pairs of protein alignments in one second. Therefore, it is very suitable for protein search. Our experimental results show that it is more accurate than other well known protein search systems in finding proteins which are structurally similar at SCOP family and superfamily levels, and its speed is also competitive with those systems. In terms of the pairwise alignment performance, it is as good as some well known alignment algorithms.
Structure prediction for the helical skeletons detected from the low resolution protein density map
Authors: Al Nasr Kamal , Sun Weitao , He Jing 
Published: January 2010

Abstract

Background
The current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 Å resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.

Results
We have developed an approach to predict the 3-dimensional structure for the helical skeletons that can be detected from the low resolution protein density map. This approach does not require the construction of the entire chain and distinguishes the structures based on the conformation of the helices. A test with 35 low resolution density maps shows that the highest ranked structure with the correct topology can be found within the top 1% of the list ranked by the effective energy formed by the helices.

Conclusion
The results in this paper suggest that it is possible to eliminate the great majority of the bad conformations of the helices even without the construction of the entire chain of the protein. For many proteins, the effective contact energy formed by the secondary structures alone can distinguish a small set of likely structures from the pool.
Computational prediction of type III secreted proteins from gram-negative bacteria
Authors: Yang Yang , Zhao Jiayuan , Morgan Robyn L , Ma Wenbo , Jiang Tao 
Published: January 2010

Abstract

Background
Type III secretion system (T3SS) is a specialized protein delivery system in gram-negative bacteria that injects proteins (called effectors) directly into the eukaryotic host cytosol and facilitates bacterial infection. For many plant and animal pathogens, T3SS is indispensable for disease development. Recently, T3SS has also been found in rhizobia and plays a crucial role in the nodulation process. Although a great deal of efforts have been done to understand type III secretion, the precise mechanism underlying the secretion and translocation process has not been fully understood. In particular, defined secretion and translocation signals enabling the secretion have not been identified from the type III secreted effectors (T3SEs), which makes the identification of these important virulence factors notoriously challenging. The availability of a large number of sequenced genomes for plant and animal-associated bacteria demands the development of efficient and effective prediction methods for the identification of T3SEs using bioinformatics approaches.

Results
We have developed a machine learning method based on the N-terminal amino acid sequences to predict novel type III effectors in the plant pathogen Pseudomonas syringae and the microsymbiont rhizobia. The extracted features used in the learning model (or classifier) include amino acid composition, secondary structure and solvent accessibility information. The method achieved a precision of over 90% on P. syringae in a cross validation study. In combination with a promoter screen for the type III specific promoters, this classifier trained on the P. syringae data was applied to predict novel T3SEs from the genomic sequences of four rhizobial strains. This application resulted in 57 candidate type III secreted proteins, 17 of which are confirmed effectors.

Conclusion
Our experimental results demonstrate that the machine learning method based on N-terminal amino acid sequences combined with a promoter screen could prove to be a very effective computational approach for predicting novel type III effectors in gram-negative bacteria. Our method and data are available to the public upon request.
Virtual Screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis
Authors: Garg Aarti , Tewari Rupinder , Raghava Gajendra PS 
Published: January 2010

Abstract

Background
An explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) is a catastrophe, which demands an urgent need to design or develop novel/potent antitubercular agents. The Lysine/DAP biosynthetic pathway is a promising target due its specific role in cell wall and amino acid biosynthesis. Here, we report identification of potential antitubercular candidates targeting Mtb dihydrodipicolinate synthase (DHDPS) enzyme of the pathway using virtual screening protocols.

Results
In the present study, we generated three sets of drug-like molecules in order to screen potential inhibitors against Mtb drug target DHDPS. The first set of compounds was a combinatorial library, which comprised analogues of pyruvate (substrate of DHDPS). The second set of compounds consisted of pyruvate-like molecules i.e. structurally similar to pyruvate, obtained using 3D flexible similarity search against NCI and PubChem database. The third set constituted 3847 anti-infective molecules obtained from PubChem. These compounds were subjected to Lipinski's rule of drug-like five filters. Finally, three sets of drug-like compounds i.e. 4088 pyruvate analogues, 2640 pyruvate-like molecules and 1750 anti-infective molecules were docked at the active site of Mtb DHDPS (PDB code: 1XXX used in the molecular docking calculations) to select inhibitors establishing favorable interactions.

Conclusion
The above-mentioned virtual screening procedures helped in the identification of several potent candidates that possess inhibitory activity against Mtb DHDPS. Therefore, these novel scaffolds/candidates which could have the potential to inhibit Mtb DHDPS enzyme would represent promising starting points as lead compounds and certainly aid the experimental designing of antituberculars in lesser time.
NeMo: Network Module identification in Cytoscape
Authors: Rivera Corban G , Vakil Rachit , Bader Joel S 
Published: January 2010

Abstract

Background
As the size of the known human interactome grows, biologists increasingly rely on computational tools to identify patterns that represent protein complexes and pathways. Previous studies have shown that densely connected network components frequently correspond to community structure and functionally related modules. In this work, we present a novel method to identify densely connected and bipartite network modules based on a log odds score for shared neighbours.

Results
To evaluate the performance of our method (NeMo), we compare it to other widely used tools for community detection including kMetis, MCODE, and spectral clustering. We test these methods on a collection of synthetically constructed networks and the set of MIPS human complexes. We apply our method to the CXC chemokine pathway and find a high scoring functional module of 12 disconnected phospholipase isoforms.

Conclusion
We present a novel method that combines a unique neighbour-sharing score with hierarchical agglomerative clustering to identify diverse network communities. The approach is unique in that we identify both dense network and dense bipartite network structures in a single approach. Our results suggest that the performance of NeMo is better than or competitive with leading approaches on both real and synthetic datasets. We minimize model complexity and generalization error in the Bayesian spirit by integrating out nuisance parameters. An implementation of our method is freely available for download as a plugin to Cytoscape through our website and through Cytoscape itself.
Molecular dynamics simulation studies and in vitro site directed mutagenesis of avian beta-defensin Apl_AvBD2
Authors: Soman Soja Saghar , Sivakumar Krishnankutty Chandrika , Sreekumar Easwaran 
Published: January 2010

Abstract

Background
Defensins comprise a group of antimicrobial peptides, widely recognized as important elements of the innate immune system in both animals and plants. Cationicity, rather than the secondary structure, is believed to be the major factor defining the antimicrobial activity of defensins. To test this hypothesis and to improve the activity of the newly identified avian β-defensin Apl_AvBD2 by enhancing the cationicity, we performed in silico site directed mutagenesis, keeping the predicted secondary structure intact. Molecular dynamics (MD) simulation studies were done to predict the activity. Mutant proteins were made by in vitro site directed mutagenesis and recombinant protein expression, and tested for antimicrobial activity to confirm the results obtained in MD simulation analysis.

Results
MD simulation revealed subtle, but critical, structural variations between the wild type Apl_AvBD2 and the more cationic in silico mutants, which were not detected in the initial structural prediction by homology modelling. The C-terminal cationic 'claw' region, important in antimicrobial activity, which was intact in the wild type, showed changes in shape and orientation in all the mutant peptides. Mutant peptides also showed increased solvent accessible surface area and more number of hydrogen bonds with the surrounding water molecules. In functional studies, the Escherichia coli expressed, purified recombinant mutant proteins showed total loss of antimicrobial activity compared to the wild type protein.

Conclusion
The study revealed that cationicity alone is not the determining factor in the microbicidal activity of antimicrobial peptides. Factors affecting the molecular dynamics such as hydrophobicity, electrostatic interactions and the potential for oligomerization may also play fundamental roles. It points to the usefulness of MD simulation studies in successful engineering of antimicrobial peptides for improved activity and other desirable functions.
Downstream genes of Pax6 revealed by comprehensive transcriptome profiling in the developing rat hindbrain
Authors: Numayama-Tsuruta Keiko , Arai Yoko , Takahashi Masanori , Sasaki-Hoshino Makiko , Funatsu Nobuo , Nakamura Shun , Osumi Noriko 
Published: January 2010

AbstractBackgroundThe transcription factor Pax6 is essential for the development of the central nervous system and it exerts its multiple functions by regulating the expression of downstream target molecules. To screen for genes downstream of Pax6, we performed comprehensive transcriptome profiling analyses in the early hindbrain ofPax6homozygous mutant and wild-type rats using microarrays.ResultsComparison of quadruplicate microarray experiments using two computational methods allowed us to identify differentially expressed genes that have relatively small fold changes or low expression levels. Gene ontology analyses of the differentially expressed molecules demonstrated that Pax6 is involved in various signal transduction pathways where it regulates the expression of many receptors, signaling molecules, transporters and transcription factors. The up- or down-regulation of these genes was further confirmed by quantitative RT-PCR.In situstaining ofFabp7,Dbx1, Unc5h1andCyp26b1mRNAs showed that expression of these transcripts not only overlapped with that ofPax6in the hindbrain of wild-type andPax6heterozygous mutants, but also was clearly reduced in the hindbrain of thePax6homozygous mutant. In addition, thePax6homozygous mutant hindbrain showed thatCyp26b1expression was lacked in the dorsal and ventrolateral regions of rhombomeres 5 and 6, and that the size of rhombomere 5 expanded rostrocaudally.ConclusionsThese results indicate thatUnc5h1andCyp26b1are novel candidates for target genes transactivated by Pax6. Furthermore, our results suggest the interesting possibility that Pax6 regulates anterior-posterior patterning of the hindbrain via activation of Cyp26b1, an enzyme that metabolizes retinoic acid.