Human protein reference database--2006 update

Mishra, Gopa R.; Suresh, M.; Kumaran, K.; Nandakumar, Kutty Selva; Suresh, Shubha; Bała, Piotr; Shivakumar, K.; Anuradha, N.; Reddy, Raghunath; Raghavan, T. Madhan; Menon, Shalini; Hanumanthu, G.; Gupta, Malaya; Upendran, Sapna; Gupta, Shweta; Mahesh, M. K.; Jacob, Bincy; Mathew, Pinky; Chatterjee, Pritam; Arun, Kumar S.; Sharma, Salil; Chandrika, K N; Deshpande, Nandan P.; Palvankar, Kshitish; Raghavnath, R.; Krishnakanth, R.; Karathia, Hiren; Rekha, B.; Nayak, Rashmi; Vishnupriya, G.; Kumar, Hemant; Nagini, M.; Kumar, Gaurav; Jose, Rojan; Deepthi, P. Naga; Mohan, S. Sujatha; Gandhi, Tejas; Harsha, H. C.; Deshpande, K. S.; Sarker, Malabika; Prasad, T. S. Keshava; Pandey, Akhilesh

doi:10.1093/nar/gkj141

Cited by 529 publications

(442 citation statements)

References 22 publications

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“…We applied seven prediction algorithms to the HDF data in the context of a human PPI network integrated from seven public databases [16,17,18,19,20,21,22] (see ''Data and Algorithms''). The algorithms were the SinkSource algorithm; a variant called SinkSource+ that does not need negative examples; the commonly-used guilt-by-association approach, both with and without negative examples (called Local and Local+ in this work); a method based on Hopfield networks [13]; the FunctionalFlow algorithm [14]; and another flow-based approach called PRINCE [15].…”

Section: Resultsmentioning

confidence: 99%

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Network-Based Prediction and Analysis of HIV Dependency Factors

Murali

Dyer²,

Badger

et al. 2012

Lecture Notes in Computer Science

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HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.

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Section: Resultsmentioning

confidence: 99%

“…We gathered human proteinprotein interaction data from seven public databases, BIND, DIP, HPRD, IntAct, MINT, MIPS, and Reactome [16,17,18,19,20,21,22]. After removing duplicate interactions and selfinteractions, we obtained a total of 71,461 interactions involving 9,595 proteins.…”

Section: Datasets Usedmentioning

confidence: 99%

Network-Based Prediction and Analysis of HIV Dependency Factors

Murali

Dyer²,

Badger

et al. 2012

Lecture Notes in Computer Science

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show abstract

“…Alternatively, more simplified schemas that only represent the binary relationships between interacting components have been developed. Some leading databases that follow this approach are BIND, 56 HPRD, 50 MINT, 57 IntAct, 58 DIP, 59,60 KEGG, 61 and PPID. 62 Most of these databases describe mainly binding interactions and yield undirected graphs, wherein the direction of the flow of information is not specified.…”

Section: Databases/network Of Mammalian Protein-protein and Ligand-pmentioning

confidence: 99%

“…Figure 2 shows a protein-protein and ligand-protein interaction network map made of interactions from three databases: a database of mammalian cell-signaling interactions in CA1 neurons we developed manually, 52 BIND human protein-protein interactions dataset, 55 human protein reference database HPRD, 50 and PPID. 62 The chromosome 21 genes that appear in those databases were pulled out of the network map and are highlighted in orange.…”

Section: Databases/network Of Mammalian Protein-protein and Ligand-pmentioning

confidence: 99%

The cognitive phenotype of Down syndrome: Insights from intracellular network analysis

2006

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Summary: Down syndrome (DS) is caused by trisomy of chromosome 21. All individuals with DS exhibit some level of cognitive dysfunction. It is generally accepted that these abnormalities are a result of the upregulation of genes encoded by chromosome 21. Many chromosome 21 proteins are known or predicted to function in critical neurological processes, but typically they function as modulators of these processes, not as key regulators. Thus, upregulation in DS is expected to cause only modest perturbations of normal processes. Systematic approaches such as intracellular network construction and analysis have not been generally applied in DS research. Networks can be assembled from highthroughput experiments or by text-mining of experimental literature. We survey some new developments in constructing such networks, focusing on newly developed network analysis methodologies. We propose how these methods could be integrated with creation and manipulation of mouse models of DS to advance our understanding of the perturbed cell signaling pathways in DS. This understanding could lead to potential therapeutics.

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“…1 depicts the analytical flowchart including data collection, features investigation, model training and evaluation, and independent testing. The experimentally verified phosphorylation sites are mainly extracted from dbPTM [24], [25] which has integrated the data from version 9.0 of Phospho.ELM [26], release 20120711 of UniProtKB [27], release 20120730 of PhosphoSitePlus [28], version 1.0 of PHOSIDA [29], version 1.1 of SysPTM [30] and version 9.0 of HPRD [31]. In this work, the data set extracted from Phospho.ELM and UniProtKB is regarded as the training set for sequenced and structural investigation of phosphorylated substrate sites.…”

Section: Introductionmentioning

confidence: 99%

A New Scheme to Predict Kinase-Specific Phosphorylation Sites on Protein Three-Dimensional Structures

Su¹,

Huang²,

Tung³

et al. 2013

IJBBB

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Abstract-Dueto the high-throughput of mass spectrometry-based phosphoproteomics experiment, the desire to annotate the catalytic kinases for in vivo phosphorylation sites has motivated. Many researches are undertaken to develop a computational method for the identification of kinase-specific phosphorylation sites using linear amino acid sequences. With an increasing interest in the structural environment of protein phosphorylation sites, herein, a new scheme has been developed for identifying kinase-specific phosphorylation sites on protein three-dimensional (3D) structures. For a large-scale investigation on 3D structures, all of the experimental phosphorylation sites are mapped to the protein entries of Protein Data Bank by sequence identity. In this work, a support vector machine (SVM) is applied to generate the predictive model learned from the information of spatial amino acid composition and structural alphabet. After the cross-validation evaluation, most of the kinase-specific models trained with the consideration of structural information outperform the models considering only the sequence information. Moreover, the independent testing set which is not included in training set has demonstrated that the proposed method could provide a stable performance. This study has demonstrated that the consideration of spatial context could improve the predictive performance compared to the model only considering the local sequence motifs. IndexTerms-Phosphorylation, protein kinase, three-dimensional structure, structural alphabet, spatial amino acid composition.

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Human protein reference database--2006 update

Cited by 529 publications

References 22 publications

Network-Based Prediction and Analysis of HIV Dependency Factors

Network-Based Prediction and Analysis of HIV Dependency Factors

The cognitive phenotype of Down syndrome: Insights from intracellular network analysis

A New Scheme to Predict Kinase-Specific Phosphorylation Sites on Protein Three-Dimensional Structures

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