Literature DB >> 18974176

Kinomer v. 1.0: a database of systematically classified eukaryotic protein kinases.

David M A Martin1, Diego Miranda-Saavedra, Geoffrey J Barton.   

Abstract

The regulation of protein function through reversible phosphorylation by protein kinases and phosphatases is a general mechanism controlling virtually every cellular activity. Eukaryotic protein kinases can be classified into distinct, well-characterized groups based on amino acid sequence similarity and function. We recently reported a highly sensitive and accurate hidden Markov model-based method for the automatic detection and classification of protein kinases into these specific groups. The Kinomer v. 1.0 database presented here contains annotated classifications for the protein kinase complements of 43 eukaryotic genomes. These span the taxonomic range and include fungi (16 species), plants (6), diatoms (1), amoebas (2), protists (1) and animals (17). The kinomes are stored in a relational database and are accessible through a web interface on the basis of species, kinase group or a combination of both. In addition, the Kinomer v. 1.0 HMM library is made available for users to perform classification on arbitrary sequences. The Kinomer v. 1.0 database is a continually updated resource where direct comparison of kinase sequences across kinase groups and across species can give insights into kinase function and evolution. Kinomer v. 1.0 is available at http://www.compbio.dundee.ac.uk/kinomer/.

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Year:  2008        PMID: 18974176      PMCID: PMC2686601          DOI: 10.1093/nar/gkn834

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


INTRODUCTION

The regulation of protein function through reversible phosphorylation by protein kinases and phosphatases is a widespread cellular mechanism thought to control virtually every cellular activity (1), and abnormal levels of phosphorylation are known to be responsible for severe diseases (2). Hanks and Hunter were the first to report that sequence similarity of kinase catalytic domains reflects protein kinase function and/or mode of regulation (3,4). Observation of distinct clades where function segregated with sequence similarity allowed Hanks and Hunter to divide the protein kinase superfamily into specific ‘groups’. The currently accepted classification of the eukaryotic protein kinase superfamily considers eight ‘conventional’ protein kinase groups (ePKs) and four ‘atypical’ groups (aPKs) (5,6). Among the ePKs are the AGC group (including cyclic-nucleotide and calcium-phospholipid-dependent kinases, ribosomal S6-phosphorylating kinases, G protein-coupled kinases and all close relatives of these sets); the CAMKs (calmodulin-regulated kinases); the CK1 group (casein kinase 1, and close relatives); the CMGC group (including cyclin-dependent kinases, mitogen-activated protein kinases, glycogen synthase kinases and CDK-like kinases); the RGC group (receptor guanylate cyclase); the STEs (including many kinases functioning in MAP kinase cascades); the TKs (tyrosine kinases) and the TKLs (tyrosine kinase-like kinases). However, there is a significant proportion of kinases which, whilst exhibiting some degree of sequence similarity to the eight groups above, could not be classified easily into particular groups. These form a ninth group called ‘Other’. The aPKs are a small set of protein kinases that do not share clear sequence similarity with ePKs, but have been shown experimentally to have protein kinase activity. The bona fide aPKs (6) are the alpha-kinase group (exemplified by myosin heavy chain kinase of Dictyostelium discoideum), PIKK (phosphatidyl inositol 3′ kinase-related kinases), RIO and PHDK (pyruvate dehydrogenase kinases). The sequencing of complete genomes for many eukaryotic species has allowed the determination and comparison of their complete kinase complements (kinomes). These include the kinomes of Saccharomyces cerevisiae (7), Caenorhabditis elegans (8), Drosophila melanogaster (9), Mus musculus (10), Homo sapiens (5), Dictyostelium discoideum (11), Strongylocentrotus purpuratus (12), Tetrahymena thermophila (13), and the plants Arabidopsis thaliana and Oryza sativa (14). Several parasite kinomes have been determined, including the malaria parasite Plasmodium falciparum (15), its comparison with Plasmodium yoelii (16) and those of the three Trypanosomatid species Leishmania major, Trypanosoma brucei and Trypanosoma cruzi (17). The kinomes of H. sapiens, M. musculus, S. purpuratus, D. melanogaster, C. elegans, S. cerevisiae, D. discoideum and T. thermophila are available through Kinbase (http://www.kinase.com/kinbase/). In particular, the observation that many important protein kinases of parasitic protozoa are significantly dissimilar from their eukaryotic counterparts has raised the prospects for therapeutics based on the selective inhibition of parasitic protein kinases (18–20). We have recently exploited the sequence similarity of protein kinases in developing a multi-level Hidden Markov Model (HMM) library that is capable of classifying protein kinases into their correct functional group (6). The protein kinase HMM library was shown to be three times more sensitive than BLAST for identifying kinase catalytic domains. It was also shown to be more sensitive than a general Pfam model of the kinase catalytic domain, with the added advantage that the HMM library is capable of discriminating among protein kinase groups. The validated HMM library was applied to improve the group-level classification of the S. cerevisiae ePKs from 66.96% to 90.43% by classifying many of the yeast kinases previously assigned to the ‘Other’ group. In this article, we describe the extension of this analysis to the complete classification at the kinase group level of 43 curated eukaryotic kinomes and a web-based resource through which these annotations can be examined. In addition, we provide an interface to the HMM library, allowing for the classification of arbitrary sequences.

MATERIALS AND METHODS

Sequence data sources

The complete translated protein coding sequences were obtained for the fungi Aspergillus fumigatus (21), Aspergillus nidulans (22), Aspergillus niger (23), Aspergillus oryzae (24), Candida glabrata (25), Cryptococcus neoformans (26), Debaryomyces hansenii (25), Kluyveromyces lactis (25), Magnaporthe grisea (27), Neurospora crassa (28), Phanerochaete chrysosporium (29), Ustilago maydis (30) and Yarrowia lipolytica (25). Among the photosynthetic organisms we have included A. thaliana (31), the red alga Cyanidioschyzon merolae (32), the rice species Oryza sativa ssp. Japonica (33), the green algae Ostreococcus lucimarinus (34) and Ostreococcus tauri (35), and the poplar tree Populus trichocarpa (36). The metazoan genomes include the yellow fever mosquito Aedes aegypti (37), the malaria mosquito vector Anopheles gambiae (38), the silkworm Bombyx mori (39), the common dog Canis familiaris (40), the early chordate Ciona intestinalis (41), the chicken Gallus gallus (42), the Rhesus macaque Macaca mulatta (43), the marsupial Monodelphis domestica (Opossum) (44), the fishes medaka Oryzias latipes (45), Takifugu rubripes (46) and Tetraodon nigroviridis (47), the laboratory rat Rattus norvegicus (48) and the chimpanzee Pan troglodytes (49). Finally, we have also included the amoeba Entamoeba histolytica (50), the diatom Thalassiosira pseudonana (51) and the pathogenic protist Trichomonas vaginalis (52). The manually annotated kinomes of Caenorhabditis elegans (8), Dictyostelium discoideum (11), Drosophila melanogaster, Homo sapiens (5) and M. musculus (10) were downloaded from Kinbase (http://www.kinase.com/kinbase/) on 28 September 2008. The manually annotated kinomes of Encephalitozoon cuniculi, Saccharomyces cerevisiae and Schyzosaccharomyces pombe had previously been manually annotated and analysed in detail (53).

Kinase classification

The predicted peptide sequences for each of the genomes were searched individually against the Kinomer v. 1.0 multi-level HMM library (6) with the hmmpfam program of the HMMer package (54). Partial matches to the kinase catalytic domain were excluded through manual curation. Empirical cutoffs for association of kinase matches with each of the specific kinase groups were determined through analysis of the significance scores for the matches of the library HMMs to the well annotated kinases in Kinbase for the organisms H. sapiens, C. elegans, D. melanogaster and S. cerevisiae (6). The highest observed E-value for that group was taken as the cutoff for confident assignment. These are AGC (2.7e−7), CAMK (3.2e−14), CK1 (3.2e−5), CMGC (1.2e−7), RGC (4.8e−5), STE (1.4e−6), TK (1.1e−9), TKL (1.7e−12), Alpha (8.5e−66), PDHK (2.7e−10), PIKK (8.4e−6) and RIO (2.3e−3). Protein kinase catalytic domains that had E-values above this cutoff were automatically classified as belonging to the ‘Other’ group. Table 1 lists the protein kinase complements of the 43 eukaryotic genomes contained in Kinomer v.1.0, split by kinase group. All kinase matches were stored in a relational database, linking the sequence to the library matches and the subsequent assignments to a functional group.
Table 1.

The kinomes of the 43 genomes analysed split into the major kinase groups

Protein kinase groupNumber of predicted peptidesAGCCAMKCK1CMGCRGCSTETKTKLOtherTotal ePKsAlphaPDHKPIKKRIOTotal aPKs
Fungi
Ascomycete fungi
    Aspergillus fumigatus9630202733001310810203418
    Aspergillus nidulans10 7011923227012001710003418
    Aspergillus niger11 2002121344012101611803418
    Aspergillus oryzae12 07418233320131089803418
    Candida glabrata52152530423011001110402518
    Debaryomyces hansenii6319191832301300159103317
    Encephalitozoon cuniculi199745212000152900213
    Kluyveromyces lactis532722223230120089003418
    Magnaporthe grisea11 109211524200101910003306
    Neurospora crassa9822192022101410189503418
    Saccharomyces cerevisiae67172036425014001811702529
    Schyzosaccharomyces pombe50212028526013001710901528
    Yarrowia lipolytica643619192210110047603418
Basidiomycete fungi
    Cryptococcus neoformans6578191942501301990035210
    Phanerochaete chrysosporium10 0483323525016131011603418
    Ustilago maydis6522171921801602108403216
Plants
Streptophytes
    Arabidopsis thaliana30 6907611620119073362586111801438
    Oryza sativa ssp. Japonica66 7107213131147074511791391778048214
    Populus trichocarpa58 036561071996076310331361526017210
Green algae
    Ostreococcus lucimarinus76511624421092111310001517
    Ostreococcus tauri7892151942309213139801416
Red algae
    Cyanidioschyzon merolae5014109216070996201315
Diatoms
    Thalassiosira pseudonana11 390333932408042613702428
Amoebozoa
    Dictyostelium discoideum13 463432753804336927255605213
    Entamoeba histolytica9772374994702971093432100639
Excavates/Trichomonads
    Trichomonas vaginalis59 68115432164131139190868870042244
Metazoans
Arthropods/Nematodes
    Aedes aegypti16 7894835104372635188230046313
    Anopheles gambiae13 13337347316253217719601539
    Bombyx mori21 3022420319618259713101539
    Caenorhabditis elegans27 258384984502731821738416125412
    Drosophila melanogaster20 8154141103862133221122301539
Chordata/Fishes
    Ciona intestinalis19 858717213513438323253842112419
    Oryzias latipes25 107116146169810791355625681255416
    Takifugu rubripes21 974921111310012621135425582166417
    Tetraodon nigroviridis28 00594102127314551085333544155314
Chordata/Birds
    Gallus gallus22 195818914633721175916514639422
Chordata/Mammals
    Canis familiaris25 5599911622989781246114621756422
    Homo sapiens46 70482951268561914816478656320
    Macaca mulatta36 423133153231276102134712777612711232
    Monodelphis domestica32 612126149271131311821367278539810431
    Mus musculus39 667791181167760914916498656320
    Pan troglodytes32 83411613619118597149751773210612331
    Rattus norvegicus33 43812702996701486710484766322
The kinomes of the 43 genomes analysed split into the major kinase groups

User interface

The Kinomer v. 1.0 web server provides a comprehensive search interface for accessing the database. Sequences can be retrieved by kinase group, by species or by a combination of both. A summary table illustrates the quality of match of each sequence to the HMM library, as well as providing direct clickable links to the public databases (Figure 1). In addition, an option is available to allow data sets to be downloaded as FASTA format sequence files. The multiple sequence alignment analysis program Jalview (55) is integrated into the Kinomer v. 1.0 interface and allows visualization of the query results. Kinase sequences retrieved are grouped by type and aligned. Jalview allows colouring of the sequences by protein secondary structural properties or amino acid chemical character and on-the-fly calculation of Neighbour-Joining and average distance phylogenetic trees. The web-applet form of Jalview can launch the full Jalview application via the ‘File->View in Full Application’ option. This gives access to further tools for the generation of multiple sequence alignments by Muscle (56), MAFFT (57,58) or ClustalW (59) and secondary structure prediction by JNet (60,61).
Figure 1.

The precalculated kinomes may be downloaded from the Kinomer v. 1.0 website and select by species, kinase group or a combination of both.

The precalculated kinomes may be downloaded from the Kinomer v. 1.0 website and select by species, kinase group or a combination of both. In addition, a separate web interface allows users to classify arbitrary sequences with the HMM library. This web based tool allows a user to upload a sequence in any of the many sequence formats supported by EMBOSS (62), including the popular FASTA, GCG, PIR and SwissProt (62) formats. This sequence is subjected to basic quality assurance checks before the hmmpfam search job is queued for execution on a multi-node Linux cluster. The user is then provided with a job ID, and the interface is asynchronous, returning a status page to the user which is updated automatically. The user can bookmark the results page and return at a later time. In addition, an optional field allows the user to associate arbitrary comments with their job, a useful feature to allow otherwise similar jobs to be distinguished. There are no additional parameters that are user-selectable. This allows for a clean and straightforward interface form. The results are displayed as a formatted HTML page (Figure 2) with the group classification clearly indicated. This shows to which protein kinase group Kinomer v. 1.0 has assigned the sequence. In addition, alternative assignments are given and a summary of all potential significant matches shown. Kinomer v. 1.0 will typically show matches to many kinase group HMMs spanning several kinase groups. All the top-scoring HMMs for one particular group will be the most significant matches, followed by closely related groups. The detailed alignment for each HMM match is linked further down the screen. As some users may wish for more details, the Kinomer v. 1.0 results page also provides a link to the raw HMMer output.
Figure 2.

Results of searching a peptide sequence for kinase catalytic domains using the Kinomer v. 1.0 HMM library. A list of hits is displayed at the top followed by the alignment of the peptide sequence to the individual sub-group HMMs that constitute the HMM library.

Results of searching a peptide sequence for kinase catalytic domains using the Kinomer v. 1.0 HMM library. A list of hits is displayed at the top followed by the alignment of the peptide sequence to the individual sub-group HMMs that constitute the HMM library.

DISCUSSION

The 43 species considered here span a number of phylogenetic lineages, genome sizes and display a range of adaptations to their environment. The genome-wide kinase group assignments are consistent with our previously published results (6) in that seven protein kinase groups (AGC, CAMK, CK1, CMGC, STE, PIKK and RIO) are present in all species surveyed (Table 1) and some kinases in these groups are likely to be essential. Kinases of the groups RGC, TK, TKL, Alpha and PDHK are late innovations in specific phyla or have been lost secondarily in specific lines of descent. The presence of a discrete number of putative TKs in photosynthetic organisms and the pathogen Entamoeba histolytica suggests that TKs are also likely to have had an ancient origin. This observation has recently been strengthened by the finding of animal-like signalling molecules in the green alga Chlamydomonas reinhardtii (63). These include scavenger receptor cysteine rich (SRCR) and C-type lectin domain (CTLD) proteins, both of which play key roles in the innate immune system of metazoa. The identification of SH2 domain proteins in photosynthetic organisms (63,64) suggests that phosphotyrosine-SH2 domain signalling also has an ancient origin and that important cell signalling and adhesion domains evolved before the divergence of the animal lineage. The observation that many species outside the Opisthokont group lack important kinase groups, as is the case of TKs in Apicomplexa (Miranda-Saavedra, D. et al., manuscript submitted for publication), and which have many lineage-specific groups of kinases, suggests that the group level is the most specific level for the automatic classification of kinomes based on models constructed from sequences outside the taxonomic clade under investigation. With the availability of a number of Deuterostome, Protostome and pre-bilaterian genome sequences, having all kinases belonging to a particular kinase group enables novel analyses to be performed. For example, it is now possible to trace the evolution of receptor tyrosine kinase families and that of their ligands. Since receptor tyrosine kinases are multi-domain proteins, diverging rates of evolution of the various domains, and their incorporation in the receptor molecule in select phylogenetic lineages, is informative of distinct selection pressures and can be informative of newly acquired functions through the acquisition of new ligand-binding domains. This is the case with the Trk family of receptor tyrosine kinases, which encode the neurotrophin receptors [nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3) and neurotrophin-4 (NT-4)]. The neurotrophin receptors are an ancient family whose function has been lost in multiple lineages and the roles of the receptors have been modified over time (65). Kinomer v. 1.0 also includes the manually annotated kinomes of the model fungi S. cerevisiae and S. pombe, and that of the unicellular fungi-like parasite Encephalitozoon cuniculi (53). We have recently shown that the two model fungi share ∼85% of their kinomes (53), a degree of similarity much higher than that previously reported. The kinomes of budding and fission yeasts are therefore a useful dataset for annotating the kinomes of other fungi, among which we have included species of importance in basic and medical research, and in biotechnology. The manually annotated kinomes of C. elegans, D. discoideum, D. melanogaster, H. sapiens and M. musculus, as provided in Kinbase (http://www.kinase.com/kinbase/), have also been included in the Kinomer v. 1.0 database. These will facilitate the manual annotation of other kinomes included in the database and which belong to the same taxonomic clade. The classification of a number of kinases in the kinomes of C. elegans, D. discoideum, D. melanogaster, H. sapiens and M. musculus could be improved as suggested by the Kinomer v. 1.0 HMM group scores. However, careful manual annotation of the kinomes of other species in the same taxonomic clades will be performed in the future to make a more informed decision about the re-classification of such kinases. To our knowledge, Kinomer v. 1.0 is unique in being based on a high-accuracy validated kinase-group classification method (6). Other databases of protein kinases exist, but none of these offer the combination of breadth and accuracy of kinase classification that is present in Kinomer v. 1.0. These include KinMutBase (66), a database of clinically validated mutations in human kinases that lead to disease, and RTK.db (67), a database of receptor tyrosine kinases. The Protein Kinase Resource (68) collates data from several databases and includes a subset of protein kinase 3D structures to produce high-quality multiple structure-based alignments. Kinbase (http://www.kinase.com/kinbase/) contains manually curated kinomes classified according to the Hanks and Hunter classification of protein kinases (4). Although of high quality, Kinbase only contains kinomes for nine species. Finally, KinG (69) includes protein kinases identified in completed genomes that have been classified by a variety of metazoan kinome-based sequence search methods, but do not provide the confidence in kinase classification that is seen in Kinomer v. 1.0. Different eukaryotic lineages possess lineage-specific kinase groups and families that are just beginning to be characterized and which constitute as much as 50% of their kinomes (17). The applicability of the KinG approach to non-metazoan kinases needs further testing. A similar limitation is encountered by the PANTHER (70) database. Although not specific to protein kinases, PANTHER provides an extensive and detailed HMM library for kinase families and sub-families. These family and sub-family HMM libraries are trained on metazoan sequences and thus preclude their use to annotate non-metazoan sequences confidently into kinase families and sub-families which may not exist in non-metazoan species. Kinomer v. 1.0 annotates to the group level only and in our view annotating to the family/sub-family level requires manual curation. In summary, Kinomer v. 1.0 is an easy-to-use interface to a novel database of both manually and automatically annotated kinomes. The availability of 43 eukaryotic kinomes in a relational database allows the easy querying of protein kinases by species and/or protein kinase group. In addition, the Kinomer v. 1.0 website includes a web server interface to the previously validated HMM library for the classification of peptide sequences into protein kinase groups. In the future, Kinomer v. 1.0 will be enhanced with the addition of a number of manually annotated kinomes of fungal, metazoan and photosynthetic organisms (Miranda-Saavedra, D., et al., manuscript in preparation). These will include the kinomes of pathogenic fungi of the Rhizopus and Fusarium geni, and the kinomes of several unicellular and multicellular photosynthetic organisms including diatoms, red, brown and green algae, and vascular plants. Thus, Kinomer v. 1.0 is a useful and developing repository of expert and automatically annotated kinomes.

FUNDING

D.M.S. was a Wellcome Trust Prize Student at the University of Dundee. Funding for open access charge: Wellcome Trust. Conflict of interest statement. None declared.
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Journal:  Nature       Date:  2005-12-22       Impact factor: 49.962

8.  Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus.

Authors:  William C Nierman; Arnab Pain; Michael J Anderson; Jennifer R Wortman; H Stanley Kim; Javier Arroyo; Matthew Berriman; Keietsu Abe; David B Archer; Clara Bermejo; Joan Bennett; Paul Bowyer; Dan Chen; Matthew Collins; Richard Coulsen; Robert Davies; Paul S Dyer; Mark Farman; Nadia Fedorova; Natalie Fedorova; Tamara V Feldblyum; Reinhard Fischer; Nigel Fosker; Audrey Fraser; Jose L García; Maria J García; Arlette Goble; Gustavo H Goldman; Katsuya Gomi; Sam Griffith-Jones; Ryan Gwilliam; Brian Haas; Hubertus Haas; David Harris; H Horiuchi; Jiaqi Huang; Sean Humphray; Javier Jiménez; Nancy Keller; Hoda Khouri; Katsuhiko Kitamoto; Tetsuo Kobayashi; Sven Konzack; Resham Kulkarni; Toshitaka Kumagai; Anne Lafon; Anne Lafton; Jean-Paul Latgé; Weixi Li; Angela Lord; Charles Lu; William H Majoros; Gregory S May; Bruce L Miller; Yasmin Mohamoud; Maria Molina; Michel Monod; Isabelle Mouyna; Stephanie Mulligan; Lee Murphy; Susan O'Neil; Ian Paulsen; Miguel A Peñalva; Mihaela Pertea; Claire Price; Bethan L Pritchard; Michael A Quail; Ester Rabbinowitsch; Neil Rawlins; Marie-Adele Rajandream; Utz Reichard; Hubert Renauld; Geoffrey D Robson; Santiago Rodriguez de Córdoba; Jose M Rodríguez-Peña; Catherine M Ronning; Simon Rutter; Steven L Salzberg; Miguel Sanchez; Juan C Sánchez-Ferrero; David Saunders; Kathy Seeger; Rob Squares; Steven Squares; Michio Takeuchi; Fredj Tekaia; Geoffrey Turner; Carlos R Vazquez de Aldana; Janice Weidman; Owen White; John Woodward; Jae-Hyuk Yu; Claire Fraser; James E Galagan; Kiyoshi Asai; Masayuki Machida; Neil Hall; Bart Barrell; David W Denning
Journal:  Nature       Date:  2005-12-22       Impact factor: 49.962

9.  Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae.

Authors:  James E Galagan; Sarah E Calvo; Christina Cuomo; Li-Jun Ma; Jennifer R Wortman; Serafim Batzoglou; Su-In Lee; Meray Baştürkmen; Christina C Spevak; John Clutterbuck; Vladimir Kapitonov; Jerzy Jurka; Claudio Scazzocchio; Mark Farman; Jonathan Butler; Seth Purcell; Steve Harris; Gerhard H Braus; Oliver Draht; Silke Busch; Christophe D'Enfert; Christiane Bouchier; Gustavo H Goldman; Deborah Bell-Pedersen; Sam Griffiths-Jones; John H Doonan; Jaehyuk Yu; Kay Vienken; Arnab Pain; Michael Freitag; Eric U Selker; David B Archer; Miguel A Peñalva; Berl R Oakley; Michelle Momany; Toshihiro Tanaka; Toshitaka Kumagai; Kiyoshi Asai; Masayuki Machida; William C Nierman; David W Denning; Mark Caddick; Michael Hynes; Mathieu Paoletti; Reinhard Fischer; Bruce Miller; Paul Dyer; Matthew S Sachs; Stephen A Osmani; Bruce W Birren
Journal:  Nature       Date:  2005-12-22       Impact factor: 49.962

10.  The dictyostelium kinome--analysis of the protein kinases from a simple model organism.

Authors:  Jonathan M Goldberg; Gerard Manning; Allen Liu; Petra Fey; Karen E Pilcher; Yanji Xu; Janet L Smith
Journal:  PLoS Genet       Date:  2006-03-31       Impact factor: 5.917

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  38 in total

1.  Uncovering Phosphorylation-Based Specificities through Functional Interaction Networks.

Authors:  Omar Wagih; Naoyuki Sugiyama; Yasushi Ishihama; Pedro Beltrao
Journal:  Mol Cell Proteomics       Date:  2015-11-16       Impact factor: 5.911

2.  Insights into the red algae and eukaryotic evolution from the genome of Porphyra umbilicalis (Bangiophyceae, Rhodophyta).

Authors:  Susan H Brawley; Nicolas A Blouin; Elizabeth Ficko-Blean; Glen L Wheeler; Martin Lohr; Holly V Goodson; Jerry W Jenkins; Crysten E Blaby-Haas; Katherine E Helliwell; Cheong Xin Chan; Tara N Marriage; Debashish Bhattacharya; Anita S Klein; Yacine Badis; Juliet Brodie; Yuanyu Cao; Jonas Collén; Simon M Dittami; Claire M M Gachon; Beverley R Green; Steven J Karpowicz; Jay W Kim; Ulrich Johan Kudahl; Senjie Lin; Gurvan Michel; Maria Mittag; Bradley J S C Olson; Jasmyn L Pangilinan; Yi Peng; Huan Qiu; Shengqiang Shu; John T Singer; Alison G Smith; Brittany N Sprecher; Volker Wagner; Wenfei Wang; Zhi-Yong Wang; Juying Yan; Charles Yarish; Simone Zäuner-Riek; Yunyun Zhuang; Yong Zou; Erika A Lindquist; Jane Grimwood; Kerrie W Barry; Daniel S Rokhsar; Jeremy Schmutz; John W Stiller; Arthur R Grossman; Simon E Prochnik
Journal:  Proc Natl Acad Sci U S A       Date:  2017-07-17       Impact factor: 11.205

3.  Identifying and characterizing a novel protein kinase STK35L1 and deciphering its orthologs and close-homologs in vertebrates.

Authors:  Pankaj Goyal; Antje Behring; Abhishek Kumar; Wolfgang Siess
Journal:  PLoS One       Date:  2009-09-16       Impact factor: 3.240

4.  Meta-Analysis of Arabidopsis thaliana Phospho-Proteomics Data Reveals Compartmentalization of Phosphorylation Motifs.

Authors:  Klaas J van Wijk; Giulia Friso; Dirk Walther; Waltraud X Schulze
Journal:  Plant Cell       Date:  2014-06-03       Impact factor: 11.277

5.  Phosphoproteome Analysis Links Protein Phosphorylation to Cellular Remodeling and Metabolic Adaptation during Magnaporthe oryzae Appressorium Development.

Authors:  William L Franck; Emine Gokce; Shan M Randall; Yeonyee Oh; Alex Eyre; David C Muddiman; Ralph A Dean
Journal:  J Proteome Res       Date:  2015-05-15       Impact factor: 4.466

6.  A genomic-scale artificial microRNA library as a tool to investigate the functionally redundant gene space in Arabidopsis.

Authors:  Felix Hauser; Wenxiao Chen; Ulrich Deinlein; Kenneth Chang; Stephan Ossowski; Joffrey Fitz; Gregory J Hannon; Julian I Schroeder
Journal:  Plant Cell       Date:  2013-08-16       Impact factor: 11.277

7.  Bioinformatic and experimental survey of 14-3-3-binding sites.

Authors:  Catherine Johnson; Sandra Crowther; Margaret J Stafford; David G Campbell; Rachel Toth; Carol MacKintosh
Journal:  Biochem J       Date:  2010-03-15       Impact factor: 3.857

8.  A rapid method for characterization of protein relatedness using feature vectors.

Authors:  Kareem Carr; Eleanor Murray; Ebenezer Armah; Rong L He; Stephen S-T Yau
Journal:  PLoS One       Date:  2010-03-05       Impact factor: 3.240

9.  Classification of protein kinases on the basis of both kinase and non-kinase regions.

Authors:  Juliette Martin; Krishanpal Anamika; Narayanaswamy Srinivasan
Journal:  PLoS One       Date:  2010-09-15       Impact factor: 3.240

10.  Annotation of microsporidian genomes using transcriptional signals.

Authors:  Eric Peyretaillade; Nicolas Parisot; Valérie Polonais; Sébastien Terrat; Jérémie Denonfoux; Eric Dugat-Bony; Ivan Wawrzyniak; Corinne Biderre-Petit; Antoine Mahul; Sébastien Rimour; Olivier Gonçalves; Stéphanie Bornes; Frédéric Delbac; Brigitte Chebance; Simone Duprat; Gaëlle Samson; Michael Katinka; Jean Weissenbach; Patrick Wincker; Pierre Peyret
Journal:  Nat Commun       Date:  2012       Impact factor: 14.919

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