A Keith Dunker

University of Alberta, Edmonton, Alberta, Canada

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Publications (227)837.94 Total impact

  • International Conference on Bioinformatics & Computational Biology, BIOCOMP 2007, Volume II, June 25-28, 2007, Las Vegas Nevada, USA; 01/2007
  • A. Keith Dunker
    International Conference on Bioinformatics & Computational Biology, BIOCOMP 2007, Volume II, June 25-28, 2007, Las Vegas Nevada, USA; 01/2007
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    ABSTRACT: Protein interaction networks display approximate scale-free topology, in which hub proteins that interact with a large number of other proteins determine the overall organization of the network. In this study, we aim to determine whether hubs are distinguishable from other networked proteins by specific sequence features. Proteins of different connectednesses were compared in the interaction networks of Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Homo sapienswith respect to the distribution of predicted structural disorder, sequence repeats, low complexity regions, and chain length. Highly connected proteins ("hub proteins") contained significantly more of, and greater proportion of, these sequence features and tended to be longer overall as compared to less connected proteins. These sequence features provide two different functional means for realizing multiple interactions: (1) extended interaction surface and (2) flexibility and adaptability, providing a mechanism for the same region to bind distinct partners. Our view contradicts the prevailing view that scaling in protein interactomes arose from gene duplication and preferential attachment of equivalent proteins. We propose an alternative evolutionary network specialization process, in which certain components of the protein interactome improved their fitness for binding by becoming longer or accruing regions of disorder and/or internal repeats and have therefore become specialized in network organization.
    Journal of Proteome Research 12/2006; 5(11):2985-95. · 5.06 Impact Factor
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    ABSTRACT: Several proteomic studies in the last decade revealed that many proteins are either completely disordered or possess long structurally flexible regions. Many such regions were shown to be of functional importance, often allowing a protein to interact with a large number of diverse partners. Parallel to these findings, during the last five years structural bioinformatics has produced an explosion of results regarding protein-protein interactions and their importance for cell signaling. We studied the occurrence of relatively short (10-70 residues), loosely structured protein regions within longer, largely disordered sequences that were characterized as bound to larger proteins. We call these regions molecular recognition features (MoRFs, also known as molecular recognition elements, MoREs). Interestingly, upon binding to their partner(s), MoRFs undergo disorder-to-order transitions. Thus, in our interpretation, MoRFs represent a class of disordered region that exhibits molecular recognition and binding functions. This work extends previous research showing the importance of flexibility and disorder for molecular recognition. We describe the development of a database of MoRFs derived from the RCSB Protein Data Bank and present preliminary results of bioinformatics analyses of these sequences. Based on the structure adopted upon binding, at least three basic types of MoRFs are found: alpha-MoRFs, beta-MoRFs, and iota-MoRFs, which form alpha-helices, beta-strands, and irregular secondary structure when bound, respectively. Our data suggest that functionally significant residual structure can exist in MoRF regions prior to the actual binding event. The contribution of intrinsic protein disorder to the nature and function of MoRFs has also been addressed. The results of this study will advance the understanding of protein-protein interactions and help towards the future development of useful protein-protein binding site predictors.
    Journal of Molecular Biology 11/2006; 362(5):1043-59. · 3.91 Impact Factor
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    ABSTRACT: Despite substantial increases in research funding by the pharmaceutical industry, drug discovery rates seem to have reached a plateau or perhaps are even declining, suggesting the need for new strategies. Protein-protein interactions have long been thought to provide interesting drug discovery targets, but the development of small molecules that modulate such interactions has so far achieved a low success rate. In contrast to this historic trend, a few recent successes raise hopes for routinely identifying druggable protein-protein interactions. In this Opinion article, we point out the importance of coupled binding and folding for protein-protein signalling interactions generally, and from this and associated observations, we develop a new strategy for identifying protein-protein interactions that would be particularly promising targets for modulation by small molecules. This novel strategy, based on intrinsically disordered protein, has the potential to increase significantly the discovery rate for new molecule entities.
    Trends in Biotechnology 11/2006; 24(10):435-42. · 9.66 Impact Factor
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    ABSTRACT: Evidence that many protein regions and even entire proteins lacking stable tertiary and/or secondary structure in solution (i.e., intrinsically disordered proteins) might be involved in protein-protein interactions, regulation, recognition, and signal transduction is rapidly accumulating. These signaling proteins play a crucial role in the development of several pathological conditions, including cancer. To test a hypothesis that intrinsic disorder is also abundant in cardiovascular disease (CVD), a data set of 487 CVD-related proteins was extracted from SWISS-PROT. CVD-related proteins are depleted in major order-promoting residues (Trp, Phe, Tyr, Ile, and Val) and enriched in some disorder-promoting residues (Arg, Gln, Ser, Pro, and Glu). The application of a neural network predictor of natural disordered regions (PONDR VL-XT) together with cumulative distribution function (CDF) analysis, charge-hydropathy plot (CH plot) analysis, and alpha-helical molecular recognition feature (alpha-MoRF) indicator revealed that CVD-related proteins are enriched in intrinsic disorder. In fact, the percentage of proteins with 30 or more consecutive residues predicted by PONDR VL-XT to be disordered was 57 +/- 4% for CVD-associated proteins. This value is close that described earlier for signaling proteins (66 +/- 6%) and is significantly larger than the content of intrinsic disorder in eukaryotic proteins from SWISS-PROT (47 +/- 4%) and in nonhomologous protein segments with a well-defined three-dimensional structure (13 +/- 4%). Furthermore, CDF and CH-plot analyses revealed that 120 and 36 CVD-related proteins, respectively, are wholly disordered. This high level of intrinsic disorder could be important for the function of CVD-related proteins and for the control and regulation of processes associated with cardiovascular disease. In agreement with this hypothesis, 198 alpha-MoRFs were predicted in 101 proteins from the CVD data set. A comparison of disorder predictions with the experimental structural and functional data for a subset of the CVD-associated proteins indicated good agreement between predictions and observations. Thus, our data suggest that intrinsically disordered proteins might play key roles in cardiovascular disease.
    Biochemistry 10/2006; 45(35):10448-60. · 3.38 Impact Factor
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    ABSTRACT: It is recognized now that many functional proteins or their long segments are devoid of stable secondary and/or tertiary structure and exist instead as very dynamic ensembles of conformations. They are known by different names including natively unfolded, intrinsically disordered, intrinsically unstructured, rheomorphic, pliable, and different combinations thereof. Many important functions and activities have been associated with these intrinsically disordered proteins (IDPs), including molecular recognition, signaling, and regulation. It is also believed that disorder of these proteins allows function to be readily modified through phosphorylation, acetylation, ubiquitination, hydroxylation, and proteolysis. Bioinformatics analysis revealed that IDPs comprise a large fraction of different proteomes. Furthermore, it is established that the intrinsic disorder is relatively abundant among cancer-related and other disease-related proteins and IDPs play a number of key roles in oncogenesis. There are more than 100 different types of human papillomaviruses (HPVs), which are the causative agents of benign papillomas/warts, and cofactors in the development of carcinomas of the genital tract, head and neck, and epidermis. With respect to their association with cancer, HPVs are grouped into two classes, known as low (e.g., HPV-6 and HPV-11) and high-risk (e.g., HPV-16 and HPV-18) types. The entire proteome of HPV includes six nonstructural proteins [E1, E2, E4, E5, E6, and E7 (the latter two are known to function as oncoproteins in the high-risk HPVs)] and two structural proteins (L1 and L2). To understand whether intrinsic disorder plays a role in the oncogenic potential of different HPV types, we have performed a detailed bioinformatics analysis of proteomes of high-risk and low-risk HPVs with the major focus on E6 and E7 oncoproteins. The results of this analysis are consistent with the conclusion that high-risk HPVs are characterized by the increased amount of intrinsic disorder in transforming proteins E6 and E7.
    Journal of Proteome Research 09/2006; 5(8):1829-42. · 5.06 Impact Factor
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    ABSTRACT: Aggregation of β-Lactoglobulin β-Lg) solutions, with and without sodium polypectate (SPP), was investigated at pH 6.5 and 3.5 by turbidity measurements and gel permeation chromatography during heating at 1°C/min. The ratio of β-Lg:SPP was maintained at 10:1. At pH 6.5, the transition temperature of β-Lg aggregation decreased linearly with the logarithm of β-Lg concentration. Irrespective of β-Lg concentration, SPP did not affect the rate of β-Lg aggregation during heating at pH 6.5. However, SPP influenced the formation of high-molecular-weight (HMW) β-Lg aggregates during heating at pH 6.5 was related to bulk macromolecular concentration. No thermal aggregation transitions were detected for β-Lg solutions at pH 3.5. SPP interacted with β-Lg at pH 3.5 to form a complex that precipitated on heating.
    Journal of Food Science 08/2006; 61(1):69 - 73. · 1.78 Impact Factor
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    ABSTRACT: β-lactoglobulin (β-LG) in the molten globule state induced by high hydrostatic pressure (HHP) at 500 MPa and 50 °C for 32 min exhibited a significant decrease in affinity for retinol and a significant increase in affinity for cis-parinaric acid (CPA) and 1-anilino-naphthalene-8-sulfonate (ANS) compared to native β-LG. The number of β-LG binding sites for retinol and CPA significantly decreased after HHP treatment. The HHP-induced molten globule state of β-LG exhibited less affinity for palmitic acid, capsaicin, or carvacrol ligands than native β-LG, and no detectable specific binding for -ionone, β-ionone, cinnamaldehyde or vanillin flavors. HHP treatment resulted in changes in the hydrophobic calyx and surface hydrophobic sites of β-LG.
    Journal of Food Science 07/2006; 68(2):444 - 452. · 1.78 Impact Factor
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    ABSTRACT: Intrinsic disorder (ID) is highly abundant in eukaryotes, which reflect the greater need for disorder-associated signaling and transcriptional regulation in nucleated cells. Although several well-characterized examples of intrinsically disordered proteins in transcriptional regulation have been reported, no systematic analysis has been reported so far. To test for the general prevalence of intrinsic disorder in transcriptional regulation, we used the predictor of natural disorder regions (PONDR) to analyze the abundance of intrinsic disorder in three transcription factor datasets and two control sets. This analysis revealed that from 94.13 to 82.63% of transcription factors possess extended regions of intrinsic disorder, relative to 54.51 and 18.64% of the proteins in two control datasets, which indicates the significant prevalence of intrinsic disorder in transcription factors. This propensity of transcription factors to intrinsic disorder was confirmed by cumulative distribution function analysis and charge-hydropathy plots. The amino acid composition analysis showed that all three transcription factor datasets were substantially depleted in order-promoting residues and significantly enriched in disorder-promoting residues. Our analysis of the distribution of disorder within the transcription factor datasets revealed that (a) the AT-hooks and basic regions of transcription factor DNA-binding domains are highly disordered; (b) the degree of disorder in transcription factor activation regions is much higher than that in DNA-binding domains; (c) the degree of disorder is significantly higher in eukaryotic transcription factors than in prokaryotic transcription factors; and (d) the level of alpha-MoRF (molecular recognition feature) prediction is much higher in transcription factors. Overall, our data reflected the fact that eukaryotes with well-developed gene transcription machinery require transcription factor flexibility to be more efficient.
    Biochemistry 07/2006; 45(22):6873-88. · 3.38 Impact Factor
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    ABSTRACT: Calmodulin (CaM) signaling involves important, wide spread eukaryotic protein-protein interactions. The solved structures of CaM associated with several of its binding targets, the distinctive binding mechanism of CaM, and the significant trypsin sensitivity of the binding targets combine to indicate that the process of association likely involves coupled binding and folding for both CaM and its binding targets. Here, we use bioinformatics approaches to test the hypothesis that CaM-binding targets are intrinsically disordered. We developed a predictor of CaM-binding regions and estimated its performance. Per residue accuracy of this predictor reached 81%, which, in combination with a high recall/precision balance at the binding region level, suggests high predictability of CaM-binding partners. An analysis of putative CaM-binding proteins in yeast and human strongly indicates that their molecular functions are related to those of intrinsically disordered proteins. These findings add to the growing list of examples in which intrinsically disordered protein regions are indicated to provide the basis for cell signaling and regulation.
    Proteins Structure Function and Bioinformatics 06/2006; 63(2):398-410. · 3.34 Impact Factor
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    ABSTRACT: Alternative splicing of pre-mRNA generates two or more protein isoforms from a single gene, thereby contributing to protein diversity. Despite intensive efforts, an understanding of the protein structure-function implications of alternative splicing is still lacking. Intrinsic disorder, which is a lack of equilibrium 3D structure under physiological conditions, may provide this understanding. Intrinsic disorder is a common phenomenon, particularly in multicellular eukaryotes, and is responsible for important protein functions including regulation and signaling. We hypothesize that polypeptide segments affected by alternative splicing are most often intrinsically disordered such that alternative splicing enables functional and regulatory diversity while avoiding structural complications. We analyzed a set of 46 differentially spliced genes encoding experimentally characterized human proteins containing both structured and intrinsically disordered amino acid segments. We show that 81% of 75 alternatively spliced fragments in these proteins were associated with fully (57%) or partially (24%) disordered protein regions. Regions affected by alternative splicing were significantly biased toward encoding disordered residues, with a vanishingly small P value. A larger data set composed of 558 SwissProt proteins with known isoforms produced by 1,266 alternatively spliced fragments was characterized by applying the pondr vsl1 disorder predictor. Results from prediction data are consistent with those obtained from experimental data, further supporting the proposed hypothesis. Associating alternative splicing with protein disorder enables the time- and tissue-specific modulation of protein function needed for cell differentiation and the evolution of multicellular organisms.
    Proceedings of the National Academy of Sciences 06/2006; 103(22):8390-5. · 9.81 Impact Factor
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    ABSTRACT: Regions of conserved disorder prediction (CDP) were found in protein domains from all available InterPro member databases, although with varying frequency. These CDP regions were found in proteins from all kingdoms of life, including viruses. However, eukaryotes had 1 order of magnitude more proteins containing long disordered regions than did archaea and bacteria. Sequence conservation in CDP regions varied, but was on average slightly lower than in regions of conserved order. In some cases, disordered regions evolve faster than ordered regions, in others they evolve slower, and in the rest they evolve at roughly the same rate. A variety of functions were found to be associated with domains containing conserved disorder. The most common were DNA/RNA binding, and protein binding. Many ribosomal proteins also were found to contain conserved disordered regions. Other functions identified included membrane translocation and amino acid storage for germination. Due to limitations of current knowledge as well as the methodology used for this work, it was not determined whether these functions were directly associated with the predicted disordered region. However, the functions associated with conserved disorder in this work are in agreement with the functions found in other studies to correlate to disordered regions. We have established that intrinsic disorder may be more common in bacterial and archaeal proteins than previously thought, but this disorder is likely to be used for different purposes than in eukaryotic proteins, as well as occurring in shorter stretches of protein. Regions of predicted disorder were found to be conserved within a large number of protein families and domains. Although many think of such conserved domains as being ordered, in fact a significant number of them contain regions of disorder that are likely to be crucial to their functions.
    Journal of Proteome Research 05/2006; 5(4):888-98. · 5.06 Impact Factor
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    ABSTRACT: Many protein regions have been shown to be intrinsically disordered, lacking unique structure under physiological conditions. These intrinsically disordered regions are not only very common in proteomes, but also crucial to the function of many proteins, especially those involved in signaling, recognition, and regulation. The goal of this work was to identify the prevalence, characteristics, and functions of conserved disordered regions within protein domains and families. A database was created to store the amino acid sequences of nearly one million proteins and their domain matches from the InterPro database, a resource integrating eight different protein family and domain databases. Disorder prediction was performed on these protein sequences. Regions of sequence corresponding to domains were aligned using a multiple sequence alignment tool. From this initial information, regions of conserved predicted disorder were found within the domains. The methodology for this search consisted of finding regions of consecutive positions in the multiple sequence alignments in which a 90% or more of the sequences were predicted to be disordered. This procedure was constrained to find such regions of conserved disorder prediction that were at least 20 amino acids in length. The results of this work included 3,653 regions of conserved disorder prediction, found within 2,898 distinct InterPro entries. Most regions of conserved predicted disorder detected were short, with less than 10% of those found exceeding 30 residues in length.
    Journal of Proteome Research 05/2006; 5(4):879-87. · 5.06 Impact Factor
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    ABSTRACT: Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (< or =30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions. We proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (< or = 30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder. The VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at http://www.ist.temple.edu/disprot/predictorVSL2.php.
    BMC Bioinformatics 02/2006; 7:208. · 3.02 Impact Factor
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    K.M. Daily, P. Radivojac, A.K. Dunker
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    ABSTRACT: Post-translational prote in modifications play an important role in many protein path ways and interactions. It has been hypothesized that modifications to prote insoccur in regions that are easily accessible, and many have been determined to belocated with in intrinsically disordered regions. However, identifying precise locations of prote in modifications involve sex pensive and time consuming laboratory work. Thus, automated identification of these sites is helpful. This paper studies methylated proteins and describes methods of building a predictor for arginine and lysine methylation sites using support vector machines. Our results indicate that, based on current data, both arginine and lysine methylation sites are likely to be intrinsically disordered and that the accuracies of methylation site predictions are high enough to be useful for prote in screening and for testing biological hypotheses.
    Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB '05. Proceedings of the 2005 IEEE Symposium on; 12/2005
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    U. Midic, A.K. Dunker, Z. Obradovic
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    ABSTRACT: Motivated by known preferences for certain amino acids in positions around a-helices, we developed neural network-based predictors of both N and C a-helix ends, which achieved about 88% accuracy. We applied a similar approach for predicting the ends of three types of secondary structure segments. The predictors for the ends of H, E and C segments were then used to create input for protein secondary-structure prediction. By incorporating this new type of input, we significantly improved the basic one-stage predictor of protein secondary structure in terms of both per-residue (Q 3 ) accuracy (+0.8%) and segment overlap (SOV 3 ) measure (+1.4).
    Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB '05. Proceedings of the 2005 IEEE Symposium on; 12/2005
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    ABSTRACT: Proteins participate in complex sets of interactions that represent the mechanistic foundation for much of the physiology and function of the cell. These protein-protein interactions are organized into exquisitely complex networks. The architecture of protein-protein interaction networks was recently proposed to be scale-free, with most of the proteins having only one or two connections but with relatively fewer 'hubs' possessing tens, hundreds or more links. The high level of hub connectivity must somehow be reflected in protein structure. What structural quality of hub proteins enables them to interact with large numbers of diverse targets? One possibility would be to employ binding regions that have the ability to bind multiple, structurally diverse partners. This trait can be imparted by the incorporation of intrinsic disorder in one or both partners. To illustrate the value of such contributions, this review examines the roles of intrinsic disorder in protein network architecture. We show that there are three general ways that intrinsic disorder can contribute: First, intrinsic disorder can serve as the structural basis for hub protein promiscuity; secondly, intrinsically disordered proteins can bind to structured hub proteins; and thirdly, intrinsic disorder can provide flexible linkers between functional domains with the linkers enabling mechanisms that facilitate binding diversity. An important research direction will be to determine what fraction of protein-protein interaction in regulatory networks relies on intrinsic disorder.
    FEBS Journal 11/2005; 272(20):5129-48. · 4.25 Impact Factor
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    ABSTRACT: Many protein-protein and protein-nucleic acid interactions involve coupled folding and binding of at least one of the partners. Here, we propose a protein structural element or feature that mediates the binding events of initially disordered regions. This element consists of a short region that undergoes coupled binding and folding within a longer region of disorder. We call these features "molecular recognition elements" (MoREs). Examples of MoREs bound to their partners can be found in the alpha-helix, beta-strand, polyproline II helix, or irregular secondary structure conformations, and in various mixtures of the four structural forms. Here we describe an algorithm that identifies regions having propensities to become alpha-helix-forming molecular recognition elements (alpha-MoREs) based on a discriminant function that indicates such regions while giving a low false-positive error rate on a large collection of structured proteins. Application of this algorithm to databases of genomics and functionally annotated proteins indicates that alpha-MoREs are likely to play important roles protein-protein interactions involved in signaling events.
    Biochemistry 10/2005; 44(37):12454-70. · 3.38 Impact Factor
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    ABSTRACT: The Center for Eukaryotic Structural Genomics (CESG), as part of the Protein Structure Initiative (PSI), has established a high-throughput structure determination pipeline focused on eukaryotic proteins. NMR spectroscopy is an integral part of this pipeline, both as a method for structure determinations and as a means for screening proteins for stable structure. Because computational approaches have estimated that many eukaryotic proteins are highly disordered, about 1 year into the project, CESG began to use an algorithm (the Predictor of Naturally Disordered Regions, PONDR to avoid proteins that were likely to be disordered. We report a retrospective analysis of the effect of this filtering on the yield of viable structure determination candidates. In addition, we have used our current database of results on 70 protein targets from Arabidopsis thaliana and 1 from Caenorhabditis elegans, which were labeled uniformly with nitrogen-15 and screened for disorder by NMR spectroscopy, to compare the original algorithm with 13 other approaches for predicting disorder from sequence. Our study indicates that the efficiency of structural proteomics of eukaryotes can be improved significantly by removing targets predicted to be disordered by an algorithm chosen to provide optimal performance.
    Proteins Structure Function and Bioinformatics 06/2005; 59(3):444-53. · 3.34 Impact Factor

Publication Stats

14k Citations
837.94 Total Impact Points

Institutions

  • 2013
    • University of Alberta
      • Department of Electrical and Computer Engineering
      Edmonton, Alberta, Canada
    • USF Health Byrd Alzheimer's Institute
      Tampa, Florida, United States
    • University of Bristol
      • Department of Computer Science
      Bristol, ENG, United Kingdom
  • 2011–2013
    • University of South Florida
      • • Division of Molecular Medicine
      • • Department of Molecular Medicine
      Tampa, FL, United States
  • 2003–2013
    • Indiana University-Purdue University Indianapolis
      • • Department of Biochemistry and Molecular Biology
      • • Center for Computational Biology and Bioinformatics
      Indianapolis, IN, United States
    • The Rockefeller University
      New York City, New York, United States
  • 2012
    • Kansas State University
      • Department of Biochemistry and Molecular Biophysics
      Manhattan, KS, United States
    • Indiana University-Purdue University School of Medicine
      Indianapolis, Indiana, United States
  • 2010–2011
    • Plant and Food Research
      Окленд, Auckland, New Zealand
  • 1991–2011
    • Washington State University
      • • School of Molecular Biosciences
      • • School of Electrical Engineering and Computer Science
      Pullman, WA, United States
  • 2009–2010
    • University of Missouri
      • Department of Computer Science and IT
      Columbia, MO, United States
    • Harbin Engineering University
      • College of Automation
      Harbin, Heilongjiang Sheng, China
  • 2002–2009
    • Temple University
      • Department of Computer and Information Science
      Philadelphia, Pennsylvania, United States
  • 2008
    • Universität Basel
      Bâle, Basel-City, Switzerland
    • Wright State University
      • Department of Biochemistry and Molecular Biology
      Dayton, OH, United States
  • 2005–2008
    • Indiana University Bloomington
      • Department of Informatics
      Bloomington, IN, United States
  • 2007
    • Indiana University East
      Indiana, United States
  • 2004
    • Amsterdamse Hogeschool voor de Kunsten
      Amsterdamo, North Holland, Netherlands
    • Cornell University
      • Department of Biochemistry
      Ithaca, NY, United States