A Keith Dunker

Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States

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Publications (259)921.76 Total impact

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    A Keith Dunker, Ariel Fernández
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    ABSTRACT: Natural enzymes were selected to function inside the cell, not in the test tube; therefore, their performance is optimized for the crowded conditions encountered in vivo. Most man-made matrices for enzyme confinement lead to suboptimal catalytic activity. Ackerman and colleagues showed that an entrapping environment consisting of functionalized mesoporous silica actually enhances enzyme activity beyond the test-tube levels of free enzymes in solution. These findings provide an approach for dissecting the effect of various contributors to enzyme activity and thereby provide a means for fine-tuning the entrapping matrices to optimize enzyme performance in a rational way.
    Trends in Biotechnology 06/2007; 25(5):189-90. DOI:10.1016/j.tibtech.2007.03.009 · 10.04 Impact Factor
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    ABSTRACT: Currently, the understanding of the relationships between function, amino acid sequence, and protein structure continues to represent one of the major challenges of the modern protein science. As many as 50% of eukaryotic proteins are likely to contain functionally important long disordered regions. Many proteins are wholly disordered but still possess numerous biologically important functions. However, the number of experimentally confirmed disordered proteins with known biological functions is substantially smaller than their actual number in nature. Therefore, there is a crucial need for novel bionformatics approaches that allow projection of the current knowledge from a few experimentally verified examples to much larger groups of known and potential proteins. The elaboration of a bioinformatics tool for the analysis of functional diversity of intrinsically disordered proteins and application of this data mining tool to >200 000 proteins from the Swiss-Prot database, each annotated with at least one of the 875 functional keywords, was described in the first paper of this series (Xie, H.; Vucetic, S.; Iakoucheva, L. M.; Oldfield, C. J.; Dunker, A. K.; Obradovic, Z.; Uversky, V.N. Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions. J. Proteome Res. 2007, 5, 1882-1898). Using this tool, we have found that out of the 710 Swiss-Prot functional keywords associated with at least 20 proteins, 262 were strongly positively correlated with long intrinsically disordered regions, and 302 were strongly negatively correlated. Illustrative examples of functional disorder or order were found for the vast majority of keywords showing strongest positive or negative correlation with intrinsic disorder, respectively. Some 80 Swiss-Prot keywords associated with disorder- and order-driven biological processes and protein functions were described in the first paper (see above). The second paper of the series was devoted to the presentation of 87 Swiss-Prot keywords attributed to the cellular components, domains, technical terms, developmental processes, and coding sequence diversities possessing strong positive and negative correlation with long disordered regions (Vucetic, S.; Xie, H.; Iakoucheva, L. M.; Oldfield, C. J.; Dunker, A. K.; Obradovic, Z.; Uversky, V. N. Functional anthology of intrinsic disorder. 2. Cellular components, domains, technical terms, developmental processes, and coding sequence diversities correlated with long disordered regions. J. Proteome Res. 2007, 5, 1899-1916). Protein structure and functionality can be modulated by various post-translational modifications or/and as a result of binding of specific ligands. Numerous human diseases are associated with protein misfolding/misassembly/misfunctioning. This work concludes the series of papers dedicated to the functional anthology of intrinsic disorder and describes approximately 80 Swiss-Prot functional keywords that are related to ligands, post-translational modifications, and diseases possessing strong positive or negative correlation with the predicted long disordered regions in proteins.
    Journal of Proteome Research 05/2007; 6(5):1917-32. DOI:10.1021/pr060394e · 5.00 Impact Factor
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    ABSTRACT: Identifying relationships between function, amino acid sequence, and protein structure represents a major challenge. In this study, we propose a bioinformatics approach that identifies functional keywords in the Swiss-Prot database that correlate with intrinsic disorder. A statistical evaluation is employed to rank the significance of these correlations. Protein sequence data redundancy and the relationship between protein length and protein structure were taken into consideration to ensure the quality of the statistical inferences. Over 200,000 proteins from the Swiss-Prot database were analyzed using this approach. The predictions of intrinsic disorder were carried out using PONDR VL3E predictor of long disordered regions that achieves an accuracy of above 86%. Overall, out of the 710 Swiss-Prot functional keywords that were each associated with at least 20 proteins, 238 were found to be strongly positively correlated with predicted long intrinsically disordered regions, whereas 302 were strongly negatively correlated with such regions. The remaining 170 keywords were ambiguous without strong positive or negative correlation with the disorder predictions. These functions cover a large variety of biological activities and imply that disordered regions are characterized by a wide functional repertoire. Our results agree well with literature findings, as we were able to find at least one illustrative example of functional disorder or order shown experimentally for the vast majority of keywords showing the strongest positive or negative correlation with intrinsic disorder. This work opens a series of three papers, which enriches the current view of protein structure-function relationships, especially with regards to functionalities of intrinsically disordered proteins, and provides researchers with a novel tool that could be used to improve the understanding of the relationships between protein structure and function. The first paper of the series describes our statistical approach, outlines the major findings, and provides illustrative examples of biological processes and functions positively and negatively correlated with intrinsic disorder.
    Journal of Proteome Research 05/2007; 6(5):1882-98. DOI:10.1021/pr060392u · 5.00 Impact Factor
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    ABSTRACT: Biologically active proteins without stable ordered structure (i.e., intrinsically disordered proteins) are attracting increased attention. Functional repertoires of ordered and disordered proteins are very different, and the ability to differentiate whether a given function is associated with intrinsic disorder or with a well-folded protein is crucial for modern protein science. However, there is a large gap between the number of proteins experimentally confirmed to be disordered and their actual number in nature. As a result, studies of functional properties of confirmed disordered proteins, while helpful in revealing the functional diversity of protein disorder, provide only a limited view. To overcome this problem, a bioinformatics approach for comprehensive study of functional roles of protein disorder was proposed in the first paper of this series (Xie, H.; Vucetic, S.; Iakoucheva, L. M.; Oldfield, C. J.; Dunker, A. K.; Obradovic, Z.; Uversky, V. N. Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions. J. Proteome Res. 2007, 5, 1882-1898). Applying this novel approach to Swiss-Prot sequences and functional keywords, we found over 238 and 302 keywords to be strongly positively or negatively correlated, respectively, with long intrinsically disordered regions. This paper describes approximately 90 Swiss-Prot keywords attributed to the cellular components, domains, technical terms, developmental processes, and coding sequence diversities possessing strong positive and negative correlation with long disordered regions.
    Journal of Proteome Research 05/2007; 6(5):1899-916. DOI:10.1021/pr060393m · 5.00 Impact Factor
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    ABSTRACT: The recent advances in the prediction of intrinsically disordered proteins and the use of protein disorder prediction in the fields of molecular biology and bioinformatics are reviewed here, especially with regard to protein function. First, a close look is taken at intrinsically disordered proteins and then at the methods used for their experimental characterization. Next, the major statistical properties of disordered regions are summarized, and prediction models developed thus far are described, including their numerous applications in functional proteomics. The future of the prediction of protein disorder and the future uses of such predictions in functional proteomics comprise the last section of this article.
    Biophysical Journal 04/2007; 92(5):1439-56. DOI:10.1529/biophysj.106.094045 · 3.83 Impact Factor
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    ABSTRACT: The Protein Data Bank (PDB) is the preeminent source of protein structural information. PDB contains over 32,500 experimentally determined 3-D structures solved using X-ray crystallography or nuclear magnetic resonance spectroscopy. Intrinsically disordered regions fail to form a fixed 3-D structure under physiological conditions. In this study, we compare the amino-acid sequences of proteins whose structures are determined by X-ray crystallography with the corresponding sequences from the Swiss-Prot database. The analyzed dataset includes 16,370 structures, which represent 18,101 PDB chains and 5,434 different proteins from 910 different organisms (2,793 eukaryotic, 2,109 bacterial, 288 viral, and 244 archaeal). In this dataset, on average, each Swiss-Prot protein is represented by 7 PDB chains with 76% of the crystallized regions being represented by more than one structure. Intriguingly, the complete sequences of only approximately 7% of proteins are observed in the corresponding PDB structures, and only approximately 25% of the total dataset have >95% of their lengths observed in the corresponding PDB structures. This suggests that the vast majority of PDB proteins is shorter than their corresponding Swiss-Prot sequences and/or contain numerous residues, which are not observed in maps of electron density. To determine the prevalence of disordered regions in PDB, the residues in the Swiss-Prot sequences were grouped into four general categories, "Observed" (which correspond to structured regions), "Not observed" (regions with missing electron density, potentially disordered), "Uncharacterized," and "Ambiguous," depending on their appearance in the corresponding PDB entries. This non-redundant set of residues can be viewed as a 'fragment' or empirical domain database that contains a set of experimentally determined structured regions or domains and a set of experimentally verified disordered regions or domains. We studied the propensities and properties of residues in these four categories and analyzed their relations to the predictions of disorder using several algorithms. "Non-observed," "Ambiguous," and "Uncharacterized" regions were shown to possess the amino acid compositional biases typical of intrinsically disordered proteins. The application of four different disorder predictors (PONDR(R) VL-XT, VL3-BA, VSL1P, and IUPred) revealed that the vast majority of residues in the "Observed" dataset are ordered, and that the "Not observed" regions are mostly disordered. The "Uncharacterized" regions possess some tendency toward order, whereas the predictions for the short "Ambiguous" regions are really ambiguous. Long "Ambiguous" regions (>70 amino acid residues) are mostly predicted to be ordered, suggesting that they are likely to be "wobbly" domains. Overall, we showed that completely ordered proteins are not highly abundant in PDB and many PDB sequences have disordered regions. In fact, in the analyzed dataset approximately 10% of the PDB proteins contain regions of consecutive missing or ambiguous residues longer than 30 amino-acids and approximately 40% of the proteins possess short regions (> or =10 and < 30 amino-acid long) of missing and ambiguous residues.
    Journal of biomolecular Structure & Dynamics 03/2007; 24(4):325-42. DOI:10.1080/07391102.2007.10507123 · 2.98 Impact Factor
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    Uros Midic, A Keith Dunker, Zoran Obradovic
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    ABSTRACT: Methods for 3-class secondary-structure prediction are thought to be reaching the highest achievable accuracy. Their accuracy on beta-sheet residue class is considerably lower than for the other two classes. We analysed the relevance of 315 individual input attributes for a predictor with the usual framework of using sequence-profile based data with an input window of fixed size. We propose two alternative knowledge representations with significantly smaller sets of input attributes. We also investigated the possibility of exploiting the prediction of connected pairs of beta-sheet residues and the prediction of residue contact maps for the improvement of accuracy of secondary-structure prediction.
    International Journal of Data Mining and Bioinformatics 02/2007; 1(3):286-313. DOI:10.1504/IJDMB.2007.011614 · 0.66 Impact Factor
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    ABSTRACT: The number of experimentally verified, intrinsically disordered (ID) proteins is rapidly rising. Research is often focused on a structural characterization of a given protein, looking for several key features. However, ID proteins with their dynamic structures that interconvert on a number of time-scales are difficult targets for the majority of traditional biophysical and biochemical techniques. Structural and functional analyses of these proteins can be significantly aided by disorder predictions. The current advances in the prediction of ID proteins and the use of protein disorder prediction in the fields of molecular biology and bioinformatics are briefly overviewed herein. A method is provided to utilize intrinsic disorder knowledge to gain structural and functional information related to individual proteins, protein groups, families, classes, and even entire proteomes.
    Methods in Molecular Biology 02/2007; 408:69-92. · 1.29 Impact Factor
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    ABSTRACT: The Database of Protein Disorder (DisProt) links structure and function information for intrinsically disordered proteins (IDPs). Intrinsically disordered proteins do not form a fixed three-dimensional structure under physiological conditions, either in their entireties or in segments or regions. We define IDP as a protein that contains at least one experimentally determined disordered region. Although lacking fixed structure, IDPs and regions carry out important biological functions, being typically involved in regulation, signaling and control. Such functions can involve high-specificity low-affinity interactions, the multiple binding of one protein to many partners and the multiple binding of many proteins to one partner. These three features are all enabled and enhanced by protein intrinsic disorder. One of the major hindrances in the study of IDPs has been the lack of organized information. DisProt was developed to enable IDP research by collecting and organizing knowledge regarding the experimental characterization and the functional associations of IDPs. In addition to being a unique source of biological information, DisProt opens doors for a plethora of bioinformatics studies. DisProt is openly available at http://www.disprot.org.
    Nucleic Acids Research 02/2007; 35(Database issue):D786-93. DOI:10.1093/nar/gkl893 · 8.81 Impact Factor
    This article is viewable in ResearchGate's enriched format
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    ABSTRACT: Composition Profiler is a web-based tool for semi-automatic discovery of enrichment or depletion of amino acids, either individually or grouped by their physico-chemical or structural properties. The program takes two samples of amino acids as input: a query sample and a reference sample. The latter provides a suitable background amino acid distribution, and should be chosen according to the nature of the query sample, for example, a standard protein database (e.g. SwissProt, PDB), a representative sample of proteins from the organism under study, or a group of proteins with a contrasting functional annotation. The results of the analysis of amino acid composition differences are summarized in textual and graphical form. As an exploratory data mining tool, our software can be used to guide feature selection for protein function or structure predictors. For classes of proteins with significant differences in frequencies of amino acids having particular physico-chemical (e.g. hydrophobicity or charge) or structural (e.g. alpha helix propensity) properties, Composition Profiler can be used as a rough, light-weight visual classifier.
    BMC Bioinformatics 02/2007; 8:211. DOI:10.1186/1471-2105-8-211 · 2.67 Impact Factor
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    ABSTRACT: About 10 years ago we published our first predictor of intrinsically disordered protein residues in another IEEE journal, the Proceedings of the IEEE International Conference on Neural Networks. Others call such proteins "natively unfolded" and "intrinsically unstructured." Since then, we and others have substantially improved the prediction of intrinsically disordered residues. The prediction of protein intrinsic disorder is similar to the prediction of secondary structure in terms of methodology, but, at the structural level, secondary structure (especially random coil) and intrinsic disorder differ completely in their dynamic motion. First, we will briefly describe the prediction of protein disorder, show the progress from ~ 70 % to ~ 85 % per residue prediction accuracy, and show that intrinsically disordered proteins are common over the three domains of life, but are especially common among the eukaryotes. Next we will discuss our methods for deducing functions that are associated with disordered rather than structured proteins. In brief, structured proteins have advantages for catalysis while disordered proteins and regions have advantages for the reversible, weak binding often observed in signaling, control, and regulation. After that we will discuss how disorder facilitates binding diversity in protein-protein interaction networks, both for single disordered regions binding to many partners and for many disordered regions with different sequences binding to a common site on the surface of one structured protein. Part three presents data indicating that alternative splicing is more prevalent in regions of RNA that code for disorder than those that code for structure, thus providing a means for evolving tissue-specific signaling networks. Finally, we will present a novel approach to drug discovery based on disordered protein.
    Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2007, October 14-17, 2007, Harvard Medical School, Boston, MA, 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
  • 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: Just over 10 years ago, in June, 1997, in the Proceedings of the IEEE International Conference on Neural Networks, we published our first predictor of intrinsically disordered protein. Since then, we have substantially improved our predictors, and more than 20 other laboratory groups have joined in efforts to improve the prediction of protein disorder. At the algorithmic level, prediction of protein intrinsic disorder is similar to the prediction of secondary structure, but, at the structural level, secondary structure and intrinsic disorder are entirely different. The secondary structure class called random coil or irregular differs from intrinsic disorder due to very different dynamic properties, with the secondary structure class being much less mobile than the region of disorder. At the biological level, unlike the prediction of secondary structure, the prediction of intrinsic disorder has been revolutionary. That is, for many years, experimentalists have provided evidence that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. Experimentalists further are showing that, for some proteins, functions depended on the unstructured rather than structured state. However, these examples have been mostly ignored. To our knowledge, not one disordered protein or disorder-associated function is discussed in any biochemistry textbook, even though such examples began to be discovered more than 50 years ago. Disorder prediction has been important for showing that the few experimentally characterized examples represent a very large cohort that is found all across all three domains of life. We now know that many significant biological functions depend directly on, or are importantly associated with, the unfolded or partially folded state. In this paper, we will briefly review some of the key discoveries that have occurred in the last decade, and, furthermore, will make a few highly speculative projections.
    Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2007, October 14-17, 2007, Harvard Medical School, Boston, MA, 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. DOI:10.1021/pr060171o · 5.00 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. DOI:10.1016/j.tibtech.2006.07.005 · 10.04 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. DOI:10.1016/j.jmb.2006.07.087 · 3.96 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. DOI:10.1021/bi060981d · 3.19 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. DOI:10.1021/pr0602388 · 5.00 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. DOI:10.1111/j.1365-2621.1996.tb14727.x · 1.78 Impact Factor

Publication Stats

18k Citations
921.76 Total Impact Points


  • 2003–2014
    • Indiana University-Purdue University Indianapolis
      • • Center for Computational Biology and Bioinformatics
      • • Department of Biochemistry and Molecular Biology
      Indianapolis, Indiana, United States
  • 2013
    • USF Health Byrd Alzheimer's Institute
      Tampa, Florida, United States
    • University of Bristol
      • Department of Computer Science
      Bristol, ENG, United Kingdom
    • University of Alberta
      • Department of Electrical and Computer Engineering
      Edmonton, Alberta, Canada
  • 2011–2013
    • University of South Florida
      • • Division of Molecular Medicine
      • • Department of Molecular Medicine
      Tampa, FL, United States
  • 1978–2012
    • Washington State University
      • • School of Mechanical and Materials Engineering
      • • Department of Biological Systems Engineering
      • • School of Molecular Biosciences
      • • School of Electrical Engineering and Computer Science
      • • Department of Chemistry
      Pullman, Washington, United States
  • 2010–2011
    • Plant and Food Research
      Окленд, Auckland, New Zealand
  • 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
    • Hungarian Academy of Sciences
      • Institute of Enzymology
      Budapest, Budapest fovaros, Hungary
  • 2001–2009
    • Temple University
      • Department of Computer and Information Science
      Philadelphia, Pennsylvania, United States
  • 2008
    • Universität Basel
      • Department of Biophysical Chemistry
      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
  • 2006
    • Indiana University-Purdue University School of Medicine
      Indianapolis, Indiana, United States
  • 2004
    • Amsterdamse Hogeschool voor de Kunsten
      Amsterdamo, North Holland, Netherlands
  • 1980
    • University of Oregon
      • Department of Chemistry
      Eugene, Oregon, United States