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Thayer KM, Galganov JC, Stein AJ. Dependence of prevalence of contiguous pathways in proteins on structural complexity. PLoS One 2017; 12:e0188616. [PMID: 29232711 PMCID: PMC5726733 DOI: 10.1371/journal.pone.0188616] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 11/10/2017] [Indexed: 12/15/2022] Open
Abstract
Allostery is a regulatory mechanism in proteins where an effector molecule binds distal from an active site to modulate its activity. Allosteric signaling may occur via a continuous path of residues linking the active and allosteric sites, which has been suggested by large conformational changes evident in crystal structures. An alternate possibility is that the signal occurs in the realm of ensemble dynamics via an energy landscape change. While the latter was first proposed on theoretical grounds, increasing evidence suggests that such a control mechanism is plausible. A major difficulty for testing the two methods is the ability to definitively determine that a residue is directly involved in allosteric signal transduction. Statistical Coupling Analysis (SCA) is a method that has been successful at predicting pathways, and experimental tests involving mutagenesis or domain substitution provide the best available evidence of signaling pathways. However, ascertaining energetic pathways which need not be contiguous is far more difficult. To date, simple estimates of the statistical significance of a pathway in a protein remain to be established. The focus of this work is to estimate such benchmarks for the statistical significance of contiguous pathways for the null model of selecting residues at random. We found that when 20% of residues in proteins are randomly selected, contiguous pathways at the 6 Å cutoff level were found with success rates of 51% in PDZ, 30% in p53, and 3% in MutS. The results suggest that the significance of pathways may have system specific factors involved. Furthermore, the possible existence of false positives for contiguous pathways implies that signaling could be occurring via alternate routes including those consistent with the energetic landscape model.
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Affiliation(s)
- Kelly M. Thayer
- Department of Mathematics and Computer Science, Wesleyan University, Middletown, CT, United States of America
- Program in Molecular Biophysics, Wesleyan University, Middletown, CT, United States of America
- Department of Chemistry, Wesleyan University, Middletown, CT, United States of America
- * E-mail:
| | - Jesse C. Galganov
- Department of Mathematics and Computer Science, Wesleyan University, Middletown, CT, United States of America
- Program in Bioinformatics, Wesleyan University, Middletown, CT, United States of America
| | - Avram J. Stein
- Department of Astronomy, Wesleyan University, Middletown, CT, United States of America
- Department of Earth and Environmental Sciences, Wesleyan University, Middletown, CT, United States of America
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Stetz G, Tse A, Verkhivker GM. Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone. PLoS One 2017; 12:e0186089. [PMID: 29095844 PMCID: PMC5667858 DOI: 10.1371/journal.pone.0186089] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 09/25/2017] [Indexed: 12/24/2022] Open
Abstract
The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies.
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Affiliation(s)
- Gabrielle Stetz
- Department of Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America
| | - Amanda Tse
- Department of Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America
| | - Gennady M. Verkhivker
- Department of Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California, United States of America
- * E-mail:
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53
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Wenzel SE, Tyurina YY, Zhao J, St Croix CM, Dar HH, Mao G, Tyurin VA, Anthonymuthu TS, Kapralov AA, Amoscato AA, Mikulska-Ruminska K, Shrivastava IH, Kenny EM, Yang Q, Rosenbaum JC, Sparvero LJ, Emlet DR, Wen X, Minami Y, Qu F, Watkins SC, Holman TR, VanDemark AP, Kellum JA, Bahar I, Bayır H, Kagan VE. PEBP1 Wardens Ferroptosis by Enabling Lipoxygenase Generation of Lipid Death Signals. Cell 2017; 171:628-641.e26. [PMID: 29053969 PMCID: PMC5683852 DOI: 10.1016/j.cell.2017.09.044] [Citation(s) in RCA: 585] [Impact Index Per Article: 83.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 07/05/2017] [Accepted: 09/25/2017] [Indexed: 02/07/2023]
Abstract
Ferroptosis is a form of programmed cell death that is pathogenic to several acute and chronic diseases and executed via oxygenation of polyunsaturated phosphatidylethanolamines (PE) by 15-lipoxygenases (15-LO) that normally use free polyunsaturated fatty acids as substrates. Mechanisms of the altered 15-LO substrate specificity are enigmatic. We sought a common ferroptosis regulator for 15LO. We discovered that PEBP1, a scaffold protein inhibitor of protein kinase cascades, complexes with two 15LO isoforms, 15LO1 and 15LO2, and changes their substrate competence to generate hydroperoxy-PE. Inadequate reduction of hydroperoxy-PE due to insufficiency or dysfunction of a selenoperoxidase, GPX4, leads to ferroptosis. We demonstrated the importance of PEBP1-dependent regulatory mechanisms of ferroptotic death in airway epithelial cells in asthma, kidney epithelial cells in renal failure, and cortical and hippocampal neurons in brain trauma. As master regulators of ferroptotic cell death with profound implications for human disease, PEBP1/15LO complexes represent a new target for drug discovery.
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Affiliation(s)
- Sally E Wenzel
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Yulia Y Tyurina
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jinming Zhao
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Haider H Dar
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gaowei Mao
- Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vladimir A Tyurin
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tamil S Anthonymuthu
- Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alexandr A Kapralov
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew A Amoscato
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Karolina Mikulska-Ruminska
- Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, USA; Institute of Physics, Nicolaus Copernicus University, Torun, Poland
| | - Indira H Shrivastava
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA; Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth M Kenny
- Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Qin Yang
- Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joel C Rosenbaum
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Louis J Sparvero
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - David R Emlet
- Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xiaoyan Wen
- Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yoshinori Minami
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Feng Qu
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Simon C Watkins
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Theodore R Holman
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA, USA
| | - Andrew P VanDemark
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - John A Kellum
- Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ivet Bahar
- Department of Computational and System Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hülya Bayır
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA; Department of Critical Care Medicine, Center for Critical Care Nephrology, Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, PA, USA; Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.
| | - Valerian E Kagan
- Department of Environmental and Occupational Health, Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA; Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA; Department of Radiation Oncology, University of Pittsburgh, Pittsburgh, PA, USA.
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54
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Gaussian network model can be enhanced by combining solvent accessibility in proteins. Sci Rep 2017; 7:7486. [PMID: 28790346 PMCID: PMC5548781 DOI: 10.1038/s41598-017-07677-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 06/29/2017] [Indexed: 01/03/2023] Open
Abstract
Gaussian network model (GNM), regarded as the simplest and most representative coarse-grained model, has been widely adopted to analyze and reveal protein dynamics and functions. Designing a variation of the classical GNM, by defining a new Kirchhoff matrix, is the way to improve the residue flexibility modeling. We combined information arising from local relative solvent accessibility (RSA) between two residues into the Kirchhoff matrix of the parameter-free GNM. The undetermined parameters in the new Kirchhoff matrix were estimated by using particle swarm optimization. The usage of RSA was motivated by the fact that our previous work using RSA based linear regression model resulted out higher prediction quality of the residue flexibility when compared with the classical GNM and the parameter free GNM. Computational experiments, conducted based on one training dataset, two independent datasets and one additional small set derived by molecular dynamics simulations, demonstrated that the average correlation coefficients of the proposed RSA based parameter-free GNM, called RpfGNM, were significantly increased when compared with the parameter-free GNM. Our empirical results indicated that a variation of the classical GNMs by combining other protein structural properties is an attractive way to improve the quality of flexibility modeling.
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Li H, Sharma N, General IJ, Schreiber G, Bahar I. Dynamic Modulation of Binding Affinity as a Mechanism for Regulating Interferon Signaling. J Mol Biol 2017; 429:2571-2589. [PMID: 28648616 PMCID: PMC5545807 DOI: 10.1016/j.jmb.2017.06.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 06/15/2017] [Accepted: 06/16/2017] [Indexed: 12/22/2022]
Abstract
How structural dynamics affects cytokine signaling is under debate. Here, we investigated the dynamics of the type I interferon (IFN) receptor, IFNAR1, and its effect on signaling upon binding IFN and IFNAR2 using a combination of structure-based mechanistic studies, in situ binding, and gene induction assays. Our study reveals that IFNAR1 flexibility modulates ligand-binding affinity, which, in turn, regulates biological signaling. We identified the hinge sites and key interactions implicated in IFNAR1 inter-subdomain (SD1-SD4) movements. We showed that the predicted cooperative movements are essential to accommodate intermolecular interactions. Engineered disulfide bridges, computationally predicted to interfere with IFNAR1 dynamics, were experimentally confirmed. Notably, introducing disulfide bonds between subdomains SD2 and SD3 modulated IFN binding and activity in accordance with the relative attenuation of cooperative movements with varying distance from the hinge center, whereas locking the SD3-SD4 interface flexibility in favor of an extended conformer increased activity.
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Affiliation(s)
- Hongchun Li
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Nanaocha Sharma
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ignacio J General
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; School of Science and Technology, and CONICET, Universidad Nacional de San Martin, San Martin, Buenos Aires 1650, Argentina
| | - Gideon Schreiber
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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56
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Sauerwald N, Zhang S, Kingsford C, Bahar I. Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings. Nucleic Acids Res 2017; 45:3663-3673. [PMID: 28334818 PMCID: PMC5397156 DOI: 10.1093/nar/gkx172] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 01/16/2017] [Accepted: 03/06/2017] [Indexed: 12/11/2022] Open
Abstract
Understanding the three-dimensional (3D) architecture of chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to study 3D genome organization, but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (>50 Mb) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to model chromatin dynamics using Hi-C data. We show that the GNM can identify spatial couplings at multiple scales: it can quantify the correlated fluctuations in the positions of gene loci, find large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with genome-wide experimental measurements. We use the GNM to identify novel cross-correlated distal domains (CCDDs) representing pairs of regions distinguished by their long-range dynamic coupling and show that CCDDs are associated with increased gene co-expression. Together, these results show that GNM provides a mathematically well-founded unified framework for modeling chromatin dynamics and assessing the structural basis of genome-wide observations.
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Affiliation(s)
- Natalie Sauerwald
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - She Zhang
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Carl Kingsford
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
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57
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Simplified Swarm Optimization-Based Function Module Detection in Protein–Protein Interaction Networks. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7040412] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Intrinsic Dynamics Analysis of a DNA Octahedron by Elastic Network Model. Molecules 2017; 22:molecules22010145. [PMID: 28275219 PMCID: PMC6155889 DOI: 10.3390/molecules22010145] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 01/10/2023] Open
Abstract
DNA is a fundamental component of living systems where it plays a crucial role at both functional and structural level. The programmable properties of DNA make it an interesting building block for the construction of nanostructures. However, molecular mechanisms for the arrangement of these well-defined DNA assemblies are not fully understood. In this paper, the intrinsic dynamics of a DNA octahedron has been investigated by using two types of Elastic Network Models (ENMs). The application of ENMs to DNA nanocages include the analysis of the intrinsic flexibilities of DNA double-helices and hinge sites through the calculation of the square fluctuations, as well as the intrinsic collective dynamics in terms of cross-collective map calculation coupled with global motions analysis. The dynamics profiles derived from ENMs have then been evaluated and compared with previous classical molecular dynamics simulation trajectories. The results presented here revealed that ENMs can provide useful insights into the intrinsic dynamics of large DNA nanocages and represent a useful tool in the field of structural DNA nanotechnology.
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Chandrasekaran A, Chan J, Lim C, Yang LW. Protein Dynamics and Contact Topology Reveal Protein–DNA Binding Orientation. J Chem Theory Comput 2016; 12:5269-5277. [DOI: 10.1021/acs.jctc.6b00688] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
| | | | | | - Lee-Wei Yang
- Physics
Division, National Center for Theoretical Sciences, Hsinchu 30013, Taiwan
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Rigden DJ, Fernández-Suárez XM, Galperin MY. The 2016 database issue of Nucleic Acids Research and an updated molecular biology database collection. Nucleic Acids Res 2016; 44:D1-6. [PMID: 26740669 PMCID: PMC4702933 DOI: 10.1093/nar/gkv1356] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2015] [Accepted: 11/23/2015] [Indexed: 01/21/2023] Open
Abstract
The 2016 Database Issue of Nucleic Acids Research starts with overviews of the resources provided by three major bioinformatics centers, the U.S. National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute (EMBL-EBI) and Swiss Institute for Bioinformatics (SIB). Also included are descriptions of 62 new databases and updates on 95 databases that have been previously featured in NAR plus 17 previously described elsewhere. A number of papers in this issue deal with resources on nucleic acids, including various kinds of non-coding RNAs and their interactions, molecular dynamics simulations of nucleic acid structure, and two databases of super-enhancers. The protein database section features important updates on the EBI's Pfam, PDBe and PRIDE databases, as well as a variety of resources on pathways, metabolomics and metabolic modeling. This issue also includes updates on popular metagenomics resources, such as MG-RAST, EBI Metagenomics, and probeBASE, as well as a newly compiled Human Pan-Microbe Communities database. A significant fraction of the new and updated databases are dedicated to the genetic basis of disease, primarily cancer, and various aspects of drug research, including resources for patented drugs, their side effects, withdrawn drugs, and potential drug targets. A further six papers present updated databases of various antimicrobial and anticancer peptides. The entire Database Issue is freely available online on the Nucleic Acids Research website (http://nar.oxfordjournals.org/). The NAR online Molecular Biology Database Collection, http://www.oxfordjournals.org/nar/database/c/, has been updated with the addition of 88 new resources and removal of 23 obsolete websites, which brought the current listing to 1685 databases.
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Affiliation(s)
- Daniel J Rigden
- Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | | | - Michael Y Galperin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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