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  • 1
    Publication Date: 2019
    Description: Abstract Arctic phytoplankton and their response to future conditions shape one of the most rapidly changing ecosystems on the planet. We tested how much the phenotypic responses of strains from an Arctic diatom population diverge and whether the physiology and intraspecific composition of multi‐strain populations differ from expectations based on single strain traits. To this end, we conducted incubation experiments with the diatom Thalassiosira hyalina under present‐day and future temperature and pCO2 treatments. Six fresh isolates from the same Svalbard population were incubated as mono‐ and multi‐strain cultures. For the first time, we were able to closely follow intraspecific selection within an artificial population using microsatellites and allele‐specific quantitative PCR. Our results show not only that there is substantial variation in how strains of the same species cope with the tested environments, but also that changes in genotype composition, production rates and cellular quotas in the multi‐strain cultures are not predictable from monoculture performance. Nevertheless, the physiological responses as well as strain composition of the artificial populations were highly reproducible within each environment. Interestingly, we only detected significant strain sorting in those populations exposed to the future treatment. This study illustrates that the genetic composition of populations can change on very short timescales through selection from the intraspecific standing stock, indicating the potential for rapid population level adaptation to climate change. We further show that individuals adjust their phenotype not only in response to their physico‐chemical, but also to their biological surroundings. Such intraspecific interactions need to be understood in order to realistically predict ecosystem responses to global change. This article is protected by copyright. All rights reserved.
    Print ISSN: 1354-1013
    Electronic ISSN: 1365-2486
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Published by Wiley
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  • 2
    Publication Date: 2015-01-31
    Description: Translocator proteins (TSPOs) bind steroids and porphyrins, and they are implicated in many human diseases, for which they serve as biomarkers and therapeutic targets. TSPOs have tryptophan-rich sequences that are highly conserved from bacteria to mammals. Here we report crystal structures for Bacillus cereus TSPO (BcTSPO) down to 1.7 A resolution, including a complex with the benzodiazepine-like inhibitor PK11195. We also describe BcTSPO-mediated protoporphyrin IX (PpIX) reactions, including catalytic degradation to a previously undescribed heme derivative. We used structure-inspired mutations to investigate reaction mechanisms, and we showed that TSPOs from Xenopus and man have similar PpIX-directed activities. Although TSPOs have been regarded as transporters, the catalytic activity in PpIX degradation suggests physiological importance for TSPOs in protection against oxidative stress.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341906/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341906/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Guo, Youzhong -- Kalathur, Ravi C -- Liu, Qun -- Kloss, Brian -- Bruni, Renato -- Ginter, Christopher -- Kloppmann, Edda -- Rost, Burkhard -- Hendrickson, Wayne A -- GM095315/GM/NIGMS NIH HHS/ -- GM107462/GM/NIGMS NIH HHS/ -- R01 GM107462/GM/NIGMS NIH HHS/ -- U54 GM075026/GM/NIGMS NIH HHS/ -- New York, N.Y. -- Science. 2015 Jan 30;347(6221):551-5. doi: 10.1126/science.aaa1534.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. ; The New York Consortium on Membrane Protein Structure (NYCOMPS), New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA. ; The New York Consortium on Membrane Protein Structure (NYCOMPS), New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA. New York Structural Biology Center, Synchrotron Beamlines, Brookhaven National Laboratory, Upton, NY 11973, USA. ; The New York Consortium on Membrane Protein Structure (NYCOMPS), New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA. Department of Informatics, Bioinformatics and Computational Biology, Technische Universitat Munchen, Garching 85748, Germany. ; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. The New York Consortium on Membrane Protein Structure (NYCOMPS), New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA. New York Structural Biology Center, Synchrotron Beamlines, Brookhaven National Laboratory, Upton, NY 11973, USA. Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA. wayne@xtl.cumc.columbia.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25635100" target="_blank"〉PubMed〈/a〉
    Keywords: Amino Acid Sequence ; Bacillus cereus/*chemistry ; Bacterial Proteins/*chemistry/*metabolism ; Binding Sites ; Crystallography, X-Ray ; Isoquinolines/metabolism ; Ligands ; Membrane Transport Proteins/*chemistry/*metabolism ; Molecular Sequence Data ; Mutant Proteins/chemistry/metabolism ; Protein Conformation ; Protein Multimerization ; Protein Structure, Secondary ; Protein Subunits/chemistry ; Protoporphyrins/metabolism ; Reactive Oxygen Species/metabolism ; Tryptophan/analysis
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2016-02-26
    Description: Polymyxins are antibiotics used in the last line of defense to combat multidrug-resistant infections by Gram-negative bacteria. Polymyxin resistance arises through charge modification of the bacterial outer membrane with the attachment of the cationic sugar 4-amino-4-deoxy-l-arabinose to lipid A, a reaction catalyzed by the integral membrane lipid-to-lipid glycosyltransferase 4-amino-4-deoxy-L-arabinose transferase (ArnT). Here, we report crystal structures of ArnT from Cupriavidus metallidurans, alone and in complex with the lipid carrier undecaprenyl phosphate, at 2.8 and 3.2 angstrom resolution, respectively. The structures show cavities for both lipidic substrates, which converge at the active site. A structural rearrangement occurs on undecaprenyl phosphate binding, which stabilizes the active site and likely allows lipid A binding. Functional mutagenesis experiments based on these structures suggest a mechanistic model for ArnT family enzymes.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Petrou, Vasileios I -- Herrera, Carmen M -- Schultz, Kathryn M -- Clarke, Oliver B -- Vendome, Jeremie -- Tomasek, David -- Banerjee, Surajit -- Rajashankar, Kanagalaghatta R -- Belcher Dufrisne, Meagan -- Kloss, Brian -- Kloppmann, Edda -- Rost, Burkhard -- Klug, Candice S -- Trent, M Stephen -- Shapiro, Lawrence -- Mancia, Filippo -- AI064184/AI/NIAID NIH HHS/ -- AI076322/AI/NIAID NIH HHS/ -- P41 GM103403/GM/NIGMS NIH HHS/ -- R01 GM111980/GM/NIGMS NIH HHS/ -- S10 RR029205/RR/NCRR NIH HHS/ -- U54 GM095315/GM/NIGMS NIH HHS/ -- New York, N.Y. -- Science. 2016 Feb 5;351(6273):608-12. doi: 10.1126/science.aad1172.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA. ; Department of Infectious Diseases, University of Georgia, College of Veterinary Medicine, Athens, GA 30602, USA. ; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA. ; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. ; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. Department of Systems Biology, Columbia University, New York, NY 10032, USA. ; Department of Chemistry and Chemical Biology, Cornell University, Northeastern Collaborative Access Team, Advanced Photon Source, Argonne, IL 60439, USA. ; New York Consortium on Membrane Protein Structure, New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA. ; Department of Informatics, Bioinformatics and Computational Biology, Technische Universitat Munchen, Boltzmannstrasse 3, 85748 Garching, Germany. ; Department of Informatics, Bioinformatics and Computational Biology, Technische Universitat Munchen, Boltzmannstrasse 3, 85748 Garching, Germany. Institute for Advanced Study (TUM-IAS), Technische Universitat Munchen, Boltzmannstrasse 3, 85748 Garching, Germany. ; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA. fm123@cumc.columbia.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26912703" target="_blank"〉PubMed〈/a〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 4
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    Oxford University Press
    Publication Date: 2015-05-12
    Description: Motivation: Machine learning may be the most popular computational tool in molecular biology. Providing sustained performance estimates is challenging. The standard cross-validation protocols usually fail in biology. Park and Marcotte found that even refined protocols fail for protein–protein interactions (PPIs). Results: Here, we sketch additional problems for the prediction of PPIs from sequence alone. First, it not only matters whether proteins A or B of a target interaction A–B are similar to proteins of training interactions (positives), but also whether A or B are similar to proteins of non-interactions (negatives). Second, training on multiple interaction partners per protein did not improve performance for new proteins (not used to train). In contrary, a strictly non-redundant training that ignored good data slightly improved the prediction of difficult cases. Third, which prediction method appears to be best crucially depends on the sequence similarity between the test and the training set, how many true interactions should be found and the expected ratio of negatives to positives. The correct assessment of performance is the most complicated task in the development of prediction methods. Our analyses suggest that PPIs square the challenge for this task. Availability and implementation: Datasets used in our analyses are available at https://rostlab.org/owiki/index.php/PPI_challenges Contact: rost@in.tum.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 5
    Publication Date: 2016-01-07
    Description: Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools .
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 6
    Publication Date: 2015-06-14
    Description: Motivation: Many methods predict the physical interaction between two proteins (protein-protein interactions; PPIs) from sequence alone. Their performance drops substantially for proteins not used for training. Results: Here, we introduce a new approach to predict PPIs from sequence alone which is based on evolutionary profiles and profile-kernel support vector machines. It improved over the state-of-the-art, in particular for proteins that are sequence-dissimilar to proteins with known interaction partners. Filtering by gene expression data increased accuracy further for the few, most reliably predicted interactions (low recall). The overall improvement was so substantial that we compiled a list of the most reliably predicted PPIs in human. Our method makes a significant difference for biology because it improves most for the majority of proteins without experimental annotations. Availability and implementation: Implementation and most reliably predicted human PPIs available at https://rostlab.org/owiki/index.php/Profppikernel . Contact: rost@in.tum.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 7
    Publication Date: 2015-02-13
    Description: Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains and three-dimensional protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology 2016, Orlando, FL). Contact: dkovats@iscb.org or rost@in.tum.de
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 8
    Publication Date: 2016-10-12
    Description: The tight spatial coupling of synaptic vesicles and voltage-gated Ca2+ channels (CaVs) ensures efficient action potential-triggered neurotransmitter release from presynaptic active zones (AZs). Rab-interacting molecule-binding proteins (RIM-BPs) interact with Ca2+ channels and via RIM with other components of the release machinery. Although human RIM-BPs have been implicated in autism spectrum...
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 9
    Publication Date: 2015-12-30
    Description: We surveyed the “dark” proteome–that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44–54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark...
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 10
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