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Christopher M. Barbieri

Bio: Christopher M. Barbieri is an academic researcher from Rutgers University. The author has contributed to research in topics: Aminoglycoside & Replication protein A. The author has an hindex of 5, co-authored 5 publications receiving 138 citations.

Papers
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Journal ArticleDOI
TL;DR: This work has used a combination of biochemical and structural analysis to compare and contrast the molecular mechanisms of action and the structure–activity relationships of a new synthetic aminoglycoside, NB33, and a structurally similar natural aminglycoside apramycin, and demonstrates the general molecular principles that determine the decreased selectivity of apramYcin for the prokaryotic decoding site, and the increased selectivity for NB33.
Abstract: The lack of absolute prokaryotic selectivity of natural antibiotics is widespread and is a significant clinical problem. The use of this disadvantage of aminoglycoside antibiotics for the possible treatment of human genetic diseases is extremely challenging. Here, we have used a combination of biochemical and structural analysis to compare and contrast the molecular mechanisms of action and the structure-activity relationships of a new synthetic aminoglycoside, NB33, and a structurally similar natural aminoglycoside apramycin. The data presented herein demonstrate the general molecular principles that determine the decreased selectivity of apramycin for the prokaryotic decoding site, and the increased selectivity of NB33 for the eukaryotic decoding site. These results are therefore extremely beneficial for further research on both the design of new aminoglycoside-based antibiotics with diminished deleterious effects on humans, as well as the design of new aminoglycoside-based structures that selectively target the eukaryotic ribosome.

57 citations

Journal ArticleDOI
01 Feb 2015-Proteins
TL;DR: This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution, and uses sparse data to limit conformational search during optimization of a physically realistic energy function.
Abstract: Oligomeric proteins are important targets for structure determination in solution. While in most cases the fold of individual subunits can be determined experimentally, or predicted by homology-based methods, protein-protein interfaces are challenging to determine de novo using conventional NMR structure determination protocols. Here we focus on a member of the bet-V1 superfamily, Aha1 from Colwellia psychrerythraea. This family displays a broad range of crystallographic interfaces none of which can be reconciled with the NMR and SAXS data collected for Aha1. Unlike conventional methods relying on a dense network of experimental restraints, the sparse data are used to limit conformational search during optimization of a physically realistic energy function. This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution.

32 citations

01 Jan 2014
TL;DR: In this paper, the authors focus on a member of the bet-V1 superfamily, Aha1 from Colwellia psychrerythraea, and highlight a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution.
Abstract: Oligomeric proteins are important targets for structure determination in solution. While in most cases the fold of individual subunits can be determined experimentally, or predicted by homology-based methods, protein–protein interfaces are challenging to determine de novo using conventional NMR structure determination protocols. Here we focus on a member of the bet-V1 superfamily, Aha1 from Colwellia psychrerythraea. This family displays a broad range of crystallographic interfaces none of which can be reconciled with the NMR and SAXS data collected for Aha1. Unlike conventional methods relying on a dense network of experimental restraints, the sparse data are used to limit conformational search during optimization of a physically realistic energy function. This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution.

29 citations

Journal ArticleDOI
TL;DR: The molecular function of protein domain family domain of unknown function DUF2128 (PF09901) as a novel ss DNA binding domain is uncovered and this bacterial domain strongly associates into a dimer and presents a highly positively charged surface that is consistent with its function in non-specific ssDNA binding.
Abstract: Single-stranded DNA (ssDNA) binding proteins are important in basal metabolic pathways for gene transcription, recombination, DNA repair and replication in all domains of life. Their main cellular role is to stabilize melted duplex DNA and protect genomic DNA from degradation. We have uncovered the molecular function of protein domain family domain of unknown function DUF2128 (PF09901) as a novel ssDNA binding domain. This bacterial domain strongly associates into a dimer and presents a highly positively charged surface that is consistent with its function in non-specific ssDNA binding. Lactococcus lactis YdbC is a representative of DUF2128. The solution NMR structures of the 20 kDa apo-YdbC dimer and YdbC:dT19G1 complex were determined. The ssDNA-binding energetics to YdbC were characterized by isothermal titration calorimetry. YdbC shows comparable nanomolar affinities for pyrimidine and mixed oligonucleotides, and the affinity is sufficiently strong to disrupt duplex DNA. In addition, YdbC binds with lower affinity to ssRNA, making it a versatile nucleic acid-binding domain. The DUF2128 family is related to the eukaryotic nuclear protein positive cofactor 4 (PC4) family and to the PUR family both by fold similarity and molecular function.

12 citations


Cited by
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01 Jan 2010
TL;DR: It is found that women over 50 are more likely to have a family history of diabetes, especially if they are obese, than women under the age of 50.
Abstract: Hypertension 66 (20.3%) 24 (24.2%) 30 (16.3%) NS Diabetes 20 (6.2%) 7 (7.1%) 10 (5.4%) NS Excess weight 78 (24%) 27 (27.3%) 44 (23.9%) NS Smokers 64 (19.7%) 17 (17.2%) 35 (19.0%) NS Age >50 years 137 (42.2%) 54 (54.5%) 67 (36.4%) <0.02 Kidney disease 7 (2.2%) 1 (1%) 5 (2.7%) NS Family history, DM 102 (31.4%) 28 (28.3%) 66 (35.9%) NS

1,369 citations

Journal ArticleDOI
TL;DR: An overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions are provided.
Abstract: The traditional computational modeling of protein structure, dynamics, and interactions remains difficult for many protein systems. It is mostly due to the size of protein conformational spaces and required simulation time scales that are still too large to be studied in atomistic detail. Lowering the level of protein representation from all-atom to coarse-grained opens up new possibilities for studying protein systems. In this review we provide an overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions. A more detailed description is given for applications of coarse-grained models suitable for efficient combinations with all-atom simulations in multiscale modeling strategies.

711 citations

Journal ArticleDOI
Julia Koehler Leman1, Brian D. Weitzner2, Brian D. Weitzner3, Steven M. Lewis4, Steven M. Lewis5, Jared Adolf-Bryfogle6, Nawsad Alam7, Rebecca F. Alford2, Melanie L. Aprahamian8, David Baker3, Kyle A. Barlow9, Patrick Barth10, Patrick Barth11, Benjamin Basanta3, Brian J. Bender12, Kristin Blacklock13, Jaume Bonet11, Jaume Bonet14, Scott E. Boyken3, Phil Bradley15, Christopher Bystroff16, Patrick Conway3, Seth Cooper17, Bruno E. Correia11, Bruno E. Correia14, Brian Coventry3, Rhiju Das18, René M. de Jong19, Frank DiMaio3, Lorna Dsilva17, Roland L. Dunbrack20, Alex Ford3, Brandon Frenz3, Darwin Y. Fu12, Caleb Geniesse18, Lukasz Goldschmidt3, Ragul Gowthaman21, Jeffrey J. Gray2, Dominik Gront22, Sharon L. Guffy5, Scott Horowitz23, Po-Ssu Huang3, Thomas Huber24, Timothy M. Jacobs5, Jeliazko R. Jeliazkov2, David K. Johnson25, Kalli Kappel18, John Karanicolas20, Hamed Khakzad14, Hamed Khakzad26, Karen R. Khar25, Sagar D. Khare13, Firas Khatib27, Alisa Khramushin7, Indigo Chris King3, Robert Kleffner17, Brian Koepnick3, Tanja Kortemme9, Georg Kuenze12, Brian Kuhlman5, Daisuke Kuroda28, Jason W. Labonte29, Jason W. Labonte2, Jason K. Lai10, Gideon Lapidoth30, Andrew Leaver-Fay5, Steffen Lindert8, Thomas W. Linsky3, Nir London7, Joseph H. Lubin2, Sergey Lyskov2, Jack Maguire5, Lars Malmström31, Lars Malmström26, Lars Malmström14, Enrique Marcos3, Orly Marcu7, Nicholas A. Marze2, Jens Meiler12, Rocco Moretti12, Vikram Khipple Mulligan3, Santrupti Nerli32, Christoffer Norn30, Shane O’Conchúir9, Noah Ollikainen9, Sergey Ovchinnikov3, Michael S. Pacella2, Xingjie Pan9, Hahnbeom Park3, Ryan E. Pavlovicz3, Manasi A. Pethe13, Brian G. Pierce21, Kala Bharath Pilla24, Barak Raveh7, P. Douglas Renfrew, Shourya S. Roy Burman2, Aliza B. Rubenstein13, Marion F. Sauer12, Andreas Scheck14, Andreas Scheck11, William R. Schief6, Ora Schueler-Furman7, Yuval Sedan7, Alexander M. Sevy12, Nikolaos G. Sgourakis32, Lei Shi3, Justin B. Siegel33, Daniel-Adriano Silva3, Shannon Smith12, Yifan Song3, Amelie Stein9, Maria Szegedy13, Frank D. Teets5, Summer B. Thyme3, Ray Yu-Ruei Wang3, Andrew M. Watkins18, Lior Zimmerman7, Richard Bonneau1 
TL;DR: This Perspective reviews tools developed over the past five years in the Rosetta software, including over 80 methods, and discusses improvements to the score function, user interfaces and usability.
Abstract: The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

430 citations

Journal ArticleDOI
TL;DR: Genetic studies of mutants affected in pathways involved in sn-glycerol-3-phosphate metabolism have led to the identification of two additional multidrug tolerance loci, glpABC, the anaerobic sn- Glycerol 3-Phosphate dehydrogenase, and plsB, an sn- glycerol -3- phosphate acyltransferase.
Abstract: Bacterial populations produce dormant persister cells that are resistant to killing by all antibiotics currently in use, a phenomenon known as multidrug tolerance (MDT). Persisters are phenotypic variants of the wild type and are largely responsible for MDT of biofilms and stationary populations. We recently showed that a hipBA toxin/antitoxin locus is part of the MDT mechanism in Escherichia coli. In an effort to find additional MDT genes, an E. coli expression library was selected for increased survival to ampicillin. A clone with increased persister production was isolated and was found to overexpress the gene for the conserved aerobic sn-glycerol-3-phosphate dehydrogenase GlpD. The GlpD overexpression strain showed increased tolerance to ampicillin and ofloxacin, while a strain with glpD deleted had a decreased level of persisters in the stationary state. This suggests that GlpD is a component of the MDT mechanism. Further genetic studies of mutants affected in pathways involved in sn-glycerol-3-phosphate metabolism have led to the identification of two additional multidrug tolerance loci, glpABC, the anaerobic sn-glycerol-3-phosphate dehydrogenase, and plsB, an sn-glycerol-3-phosphate acyltransferase.

205 citations

Journal ArticleDOI
TL;DR: This work describes here the first systematic development of the novel aminoglycoside 2 (NB54) exhibiting superior in vitro readthrough efficiency to that of gentamicin in seven different DNA fragments derived from mutant genes carrying nonsense mutations representing the genetic diseases Usher syndrome, cystic fibrosis, Duchenne muscular dystrophy, and Hurler syndrome.
Abstract: Nonsense mutations promote premature translational termination and represent the underlying cause of a large number of human genetic diseases. The aminoglycoside antibiotic gentamicin has the ability to allow the mammalian ribosome to read past a false-stop signal and generate full-length functional proteins. However, severe toxic side effects along with the reduced suppression efficiency at subtoxic doses limit the use of gentamicin for suppression therapy. We describe here the first systematic development of the novel aminoglycoside 2 (NB54) exhibiting superior in vitro readthrough efficiency to that of gentamicin in seven different DNA fragments derived from mutant genes carrying nonsense mutations representing the genetic diseases Usher syndrome, cystic fibrosis, Duchenne muscular dystrophy, and Hurler syndrome. Comparative acute lethal toxicity in mice, cell toxicity, and the assessment of hair cell toxicity in cochlear explants further indicated that 2 exhibits far lower toxicity than that of gentamicin.

188 citations